Explorez tous les épisodes du podcast Experiencing Data w/ Brian T. O’Neill (UX for AI Products, Analytics SAAS and Data Product Management)
Plongez dans la liste complète des épisodes de Experiencing Data w/ Brian T. O’Neill (UX for AI Products, Analytics SAAS and Data Product Management). Chaque épisode est catalogué accompagné de descriptions détaillées, ce qui facilite la recherche et l'exploration de sujets spécifiques. Suivez tous les épisodes de votre podcast préféré et ne manquez aucun contenu pertinent.
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Titre
Date
Durée
161 - Designing and Selling Enterprise AI Products [Worth Paying For]
21 Jan 2025
00:34:00
With GenAI and LLMs comes great potential to delight and damage customer relationships—both during the sale, and in the UI/UX. However, are B2B AI product teams actually producing real outcomes, on the business side and the UX side, such that customers find these products easy to buy, trustworthy and indispensable?
What is changing with customer problems as a result of LLM and GenAI technologies becoming more readily available to implement into B2B software? Anything?
Is your current product or feature development being driven by the fact you might be able to now solve it with AI? The “AI-first” team sounds like it’s cutting edge, but is that really determining what a customer will actually buy from you?
Today I want to talk to you about the interplay of GenAI, customer trust (both user and buyer trust), and the role of UX in products using probabilistic technology.
These thoughts are based on my own perceptions as a “user” of AI “solutions,” (quotes intentional!), conversations with prospects and clients at my company (Designing for Analytics), as well as the bright minds I mentor over at the MIT Sandbox innovation fund. I also wrote an article about this subject if you’d rather read an abridged version of my thoughts.
Highlights/ Skip to:
AI and LLM-Powered Products Do Not Turn Customer Problems into “Now” and “Expensive” Problems (4:03)
Trust and Transparency in the Sale and the Product UX: Handling LLM Hallucinations (Confabulations) and Designing for Model Interpretability (9:44)
Selling AI Products to Customers Who Aren’t Users (13:28)
How LLM Hallucinations and Model Interpretability Impact User Trust of Your Product (16:10)
Probabilistic UIs and LLMs Don’t Negate the Need to Design for Outcomes (22:48)
How AI Changes (or Doesn’t) Our Benchmark Use Cases and UX Outcomes (28:41)
Closing Thoughts (32:36)
Quotes from Today’s Episode
“Putting AI or GenAI into a product does not change the urgency or the depth of a particular customer problem; it just changes the solution space. Technology shifts in the last ten years have enabled founders to come up with all sorts of novel ways to leverage traditional machine learning, symbolic AI, and LLMs to create new products and disrupt established products; however, it would be foolish to ignore these developments as a product leader. All this technology does is change the possible solutions you can create. It does not change your customer situation, problem, or pain, either in the depth, or severity, or frequency. In fact, it might actually cause some new problems. I feel like most teams spend a lot more time living in the solution space than they do in the problem space. Fall in love with the problem and love that problem regardless of how the solution space may continue to change.” (4:51)
“Narrowly targeted, specialized AI products are going to beat solutions trying to solve problems for multiple buyers and customers. If you’re building a narrow, specific product for a narrow, specific audience, one of the things you have on your side is a solution focused on a specific domain used by people who have specific domain experience. You may not need a trillion-parameter LLM to provide significant value to your customer. AI products that have a more specific focus and address a very narrow ICP I believe are more likely to succeed than those trying to serve too many use cases—especially when GenAI is being leveraged to deliver the value. I think this can be true even for platform products as well. Narrowing the audience you want to serve also narrows the scope of the product, which in turn should increase the value that you bring to that audience—in part because you probably will have fewer trust, usability, and utility problems resulting from trying to leverage a model for a wide range of use cases.” (17:18)
“Probabilistic UIs and LLMs are going to create big problems for product teams, particularly if they lack a set of guiding benchmark use cases. I talk a lot about benchmark use cases as a core design principle and data-rich enterprise products. Why? Because a lot of B2B and enterprise products fall into the game of ‘adding more stuff over time.’ ‘Add it so you can sell it.’ As products and software companies begin to mature, you start having product owners and PMs attached to specific technologies or parts of a product. Figuring out how to improve the customer’s experience over time against the most critical problems and needs they have is a harder game to play than simply adding more stuff— especially if you have no benchmark use cases to hold you accountable. It’s hard to make the product indispensable if it’s trying to do 100 things for 100 people.“ (22:48)
“Product is a hard game, and design and UX is by far not the only aspect of product that we need to get right. A lot of designers don’t understand this, and they think if they just nail design and UX, then everything else solves itself. The reason the design and experience part is hard is that it’s tied to behavior change– especially if you are ‘disrupting’ an industry, incumbent tool, application, or product. You are in the behavior-change game, and it’s really hard to get it right. But when you get it right, it can be really amazing and transformative.” (28:01)
“If your AI product is trying to do a wide variety of things for a wide variety of personas, it’s going to be harder to determine appropriate benchmarks and UX outcomes to measure and design against. Given LLM hallucinations, the increased problem of trust, model drift problems, etc., your AI product has to actually innovate in a way that is both meaningful and observable to the customer. It doesn’t matter what your AI is trying to “fix.” If they can’t see what the benefit is to them personally, it doesn’t really matter if technically you’ve done something in a new and novel way. They’re just not going to care because that question of what’s in it for me is always sitting behind, in their brain, whether it’s stated out loud or not.” (29:32)
160 - Leading Product Through a Merger/Acquisition: Lessons from The Predictive Index’s CPO Adam Berke
07 Jan 2025
00:42:10
Today, I’m chatting with Adam Berke, the Chief Product Officer at The Predictive Index. For 70 years, The Predictive Index has helped customers hire the right employees, and after the merger with Charma, their products now nurture the employee/manager relationship. This is something right up Adam’s alley, as he previously helped co-found the employee and workflow performance management software company Charma before both aforementioned organizations merged back in 2023.
You’ll hear Adam talk about the first-time challenges (and successes) that come with integrating two products and two product teams, and why squashing out any ambiguity with overindexing (i.e. coming prepared with new org charts ASAP) is essential during the process.
Integrating behavioral science into the world of data is what has allowed The Predictive Index to thrive since the 1950s. While this is the company’s main selling point, Adam explains how the science-forward approach can still create some disagreements–and learning opportunities–with The Predictive Index’s legacy customers.
Highlights/ Skip to:
What is The Predictive Index and how does the product team conduct their work (1:24)
Why Charma merged with The Predictive Index (5:11)
The challenges Adam has faced as a CPO since the Charma/Predictive Index merger (9:21)
How Predictive Index has utilized behavioral science to remove the guesswork of hiring (14:22)
The makeup of the product team that designs and delivers The Predictive Index's products (20:24)
Navigating the clashes between changing science and Predictive Index's legacy customers (22:37)
How The Predictive Index analyzes the quality of their products with multiple user data metrics (27:21)
What Adam would do differently if had to redo the merger (37:52)
Where you can find more from Adam and The Predictive Index (41:22)
Quotes from Today’s Episode
“ Acquisitions are complicated. Outside of a few select companies, there are very few that have mergers and acquisitions as a repeatable discipline. More often than not, neither [company in the merger] has an established playbook for how to do this. You’re [acquiring a company] because of its product, team, or maybe even one feature. You have different theories on how the integration might look, but experiencing it firsthand is a whole different thing. My initial role didn’t exist in [The Predictive Index] before. The rest of the whole PI organization knows how to get their work done before this, and now there’s this new executive. There’s just tons of [questions and confusion] if you don’t go in assuming good faith and be willing to work through the bumps. It’s going to get messy.” - Adam Berke (9:41)
“We integrated the teams and relaunched the product. Charma became [a part of the product called] PI Perform, and right away there was re-skinning, redesign, and some back-end architecture that needed to happen to make it its own module. From a product perspective, we’re trying to deliver [Charma’s] unique value prop. That’s when we can start [figuring out how to] infuse PI’s behavioral science into these workflows. We have this foundation. We got the thing organized. We got the teams organized. We were 12 people when we were acquired… and here we are a year later. 150+ new customers have been added to PI Perform because it’s accelerating now that we’re figuring out the product.” - Adam Berke (12:18)
“Our product team has the roles that you would expect: a PM, researcher, ux design, and then one atypical role–a PhD behavioral scientist. [Our product already had] suggested topics and templates [for manager/IC one-on-one meetings], but now we want to make those templates and suggested topics more dynamic. There might be different questions to draw out a better discussion, and our behavioral scientists help us determine [those questions]... [Our behavioral scientists] look at the science, other research, and calibrate [the one-on-one questions] before we implement them into the product.” - Adam Berke (21:04)
“We’ve adapted the technology and science over time as they move forward. We want to update the product with the most recent science, but there are customers who have used this product in a certain way for decades in some cases. Our desire is to follow the science… but you can’t necessarily stop people from using the stuff in a way that they used it 20 years ago. We sometimes end up with disagreements [with customers over product changes based on scientific findings], and those are tricky conversations. But even in that debate… it comes down to all the best practices you would follow in product development in general–listening to your customers, asking that additional ‘why’ question, and trying to get to root causes.” - Adam Berke (23:36)
“ We’re doing an upgrade to our platform right now trying to figure out how to manage user permissions in the new version of the product. The way that we did it in the old version had a lot of problems associated… and we put out a survey. “Hey, do you use this to do X?’ We got hundreds of responses and found that half of them were not using it for the reason that we thought they were. At first, we thought thousands of people were going to have deep, deep sensitivities to tweaks in how this works, and now we realize that it might be half that, at best. A simple one-question survey asked about the right problem in the right way can help to avoid a lot of unnecessary thrashing on a product problem that might not have even existed in the first place.” - Adam Berke (35:22)
151 - Monetizing SAAS Analytics and The Challenges of Designing a Successful Embedded BI Product (Promoted Episode)
03 Sep 2024
00:49:57
Due to a technical glitch that ended up unpublishing this episode right after it originally was released, Episode 151 is a replay of my conversation with Zalak Trivdei from this past March . Please enjoy our chat if you missed it the first time around!
150 - How Specialized LLMs Can Help Enterprises Deliver Better GenAI User Experiences with Mark Ramsey
29 Aug 2024
00:52:22
“Last week was a great year in GenAI,” jokes Mark Ramsey—and it’s a great philosophy to have as LLM tools especially continue to evolve at such a rapid rate. This week, you’ll get to hear my fun and insightful chat with Mark from Ramsey International about the world of large language models (LLMs) and how we make useful UXs out of them in the enterprise.
Mark shared some fascinating insights about using a company’s website information (data) as a place to pilot a LLM project, avoiding privacy landmines, and how re-ranking of models leads to better LLM response accuracy. We also talked about the importance of real human testing to ensure LLM chatbots and AI tools truly delight users. From amusing anecdotes about the spinning beach ball on macOS to envisioning a future where AI-driven chat interfaces outshine traditional BI tools, this episode is packed with forward-looking ideas and a touch of humor.
Highlights/ Skip to:
(0:50) Why is the world of GenAI evolving so fast?
(4:20) How Mark thinks about UX in an LLM application
(8:11) How Mark defines “Specialized GenAI?”
(12:42) Mark’s consulting work with GenAI / LLMs these days
(17:29) How GenAI can help the healthcare industry
(30:23) Uncovering users’ true feelings about LLM applications
(35:02) Are UIs moving backwards as models progress forward?
(40:53) How will GenAI impact data and analytics teams?
(44:51) Will LLMs be able to consistently leverage RAG and produce proper SQL?
(51:04) Where can find more from Mark and Ramsey International
Quotes from Today’s Episode
“With [GenAI], we have a solution that we’ve built to try to help organizations, and build workflows. We have a workflow that we can run and ask the same question [to a variety of GenAI models] and see how similar the answers are. Depending on the complexity of the question, you can see a lot of variability between the models… [and] we can also run the same question against the different versions of the model and see how it’s improved. Folks want a human-like experience interacting with these models.. [and] if the model can start responding in just a few seconds, that gives you much more of a conversational type of experience.” - Mark Ramsey (2:38)
“[People] don’t understand when you interact [with GenAI tools] and it brings tokens back in that streaming fashion, you’re actually seeing inside the brain of the model. Every token it produces is then displayed on the screen, and it gives you that typewriter experience back in the day. If someone has to wait, and all you’re seeing is a logo spinning, from a UX experience standpoint… people feel like the model is much faster if it just starts to produce those results in that streaming fashion. I think in a design, it’s extremely important to take advantage of that [...] as opposed to waiting to the end and delivering the results some models support that, and other models don’t.”- Mark Ramsey (4:35)
"All of the data that’s on the website is public information. We’ve done work with several organizations on quickly taking the data that’s on their website, packaging it up into a vector database, and making that be the source for questions that their customers can ask. [Organizations] publish a lot of information on their websites, but people really struggle to get to it. We’ve seen a lot of interest in vectorizing website data, making it available, and having a chat interface for the customer. The customer can ask questions, and it will take them directly to the answer, and then they can use the website as the source information.” - Mark Ramsey (14:04)
“I’m not skeptical at all. I’ve changed much of my [AI chatbot searches] to Perplexity, and I think it’s doing a pretty fantastic job overall in terms of quality. It’s returning an answer with citations, so you have a sense of where it’s sourcing the information from. I think it’s important from a user experience perspective. This is a replacement for broken search, as I really don’t want to read all the web pages and PDFs you have that *might* be about my chiropractic care query to answer my actual [healthcare] question.” - Brian O’Neill (19:22)
“We’ve all had great experience with customer service, and we’ve all had situations where the customer service was quite poor, and we’re going to have that same thing as we begin to [release more] chatbots. We need to make sure we try to alleviate having those bad experiences, and have an exit. If someone is running into a situation where they’d rather talk to a live person, have that ability to route them to someone else. That’s why the robustness of the model is extremely important in the implementation… and right now, organizations like OpenAI and Anthropic are significantly better at that [human-like] experience.” - Mark Ramsey (23:46)
"There’s two aspects of these models: the training aspect and then using the model to answer questions. I recommend to organizations to always augment their content and don’t just use the training data. You’ll still get that human-like experience that’s built into the model, but you’ll eliminate the hallucinations. If you have a model that has been set up correctly, you shouldn’t have to ask questions in a funky way to get answers.” - Mark Ramsey (39:11)
“People need to understand GenAI is not a predictive algorithm. It is not able to run predictions, it struggles with some math, so that is not the focus for these models. What’s interesting is that you can use the model as a step to get you [the answers]. A lot of the models now support functions… when you ask a question about something that is in a database, it actually uses its knowledge about the schema of the database. It can build the query, run the query to get the data back, and then once it has the data, it can reformat the data into something that is a good response back." - Mark Ramsey (42:02)
149 - What the Data Says About Why So Many Data Science and AI Initiatives Are Still Failing to Produce Value with Evan Shellshear
06 Aug 2024
00:50:18
Guess what? Data science and AI initiatives are still failing here in 2024—despite widespread awareness. Is that news? Candidly, you’ll hear me share with Evan Shellshear—author of the new book Why Data Science Projects Fail: The Harsh Realities of Implementing AI and Analytics—about how much I actually didn’t want to talk about this story originally on my podcast—because it’s not news! However, what is news is what the data says behind Evan’s findings—and guess what? It’s not the technology.
In our chat, Evan shares why he wanted to leverage a human approach to understand the root cause of multiple organizations’ failures and how this approach highlighted the disconnect between data scientists and decision-makers. He explains the human factors at play, such as poor problem surfacing and organizational culture challenges—and how these human-centered design skills are rarely taught or offered to data scientists. The conversation delves into why these failures are more prevalent in data science compared to other fields, attributing it to the complexity and scale of data-related problems. We also discuss how analytically mature companies can mitigate these issues through strategic approaches and stakeholder buy-in. Join us as we delve into these critical insights for improving data science project outcomes.
Highlights/ Skip to:
(4:45) Why are data science projects still failing?
(9:17) Why is the disconnect between data scientists and decision-makers so pronounced relative to, say, engineering?
(13:08) Why are data scientists not getting enough training for real-world problems?
(16:18) What the data says about failure rates for mature data teams vs. immature data teams
(19:39) How to change people’s opinions so they value data more
(25:16) What happens at the stage where the beneficiaries of data don’t actually see the benefits?
(31:09) What are the skills needed to prevent a repeating pattern of creating data products that customers ignore??
(37:10) Where do more mature organizations find non-technical help to complement their data science and AI teams?
(41:44) Are executives and directors aware of the skills needed to level up their data science and AI teams?
Quotes from Today’s Episode
“People know this stuff. It’s not news anymore. And so, the reason why we needed this was really to dig in. And exactly like you did, like, keeping that list of articles is brilliant, and knowing what’s causing the failures and what’s leading to these issues still arising is really important. But at some point, we need to approach this in a scientific fashion, and we need to unpack this, and we need to really delve into the details beyond just the headlines and the articles themselves. And start collating and analyzing this to properly figure out what’s going wrong, and what do we need to do about it to fix it once and for all so you can stop your endless collection, and the AI Incident Database that now has over 3500 entries. It can hang its hat and say, ‘I’ve done my job. It’s time to move on. We’re not failing as we used to.’” - Evan Shellshear (3:01)
"What we did is we took a number of different studies, and we split companies into what we saw as being analytically mature—and this is a common, well-known thing; there are many maturity frameworks exist across data, across AI, across all different areas—and what we call analytically immature, so those companies that probably aren’t there yet. And what we wanted to draw a distinction is okay, we say 80% of projects fail, or whatever the exact number is, but for who? And for what stage and for what capability? And so, what we then went and did is we were able to take our data and look at which failures are common for analytically immature organizations, and which failures are common for analytically mature organizations, and then we’re able to understand, okay, in the market, how many organizations do we think are analytically mature versus analytically immature, and then we were able to take that 80% failure rate and establish it. For analytically mature companies, the failure rate is probably more like 40%. For analytically immature companies, it’s over 90%, right? And so, you’re exactly right: organizations can do something about it, and they can build capabilities in to mitigate this. So definitely, it can be reduced. Definitely, it can be brought down. You might say, 40% is still too high, but it proves that by bringing in these procedures, you’re completely correct, that it can be reduced.” - Evan Shellshear (14:28)
"What happens with the data science person, however, is typically they’re seen as a cost center—typically, not always; nowadays, that dialog is changing—and what they need to do is find partners across the other parts of the business. So, they’re going to go into the supply chain team, they’ll go into the merchandising team, they’ll go into the banking team, they’ll go into the other teams, and they’re going to find their supporters and winners there, and they’re going to probably build out from there. So, the first step would likely be, if you’re a big enough organization that you’re not having that strategy the executive level is to find your friends—and there will be some of the organization who support this data strategy—and get some wins for them.” - Evan Shellshear (24:38)
“It’s not like there’s this box you put one in the other in. Because, like success and failure, there’s a continuum. And companies as they move along that continuum, just like you said, this year, we failed on the lack of executive buy-in, so let’s fix that problem. Next year, we fail on not having the right resources, so we fix that problem. And you move along that continuum, and you build it up. And at some point as you’re going on, that failure rate is dropping, and you’re getting towards that end of the scale where you’ve got those really capable companies that live, eat, and breathe data science and analytics, and so have to have these to be able to survive, otherwise a simple company evolution would have wiped them out, and they wouldn’t exist if they didn’t have that capability, if that’s their core thing.” - Evan Shellshear (18:56)
“Nothing else could be correct, right? This subjective intuition and all this stuff, it’s never going to be as good as the data. And so, what happens is, is you, often as a data scientist—and I’ve been subjected to this myself—come in with this arrogance, this kind of data-driven arrogance, right? And it’s not a good thing. It puts up barriers, it creates issues, it separates you from the people.” - Evan Shellshear (27:38)
"Knowing that you’re going to have to go on that journey from day one, you can’t jump from level zero to level five. That’s what all these data maturity models are about, right? You can’t jump from level zero data maturity to level five overnight. You really need to take those steps and build it up.” - Evan Shellshear (45:21)
"What we’re talking about, it’s not new. It’s just old wine in a new skin, and we’re just presenting it for the data science age." - Evan Shellshear (48:15)
148 - UI/UX Design Considerations for LLMs in Enterprise Applications (Part 2)
23 Jul 2024
00:26:36
Ready for more ideas about UX for AI and LLM applications in enterprise environments? In part 2 of my topic on UX considerations for LLMs, I explore how an LLM might be used for a fictitious use case at an insurance company—specifically, to help internal tools teams to get rapid access to primary qualitative user research. (Yes, it’s a little “meta”, and I’m also trying to nudge you with this hypothetical example—no secret!) ;-) My goal with these episodes is to share questions you might want to ask yourself such that any use of an LLM is actually contributing to a positive UX outcome Join me as I cover the implications for design, the importance of foundational data quality, the balance between creative inspiration and factual accuracy, and the never-ending discussion of how we might handle hallucinations and errors posing as “facts”—all with a UX angle. At the end, I also share a personal story where I used an LLM to help me do some shopping for my favorite product: TRIP INSURANCE! (NOT!)
Highlights/ Skip to:
(1:05) I introduce a hypothetical internal LLM tool and what the goal of the tool is for the team who would use it
(5:31) Improving access to primary research findings for better UX
(10:19) What “quality data” means in a UX context
(12:18) When LLM accuracy maybe doesn’t matter as much
(14:03) How AI and LLMs are opening the door for fresh visioning work
(15:38) Brian’s overall take on LLMs inside enterprise software as of right now
(18:56) Final thoughts on UX design for LLMs, particularly in the enterprise
(20:25) My inspiration for these 2 episodes—and how I had to use ChatGPT to help me complete a purchase on a website that could have integrated this capability right into their website
Quotes from Today’s Episode
“If we accept that the goal of most product and user experience research is to accelerate the production of quality services, products, and experiences, the question is whether or not using an LLM for these types of questions is moving the needle in that direction at all. And secondly, are the potential downsides like hallucinations and occasional fabricated findings, is that all worth it? So, this is a design for AI problem.” - Brian T. O’Neill (8:09)
“What’s in our data? Can the right people change it when the LLM is wrong? The data product managers and AI leaders reading this or listening know that the not-so-secret path to the best AI is in the foundational data that the models are trained on. But what does the word *quality* mean from a product standpoint and a risk reduction one, as seen from an end-users’ perspective? Somebody who’s trying to get work done? This is a different type of quality measurement.” - Brian T. O’Neill (10:40)
“When we think about fact retrieval use cases in particular, how easily can product teams—internal or otherwise—and end-users understand the confidence of responses? When responses are wrong, how easily, if at all, can users and product teams update the model’s responses? Errors in large language models may be a significant design consideration when we design probabilistic solutions, and we no longer control what exactly our products and software are going to show to users. If bad UX can include leading people down the wrong path unknowingly, then AI is kind of like the team on the other side of the tug of war that we’re playing.” - Brian T. O’Neill (11:22)
“As somebody who writes a lot for my consulting business, and composes music in another, one of the hardest parts for creators can be the zero-to-one problem of getting started—the blank page—and this is a place where I think LLMs have great potential. But it also means we need to do the proper research to understand our audience, and when or where they’re doing truly generative or creative work—such that we can take a generative UX to the next level that goes beyond delivering banal and obviously derivative content.” - Brian T. O’Neill (13:31)
“One thing I actually like about the hype, investment, and excitement around GenAI and LLMs in the enterprise is that there is an opportunity for organizations here to do some fresh visioning work. And this is a place that designers and user experience professionals can help data teams as we bring design into the AI space.” - Brian T. O’Neill (14:04)
“If there was ever a time to do some new visioning work, I think now is one of those times. However, we need highly skilled design leaders to help facilitate this in order for this to be effective. Part of that skill is knowing who to include in exercises like this, and my perspective, one of those people, for sure, should be somebody who understands the data science side as well, not just the engineering perspective. And as I posited in my seminar that I teach, the AI and analytical data product teams probably need a fourth member. It’s a quartet and not a trio. And that quartet includes a data expert, as well as that engineering lead.” - Brian T. O’Neill (14:38)
147 - UI/UX Design Considerations for LLMs in Enterprise Applications (Part 1)
10 Jul 2024
00:25:46
Let’s talk about design for AI (which more and more, I’m agreeing means GenAI to those outside the data space). The hype around GenAI and LLMs—particularly as it relates to dropping these in as features into a software application or product—seems to me, at this time, to largely be driven by FOMO rather than real value. In this “part 1” episode, I look at the importance of solid user experience design and outcome-oriented thinking when deploying LLMs into enterprise products. Challenges with immature AI UIs, the role of context, the constant game of understanding what accuracy means (and how much this matters), and the potential impact on human workers are also examined. Through a hypothetical scenario, I illustrate the complexities of using LLMs in practical applications, stressing the need for careful consideration of benchmarks and the acceptance of GenAI's risks.
I also want to note that LLMs are a very immature space in terms of UI/UX design—even if the foundation models continue to mature at a rapid pace. As such, this episode is more about the questions and mindset I would be considering when integrating LLMs into enterprise software more than a suggestion of “best practices.”
Highlights/ Skip to:
(1:15) Currently, many LLM feature initiatives seem to mostly driven by FOMO
(2:45) UX Considerations for LLM-enhanced enterprise applications
(5:14) Challenges with LLM UIs / user interfaces
(7:24) Measuring improvement in UX outcomes with LLMs
(10:36) Accuracy in LLMs and its relevance in enterprise software
(11:28) Illustrating key consideration for implementing an LLM-based feature
(19:00) Leadership and context in AI deployment
(19:27) Determining UX benchmarks for using LLMs
(20:14) The dynamic nature of LLM hallucinations and how we design for the unknown
(21:16) Closing thoughts on Part 1 of designing for AI and LLMs
Quotes from Today’s Episode
“While many product teams continue to race to deploy some sort of GenAI and especially LLMs into their products—particularly this is in the tech sector for commercial software companies—the general sense I’m getting is that this is still more about FOMO than anything else.” - Brian T. O’Neill (2:07)
“No matter what the technology is, a good user experience design foundation starts with not doing any harm, and hopefully going beyond usable to be delightful. And adding LLM capabilities into a solution is really no different. So, we still need to have outcome-oriented thinking on both our product and design teams when deploying LLM capabilities into a solution. This is a cornerstone of good product work.” - Brian T. O’Neill (3:03)
“So, challenges with LLM UIs and UXs, right, user interfaces and experiences, the most obvious challenge to me right now with large language model interfaces is that while we’ve given users tremendous flexibility in the form of a Google search-like interface, we’ve also in many cases, limited the UX of these interactions to a text conversation with a machine. We’re back to the CLI in some ways.” - Brian T. O’Neill (5:14)
“Before and after we insert an LLM into a user’s workflow, we need to know what an improvement in their life or work actually means.”- Brian T. O’Neill (7:24)
"If it would take the machine a few seconds to process a result versus what might take a day for a worker, what’s the role and purpose of that worker going forward? I think these are all considerations that need to be made, particularly if you’re concerned about adoption, which a lot of data product leaders are." - Brian T. O’Neill (10:17)
“So, there’s no right or wrong answer here. These are all range questions, and they’re leadership questions, and context really matters. They are important to ask, particularly when we have this risk of reacting to incorrect information that looks plausible and believable because of how these LLMs tend to respond to us with a positive sheen much of the time.” - Brian T. O’Neill (19:00)
146 - (Rebroadcast) Beyond Data Science - Why Human-Centered AI Needs Design with Ben Shneiderman
25 Jun 2024
00:42:07
Ben Shneiderman is a leading figure in the field of human-computer interaction (HCI). Having founded one of the oldest HCI research centers in the country at the University of Maryland in 1983, Shneiderman has been intently studying the design of computer technology and its use by humans. Currently, Ben is a Distinguished University Professor in the Department of Computer Science at the University of Maryland and is working on a new book on human-centered artificial intelligence.
I’m so excited to welcome this expert from the field of UX and design to today’s episode of Experiencing Data! Ben and I talked a lot about the complex intersection of human-centered design and AI systems.
In our chat, we covered:
Ben's career studying human-computer interaction and computer science. (0:30)
'Building a culture of safety': Creating and designing ‘safe, reliable and trustworthy’ AI systems. (3:55)
'Like zoning boards': Why Ben thinks we need independent oversight of privately created AI. (12:56)
'There’s no such thing as an autonomous device': Designing human control into AI systems. (18:16)
A/B testing, usability testing and controlled experiments: The power of research in designing good user experiences. (21:08)
Designing ‘comprehensible, predictable, and controllable’ user interfaces for explainable AI systems and why [explainable] XAI matters. (30:34)
Quotes from Today’s Episode
The world of AI has certainly grown and blossomed — it’s the hot topic everywhere you go. It’s the hot topic among businesses around the world — governments are launching agencies to monitor AI and are also making regulatory moves and rules. … People want explainable AI; they want responsible AI; they want safe, reliable, and trustworthy AI. They want a lot of things, but they’re not always sure how to get them. The world of human-computer interaction has a long history of giving people what they want, and what they need. That blending seems like a natural way for AI to grow and to accommodate the needs of real people who have real problems. And not only the methods for studying the users, but the rules, the principles, the guidelines for making it happen. So, that’s where the action is. Of course, what we really want from AI is to make our world a better place, and that’s a tall order, but we start by talking about the things that matter — the human values: human rights, access to justice, and the dignity of every person. We want to support individual goals, a person’s sense of self-efficacy — they can do what they need to in the world, their creativity, their responsibility, and their social connections; they want to reach out to people. So, those are the sort of high aspirational goals that become the hard work of figuring out how to build it. And that’s where we want to go. - Ben (2:05)
The software engineering teams creating AI systems have got real work to do. They need the right kind of workflows, engineering patterns, and Agile development methods that will work for AI. The AI world is different because it’s not just programming, but it also involves the use of data that’s used for training. The key distinction is that the data that drives the AI has to be the appropriate data, it has to be unbiased, it has to be fair, it has to be appropriate to the task at hand. And many people and many companies are coming to grips with how to manage that. This has become controversial, let’s say, in issues like granting parole, or mortgages, or hiring people. There was a controversy that Amazon ran into when its hiring algorithm favored men rather than women. There’s been bias in facial recognition algorithms, which were less accurate with people of color. That’s led to some real problems in the real world. And that’s where we have to make sure we do a much better job and the tools of human-computer interaction are very effective in building these better systems in testing and evaluating. - Ben (6:10)
Every company will tell you, “We do a really good job in checking out our AI systems.” That’s great. We want every company to do a really good job. But we also want independent oversight of somebody who’s outside the company — someone who knows the field, who’s looked at systems at other companies, and who can bring ideas and bring understanding of the dangers as well. These systems operate in an adversarial environment — there are malicious actors out there who are causing trouble. You need to understand what the dangers and threats are to the use of your system. You need to understand where the biases come from, what dangers are there, and where the software has failed in other places. You may know what happens in your company, but you can benefit by learning what happens outside your company, and that’s where independent oversight from accounting companies, from governmental regulators, and from other independent groups is so valuable. - Ben (15:04)
There’s no such thing as an autonomous device. Someone owns it; somebody’s responsible for it; someone starts it; someone stops it; someone fixes it; someone notices when it’s performing poorly. … Responsibility is a pretty key factor here. So, if there’s something going on, if a manager is deciding to use some AI system, what they need is a control panel, let them know: what’s happening? What’s it doing? What’s going wrong and what’s going right? That kind of supervisory autonomy is what I talk about, not full machine autonomy that’s hidden away and you never see it because that’s just head-in-the-sand thinking. What you want to do is expose the operation of a system, and where possible, give the stakeholders who are responsible for performance the right kind of control panel and the right kind of data. … Feedback is the breakfast of champions. And companies know that. They want to be able to measure the success stories, and they want to know their failures, so they can reduce them. The continuous improvement mantra is alive and well. We do want to keep tracking what’s going on and make sure it gets better. Every quarter. - Ben (19:41)
Google has had some issues regarding hiring in the AI research area, and so has Facebook with elections and the way that algorithms tend to become echo chambers. These companies — and this is not through heavy research — probably have the heaviest investment of user experience professionals within data science organizations. They have UX, ML-UX people, UX for AI people, they’re at the cutting edge. I see a lot more generalist designers in most other companies. Most of them are rather unfamiliar with any of this or what the ramifications are on the design work that they’re doing. But even these largest companies that have, probably, the biggest penetration into the most number of people out there are getting some of this really important stuff wrong. - Brian (26:36)
Explainability is a competitive advantage for an AI system. People will gravitate towards systems that they understand, that they feel in control of, that are predictable. So, the big discussion about explainable AI focuses on what’s usually called post-hoc explanations, and the Shapley, and LIME, and other methods are usually tied to the post-hoc approach.That is, you use an AI model, you get a result and you say, “What happened?” Why was I denied a parole, or a mortgage, or a job? At that point, you want to get an explanation. Now, that idea is appealing, but I’m afraid I haven’t seen too many success stories of that working. … I’ve been diving through this for years now, and I’ve been looking for examples of good user interfaces of post-hoc explanations. It took me a long time till I found one. The culture of AI model-building would be much bolstered by an infusion of thinking about what the user interface will be for these explanations. And even the DARPA’s XAI—Explainable AI—project, which has 11 projects within it—has not really grappled with this in a good way about designing what it’s going to look like. Show it to me. … There is another way. And the strategy is basically prevention. Let’s prevent the user from getting confused and so they don’t have to request an explanation. We walk them along, let the user walk through the step—this is like Amazon checkout process, seven-step process—and you know what’s happened in each step, you can go back, you can explore, you can change things in each part of it. It’s also what TurboTax does so well, in really complicated situations, and walks you through it. … You want to have a comprehensible, predictable, and controllable user interface that makes sense as you walk through each step. - Ben (31:13)
145 - Data Product Success: Adopting a Customer-Centric Approach With Malcolm Hawker, Head of Data Management at Profisee
11 Jun 2024
00:53:09
Wait, I’m talking to a head of data management at a tech company? Why!? Well, today I'm joined by Malcolm Hawker to get his perspective around data products and what he’s seeing out in the wild as Head of Data Management at Profisee. Why Malcolm? Malcolm was a former head of product in prior roles, and for several years, I’ve enjoyed Malcolm’s musings on LinkedIn about the value of a product-oriented approach to ML and analytics. We had a chance to meet at CDOIQ in 2023 as well and he went on my “need to do an episode” list!
According to Malcom, empathy is the secret to addressing key UX questions that ensure adoption and business value. He also emphasizes the need for data experts to develop business skills so that they're seen as equals by their customers. During our chat, Malcolm stresses the benefits of a product- and customer-centric approach to data products and what data professionals can learn approaching problem solving with a product orientation.
Highlights/ Skip to:
Malcolm’s definition of a data product (2:10)
Understanding your customers’ needs is the first step toward quantifying the benefits of your data product (6:34)
How product makers can gain access to users to build more successful products (11:36)
Answering the UX question to get past the adoption stage and provide business value (16:03)
Data experts must develop business expertise if they want to be seen as equals by potential customers (20:07)
What people really mean by “data culture" (23:02)
Malcolm’s data product journey and his changing perspective (32:05)
Using empathy to provide a better UX in design and data (39:24)
Avoiding the death of data science by becoming more product-driven (46:23)
Where the majority of data professionals currently land on their view of product management for data products (48:15)
Quotes from Today’s Episode
“My definition of a data product is something that is built by a data and analytics team that solves a specific customer problem that the customer would otherwise be willing to pay for. That’s it.” - Malcolm Hawker (3:42)
“You need to observe how your customer uses data to make better decisions, optimize a business process, or to mitigate business risk. You need to know how your customers operate at a very, very intimate level, arguably, as well as they know how their business processes operate.” - Malcolm Hawker (7:36)
“So, be a problem solver. Be collaborative. Be somebody who is eager to help make your customers’ lives easier. You hear "no" when people think that you’re a burden. You start to hear more “yeses” when people think that you are actually invested in helping make their lives easier.” - Malcolm Hawker (12:42)
“We [data professionals] put data on a pedestal. We develop this mindset that the data matters more—as much or maybe even more than the business processes, and that is not true. We would not exist if it were not for the business. Hard stop.” - Malcolm Hawker (17:07)
“I hate to say it, I think a lot of this data stuff should kind of feel invisible in that way, too. It’s like this invisible ally that you’re not thinking about the dashboard; you just access the information as part of your natural workflow when you need insights on making a decision, or a status check that you’re on track with whatever your goal was. You’re not really going out of mode.” - Brian O’Neill (24:59)
“But you know, data people are basically librarians. We want to put things into classifications that are logical and work forwards and backwards, right? And in the product world, sometimes they just don’t, where you can have something be a product and be a material to a subsequent product.” - Malcolm Hawker (37:57)
“So, the broader point here is just more of a mindset shift. And you know, maybe these things aren’t necessarily a bad thing, but how do we become a little more product- and customer-driven so that we avoid situations where everybody thinks what we’re doing is a time waster?” - Malcolm Hawker (48:00)
144 - The Data Product Debate: Essential Tech or Excessive Effort? with Shashank Garg, CEO of Infocepts (Promoted Episode)
28 May 2024
00:52:38
Welcome to another curated, Promoted Episode of Experiencing Data!
In episode 144, Shashank Garg, Co-Founder and CEO of Infocepts, joins me to explore whether all this discussion of data products out on the web actually has substance and is worth the perceived extra effort. Do we always need to take a product approach for ML and analytics initiatives? Shashank dives into how Infocepts approaches the creation of data solutions that are designed to be actionable within specific business workflows—and as I often do, I started out by asking Shashank how he and Infocepts define the term “data product.” We discuss a few real-world applications Infocepts has built, and the measurable impact of these data products—as well as some of the challenges they’ve faced that your team might as well. Skill sets also came up; who does design? Who takes ownership of the product/value side? And of course, we touch a bit on GenAI.
Highlights/ Skip to
Shashank gives his definition of data products (01:24)
We tackle the challenges of user adoption in data products (04:29)
We discuss the crucial role of integrating actionable insights into data products for enhanced decision-making (05:47)
Shashank shares insights on the evolution of data products from concept to practical integration (10:35)
We explore the challenges and strategies in designing user-centric data products (12:30)
I ask Shashank about typical environments and challenges when starting new data product consultations (15:57)
Shashank explains how Infocepts incorporates AI into their data solutions (18:55)
We discuss the importance of understanding user personas and engaging with actual users (25:06)
Shashank describes the roles involved in data product development’s ideation and brainstorming stages (32:20)
The issue of proxy users not truly representing end-users in data product design is examined (35:47)
We consider how organizations are adopting a product-oriented approach to their data strategies (39:48)
Shashank and I delve into the implications of GenAI and other AI technologies on product orientation and user adoption (43:47)
Closing thoughts (51:00)
Quotes from Today’s Episode
“Data products, at least to us at Infocepts, refers to a way of thinking about and organizing your data in a way so that it drives consumption, and most importantly, actions.” - Shashank Garg (1:44)
“The way I see it is [that] the role of a DPM (data product manager)—whether they have the title or not—is benefits creation. You need to be responsible for benefits, not for outputs. The outputs have to create benefits or it doesn’t count. Game over” - Brian O’Neill (10:07)
We talk about bridging the gap between the worlds of business and analytics... There's a huge gap between the perception of users and the tech leaders who are producing it." - Shashank Garg (17:37)
“IT leaders often limit their roles to provisioning their secure data, and then they rely on businesses to be able to generate insights and take actions. Sometimes this handoff works, and sometimes it doesn’t because of quality governance.” - Shashank Garg (23:02)
“Data is the kind of field where people can react very, very quickly to what’s wrong.” - Shashank Garg (29:44)
“It’s much easier to get to a good prototype if we know what the inputs to a prototype are, which include data about the people who are going to use the solution, their usage scenarios, use cases, attitudes, beliefs…all these kinds of things.” - Brian O’Neill (31:49)
“For data, you need a separate person, and then for designing, you need a separate person, and for analysis, you need a separate person—the more you can combine, I don’t think you can create super-humans who can do all three, four disciplines, but at least two disciplines and can appreciate the third one that makes it easier.” - Shashank Garg (39:20)
“When we think of AI, we’re all talking about multiple different delivery methods here. I think AI is starting to become GenAI to a lot of non-data people. It’s like their—everything is GenAI.” - Brian O'Neill (43:48)
143 - The (5) Top Reasons AI/ML and Analytics SAAS Product Leaders Come to Me For UI/UX Design Help
14 May 2024
00:50:01
Welcome back! In today's solo episode, I share the top five struggles that enterprise SAAS leaders have in the analytics/insight/decision support space that most frequently leads them to think they have a UI/UX design problem that has to be addressed. A lot of today's episode will talk about "slow creep," unaddressed design problems that gradually build up over time and begin to impact both UX and your revenue negatively. I will also share 20 UI and UX design problems I often see (even if clients do not!) that, when left unaddressed, may create sales friction, adoption problems, churn, or unhappy end users. If you work at a software company or are directly monetizing an ML or analytical data product, this episode is for you!
Highlights/ Skip to
I discuss how specific UI/UX design problems can significantly impact business performance (02:51)
I discuss five common reasons why enterprise software leaders typically reach out for help (04:39)
The 20 common symptoms I've observed in client engagements that indicate the need for professional UI/UX intervention or training (13:22)
The dangers of adding too many features or customization and how it can overwhelm users (16:00)
The issues of integrating AI into user interfaces and UXs without proper design thinking (30:08)
I encourage listeners to apply the insights shared to improve their data products (48:02)
Quotes from Today’s Episode
“One of the problems with bad design is that some of it we can see and some of it we can't — unless you know what you're looking for." - Brian O’Neill (02:23)
“Design is usually not top of mind for an enterprise software product, especially one in the machine learning and analytics space. However, if you have human users, even enterprise ones, their tolerance for bad software is much lower today than in the past.” Brian O’Neill - (13:04)
“Early on when you're trying to get product market fit, you can't be everything for everyone. You need to be an A+ experience for the person you're trying to satisfy.” -Brian O’Neill (15:39)
“Often when I see customization, it is mostly used as a crutch for not making real product strategy and design decisions.” - Brian O’Neill (16:04)
"Customization of data and dashboard products may be more of a tax than a benefit. In the marketing copy, customization sounds like a benefit...until you actually go in and try to do it. It puts the mental effort to design a good solution on the user." - Brian O’Neill (16:26)
“We need to think strategically when implementing Gen AI or just AI in general into the product UX because it won’t automatically help drive sales or increase business value.” - Brian O’Neill (20:50)
“A lot of times our analytics and machine learning tools… are insight decision support products. They're supposed to be rooted in facts and data, but when it comes to designing these products, there's not a whole lot of data and facts that are actually informing the product design choices.” Brian O’Neill - (30:37)
“If your IP is that special, but also complex, it needs the proper UI/UX design treatment so that the value can be surfaced in such a way someone is willing to pay for it if not also find it indispensable and delightful.” - Brian O’Neill (45:02)
Access my free frameworks, guides, and additional reading for SAAS leaders on designing high-value ML and analytical data products: https://designingforanalytics.com/resources
Need help getting your product’s design/UX on track—so you can see more sales, less churn, and higher user adoption? Schedule a free 60-minute Discovery Call with me and I’ll give you my read on your situation and my recommendations to get ahead:https://designingforanalytics.com/services/
142 - Live Webinar Recording: My UI/UX Design Audit of a New Podcast Analytics Service w/ Chris Hill (CEO, Humblepod)
30 Apr 2024
00:50:56
Welcome to a special edition of Experiencing Data. This episode is the audio capture from a live Crowdcast video webinar I gave on April 26th, 2024 where I conducted a mini UI/UX design audit of a new podcast analytics service that Chris Hill, CEO of Humblepod, is working on to help podcast hosts grow their show. Humblepod is also the team-behind-the-scenes of Experiencing Data, and Chris had asked me to take a look at his new “Listener Lifecycle” tool to see if we could find ways to improve the UX and visualizations in the tool, how we might productize this MVP in the future, and how improving the tool’s design might help Chris help his prospective podcast clients learn how their listener data could help them grow their listenership and “true fans.”
On a personal note, it was fun to talk to Chris on the show given we speak every week: Humblepod has been my trusted resource for audio mixing, transcription, and show note summarizing for probably over 100 of the most recent episodes of Experiencing Data. It was also fun to do a “live recording” with an audience—and we did answer questions in the full video version. (If you missed the invite, join my Insights mailing list to get notified of future free webinars).
Chris talks about using data to improve podcasts and his approach to podcast numbers (03:06)
Chris introduces the Listener Lifecycle model which informed the dashboard design (08:17)
Chris and I discuss the importance of labeling and terminology in analytics UIs (11:00)
We discuss designing for practical use of analytics dashboards to provide actionable insights (17:05)
We discuss the challenges podcast hosts face in understanding and utilizing data effectively and how design might help (21:44)
I discuss how my CED UX framework for advanced analytics applications helps to facilitate actionable insights (24:37)
I highlight the importance of presenting data effectively and in a way that centers to user needs (28:50)
I express challenges users may have with podcast rankings and the reliability of data sources (34:24)
Chris and I discuss tailoring data reports to meet the specific needs of clients (37:14)
Quotes from Today’s Episode
“The irony for me as someone who has a podcast about machine learning and analytics and design is that I basically never look at my analytics.” - Brian O’Neill (01:14)
“The problem that I have found in podcasting is that the number that everybody uses to gauge whether a podcast is good or not is the download number…But there’s a lot of other factors in a podcast that can tell you how successful it’s going to be…where you can pull levers to…grow your show, or engage more with an audience.” - Chris Hill (03:20)
“I have a framework for user experience design for analytics called CED, which stands for Conclusions, Evidence, Data… The basic idea is really simple: lead your analytic service with conclusions.”- Brian O’Neill (24:37)
“Where the eyes glaze over is when tools are mostly about evidence generators, and we just give everybody the evidence, but there’s no actual analysis about how [this is] helping me improve my life or my business. It’s just evidence. I need someone to put that together.” - Brian O’Neill (25:23)
“Sometimes the data doesn’t provide enough of a conclusion about what to do…This is where your opinion starts to matter” - Brian O’Neill (26:07)
“It sounds like a benefit, but drilling down for most people into analytics stuff is usually a tax unless you’re an analyst.” - Brian O’Neill (27:39)
“Where’s the source of this data, and who decided what these numbers are? Because so much of this stuff…is not shared. As someone who’s in this space, it’s not even that it’s confusing. It’s more like, you got to distill this down for me.” - Brian O’Neill (34:57)
“Your clients are probably going to glaze over at this level of data because it’s not helping them make any decision about what to change.”- Brian O’Neill (37:53)
159 - Uncorking Customer Insights: How Data Products Revealed Hidden Gems in Liquor & Hospitality Retail
24 Dec 2024
00:40:47
Today, I’m talking to Andy Sutton, GM of Data and AI at Endeavour Group, Australia's largest liquor and hospitality company. In this episode, Andy—who is also a member of the Data Product Leadership Community (DPLC)—shares his journey from traditional, functional analytics to a product-led approach that drives their mission to leverage data and personalization to build the “Spotify for wines.” This shift has greatly transformed how Endeavour’s digital and data teams work together, and Andy explains how their advanced analytics work has paid off in terms of the company’s value and profitability.
You’ll learn about the often overlooked importance of relationships in a data-driven world, and how Andy sees the importance of understanding how users do their job in the wild (with and without your product(s) in hand). Earlier this year, Andy also gave the DPLC community a deeper look at how they brew data products at EDG, and that recording is available to our members in the archive.
We covered:
What it was like at EDG before Andy started adopting a producty approach to data products and how things have now changed (1:52)
The moment that caused Andy to change how his team was building analytics solutions (3:42)
The amount of financial value that Andy's increased with his scaling team as a result of their data product work (5:19)
How Andy and Endeavour use personalization to help build “the Spotify of wine” (9:15)
What the team under Andy required in order to make the transition to being product-led (10:27)
The successes seen by Endeavour through the digital and data teams’ working relationship (14:04)
What data product management looks like for Andy’s team (18:45)
How Andy and his team find solutions to bridging the adoption gap (20:53)
The importance of exposure time to end users for the adoption of a data product (23:43)
How talking to the pub staff at EDG’s bars and restaurants helps his team build better data products (27:04)
What Andy loves about working for Endeavour Group (32:25)
What Andy would change if he could rewind back to 2022 and do it all over (34:55)
Final thoughts (38:25)
Quotes from Today’s Episode
“I think the biggest thing is the value we unlock in terms of incremental dollars, right? I’ve not worked in analytics team before where we’ve been able to deliver a measurable value…. So, we’re actually—in theory—we’re becoming a profit center for the organization, not just a cost center. And so, there’s kind of one key metric. The second one, we do measure the voice of the team and how engaged our team are, and that’s on an upward trend since we moved to the new operating model, too. We also measure [a type of] “voice of partner” score [and] get something like a 4.1 out of 5 on that scale. Those are probably the three biggest ones: we’re putting value in, and we’re delivering products, I guess, our internal team wants to use, and we are building an enthused team at the same time.” - Andy Sutton (16:18)
“ You can put an [unfinished] product in front of an end customer, and they will give you quality feedback that you can then iterate on quickly. You can do that with an internal team, but you’ll lose credibility. Internal teams hold their analytics colleagues to a higher standard than the external customers. We’re trying to change how people do their roles. People feel very passionate about the roles they do, and how they do them, and what they bring to that role. We’re trying to build some of that into products. It requires probably more design consideration than I’d anticipated, and we’re still bringing in more designers to help us move closer to the start line.’” - Andy Sutton (19:25)
“ [Customer research] is becoming critical in terms of the products we’re building. You’re building a product, a set of products, or a process for an operations team. In our context, an operations team can mean a team of people who run a pub. It’s not just about convincing me, my product managers, or my data scientists that you need research; we want to take some of the resources out of running that bar for a period of time because we want to spend time with [the pub staff] watching, understanding, and researching. We’ve learned some of these things along the way… we’ve earned the trust, we’ve earned that seat at the table, and so we can have those conversations. It’s not trivial to get people to say, ‘I’ll give you a day-long workshop, or give you my team off of running a restaurant and a bar for the day so that they can spend time with you, and so you can understand our processes.’” - Andy Sutton (24:42)
“ I think what is very particular to pubs is the importance of the interaction between the customer and the person serving the customer. [Pubs] are about the connections between the staff and the customer, and you don’t get any of that if you’re just looking at things from a pure data perspective… You don’t see the [relationships between pub staff and customer] in the [data], so how do you capture some of that in your product? It’s about understanding the context of the data, not just the data itself.” - Andy Sutton (28:15)
“Every winery, every wine grower, every wine has got a story. These conversations [and relationships] are almost natural in our business. Our CEO started work on the shop floor in one of our stores 30 years ago. That kind of relationship stuff percolates through the organization. Having these conversations around the customer and internal stakeholders in the context of data feels a lot easier because storytelling and relationships are the way we get things done. An analytics team may get frustrated with people who can’t understand data, but it’s [the analytics team’s job] to help bridge that gap.” - Andy Sutton (32:34)
141 - How They’re Adopting a Producty Approach to Data Products at RBC with Duncan Milne
16 Apr 2024
00:43:49
In this week's episode of Experiencing Data, I'm joined by Duncan Milne, a Director, Data Investment & Product Management at the Royal Bank of Canada (RBC). Today, Duncan (who is also a member of the DPLC) gives a preview of his upcoming webinar on April 24, 2024 entitled, “Is that Data Product Worth Building? Estimating Economic Value…Before You Build It!” Duncan shares his experience of implementing a product mindset within RBC's Chief Data Office, and he explains some of the challenges, successes, and insights gained along the way. He emphasizes the critical role of understanding user needs and evaluating the economic impact of data products—before they are built. Duncan was gracious to let us peek inside and see a transformation that is currently in progress and I’m excited to check out his webinar this month!
Highlights/ Skip to:
I introduce Duncan Milne from RBC (00:00)
Duncan outlines the Chief Data Office's function at RBC (01:01)
We discuss data products and how they are used to improve business process (04:05)
The genesis behind RBC's move towards a product-centric approach in handling data, highlighting initial challenges and strategies for fostering a product mindset (07:26)
Duncan discusses developing a framework to guide the lifecycle of data products at RBC (09:29)
Duncan addresses initial resistance and adaptation strategies for engaging teams in a new product-centric methodology (12:04)
The scaling challenges of applying a product mindset across a large organization like RBC (22:02)
Insights into the framework for evaluating and prioritizing data product ideas based on their desirability, usability, feasibility, and viability. (26:30)
Measuring success and value in data product management (30:45)
Duncan explores process mapping challenges in banking (34:13)
Duncan shares creating specialized training for data product management at RBC (36:39)
Duncan offers advice and closing thoughts on data product management (41:38)
Quotes from Today’s Episode
“We think about data products as anything that solves a problem using data... it's helping someone do something they already do or want to do faster and better using data." - Duncan Milne (04:29)
“The transition to data product management involves overcoming initial resistance by demonstrating the tangible value of this approach." - Duncan Milne (08:38)
"You have to want to show up and do this kind of work [adopting a product mindset in data product management]…even if you do a product the right way, it doesn’t always work, right? The thing you make may not be desirable, it may not be as usable as it needs to be. It can be technically right and still fail. It’s not a guarantee, it’s just a better way of working.” - Brian T. O’Neill (15:03)
“[Product management]... it's like baking versus cooking. Baking is a science... cooking is much more flexible. It’s about... did we produce a benefit for users? Did we produce an economic benefit? ...It’s a multivariate problem... a lot of it is experimentation and figuring out what works." - Brian T. O'Neill (23:03)
"The easy thing to measure [in product management] is did you follow the process or not? That is not the point of product management at all. It's about delivering benefits to the stakeholders and to the customer." - Brian O'Neill (25:16)
“Data product is not something that is set in stone... You can leverage learnings from a more traditional product approach, but don’t be afraid to improvise." - Duncan Milne (41:38)
“Data products are fundamentally different from digital products, so even the traditional approach to product management in that space doesn’t necessarily work within the data products construct.” - Duncan Milne (41:55)
“There is no textbook for data product management; the field is still being developed…don’t be afraid to create your own answer if what exists out there doesn’t necessarily work within your context.”- Duncan Milne (42:17)
140 - Why Data Visualization Alone Doesn’t Fix UI/UX Design Problems in Analytical Data Products with T from Data Rocks NZ
02 Apr 2024
00:42:44
This week on Experiencing Data, I chat with a new kindred spirit! Recently, I connected with Thabata Romanowski—better known as "T from Data Rocks NZ"—to discuss her experience applying UX design principles to modern analytical data products and dashboards. T walks us through her experience working as a data analyst in the mining sector, sharing the journey of how these experiences laid the foundation for her transition to data visualization. Now, she specializes in transforming complex, industry-specific data sets into intuitive, user-friendly visual representations, and addresses the challenges faced by the analytics teams she supports through her design business. T and I tackle common misconceptions about design in the analytics field, discuss how we communicate and educate non-designers on applying UX design principles to their dashboard and application design work, and address the problem with "pretty charts." We also explore some of the core ideas in T's Design Manifesto, including principles like being purposeful, context-sensitive, collaborative, and humanistic—all aimed at increasing user adoption and business value by improving UX.
Highlights/ Skip to:
I welcome T from Data Rocks NZ onto the show (00:00)
T's transition from mining to leading an information design and data visualization consultancy. (01:43)
T discusses the critical role of clear communication in data design solutions. (03:39)
We address the misconceptions around the role of design in data analytics. (06:54)
T explains the importance of journey mapping in understanding users' needs. (15:25)
We discuss the challenges of accurately capturing end-user needs. (19:00)
T and I discuss the importance of talking directly to end-users when developing data products. (25:56)
T shares her 'I like, I wish, I wonder' method for eliciting genuine user feedback. (33:03)
T discusses her Data Design Manifesto for creating purposeful, context-aware, collaborative, and human-centered design principles in data. (36:37)
We wrap up the conversation and share ways to connect with T. (40:49)
Quotes from Today’s Episode
"It's not so much that people…don't know what design is, it's more that they understand it differently from what it can actually do..." - T from Data Rocks NZ (06:59)
"I think [misconception about design in technology] is rooted mainly in the fact that data has been very tied to IT teams, to technology teams, and they’re not always up to what design actually does.” - T from Data Rocks NZ (07:42)
“If you strip design of function, it becomes art. So, it’s not art… it’s about being functional and being useful in helping people.” - T from Data Rocks NZ (09:06)
"It’s not that people don’t know, really, that the word design exists, or that design applies to analytics and whatnot; it’s more that they have this misunderstanding that it’s about making things look a certain way, when in fact... It’s about function. It’s about helping people do stuff better." - T from Data Rocks NZ (09:19)
“Journey Mapping means that you have to talk to people... Data is an inherently human thing. It is something that we create ourselves. So, it’s biased from the start. You can’t fully remove the human from the data" - T from Data Rocks NZ (15:36)
“The biggest part of your data product success…happens outside of your technology and outside of your actual analysis. It’s defining who your audience is, what the context of this audience is, and to which purpose do they need that product. - T from Data Rocks NZ (19:08)
“[In UX research], a tight, empowered product team needs regular exposure to end customers; there’s nothing that can replace that." - Brian O'Neill (25:58)
“You have two sides [end-users and data team] that are frustrated with the same thing. The side who asked wasn’t really sure what to ask. And then the data team gets frustrated because the users don’t know what they want…Nobody really understood what the problem is. There’s a lot of assumptions happening there. And this is one of the hardest things to let go.” - T from Data Rocks NZ (29:38)
“No piece of data product exists in isolation, so understanding what people do with it… is really important.” - T from Data Rocks NZ (38:51)
139 - Monetizing SAAS Analytics and The Challenges of Designing a Successful Embedded BI Product (Promoted Episode)
19 Mar 2024
00:51:02
This week on Experiencing Data, something new as promised at the beginning of the year. Today, I’m exploring the world of embedded analytics with Zalak Trivedi from Sigma Computing—and this is also the first approved Promoted Episode on the podcast. In today’s episode, Zalak shares his journey as the product lead for Sigma’s embedded analytics and reporting solution which seeks to accelerate and simplify the deployment of decision support dashboards to their SAAS companies’ customers. Right there, we have the first challenge that Zalak was willing to dig into with me: designing a platform UX when we have multiple stakeholder and user types. In Sigma’s case, this means Sigma’s buyers, the developers that work at these SAAS companies to integrate Sigma into their products, and then the actual customers of these SAAS companies who will be the final end users of the resulting dashboards. also discuss the challenges of creating products that serve both beginners and experts and how AI is being used in the BI industry.
Highlights/ Skip to:
I introduce Zalak Trivedi from Sigma Computing onto the show (03:15)
Zalak shares his journey leading the vision for embedded analytics at Sigma and explains what Sigma looks like when implemented into a customer’s SAAS product . (03:54)
Zalak and I discuss the challenge of integrating Sigma's analytics into various companies' software, since they need to account for a variety of stakeholders. (09:53)
We explore Sigma's team approach to user experience with product management, design, and technical writing (15:14)
Zalak reveals how Sigma leverages telemetry to understand and improve user interactions with their products (19:54)
Zalak outlines why Sigma is a faster and more supportive alternative to building your own analytics (27:21)
We cover data monetization, specifically looking at how SAAS companies can monetize analytics and insights (32:05)
Zalak highlights how Sigma is integratingAI into their BI solution (36:15)
Zalak share his customers' current pain points and interests (40:25)
We wrap up with final thoughts and ways to connect with Zalak and learn more about Sigma (49:41)
Quotes from Today’s Episode
"Something I’m really excited about personally that we are working on is [moving] beyond analytics to help customers build entire data applications within Sigma. This is something we are really excited about as a company, and marching towards [achieving] this year." - Zalak Trivedi (04:04)
“The whole point of an embedded analytics application is that it should look and feel exactly like the application it’s embedded in, and the workflow should be seamless.” - Zalak Trivedi (09:29)
“We [at Sigma] had to switch the way that we were thinking about personas. It was not just about the analysts or the data teams; it was more about how do we give the right tools to the [SAAS] product managers and developers to embed Sigma into their product.” - Zalak Trivedi (11:30)
“You can’t not have a design, and you can’t not have a user experience. There’s always an experience with every tool, solution, product that we use, whether it emerged organically as a byproduct, or it was intentionally created through knowledge data... it was intentional” - Brian O’Neill (14:52)
“If we find that [in] certain user experiences,people are tripping up, and they’re not able to complete an entire workflow, we flag that, and then we work with the product managers, or [with] our customers essentially, and figure out how we can actually simplify these experiences.” - Zalak Trivedi (20:54)
“We were able to convince many small to medium businesses and startups to sign up with Sigma. The success they experienced after embedding Sigma was tremendous. Many of our customers managed to monetize their existing data within weeks, or at most, a couple of months, with lean development teams of two to three developers and a few business-side personnel, generating seven-figure income streams from that.” - Zalak Trivedi (32:05)
“At Sigma, our stance is, let’s not just add AI for the sake of adding AI. Let’s really identify [where] in the entire user journey does the intelligence really lie, and where are the different friction points, and let’s enhance those experiences.” - Zalak Trivedi (37:38)
“Every time [we at Sigma Computing] think about a new feature or functionality, we have to ensure it works for both the first-degree persona and the second-degree persona, and consider how it will be viewed by these different personas, because that is not the primary persona for which the foundation of the product was built." - Zalak Trivedi (48:08)
138 - VC Spotlight: The Impact of AI on SAAS and Data/Developer Products in 2024 w/ Ellen Chisa of BoldStart Ventures
05 Mar 2024
00:33:05
In this episode of Experiencing Data, I speak with Ellen Chisa, Partner at BoldStart Ventures, about what she’s seeing in the venture capital space around AI-driven products and companies—particularly with all the new GenAI capabilities that have emerged in the last year. Ellen and I first met when we were both engaged in travel tech startups in Boston over a decade ago, so it was great to get her current perspective being on the “other side” of products and companies working as a VC. Ellen draws on her experience in product management and design to discuss how AI could democratize software creation and streamline backend coding, design integration, and analytics. We also delve into her work at Dark and the future prospects for developer tools and SaaS platforms. Given Ellen’s background in product management, human-centered design, and now VC, I thought she would have a lot to share—and she did!
Highlights/ Skip to:
I introduce the show and my guest, Ellen Chisa (00:00)
Ellen discusses her transition from product and design to venture capital with BoldStart Ventures. (01:15)
Ellen notes a shift from initial AI prototypes to more refined products, focusing on building and testing with minimal data. (03:22)
Ellen mentions BoldStart Ventures' focus on early-stage companies providing developer and data tooling for businesses. (07:00)
Ellen discusses what she learned from her time at Dark and Lola about narrowing target user groups for technology products (11:54)
Ellen's Insights into the importance of user experience is in product design and the process venture capitalists endure to make sure it meets user needs (15:50)
Ellen gives us her take on the impact of AI on creating new opportunities for data tools and engineering solutions, (20:00)
Ellen and I explore the future of user interfaces, and how AI tools could enhance UI/UX for end users. (25:28)
Closing remarks and the best way to find Ellen on online (32:07)
Quotes from Today’s Episode
“It's a really interesting time in the venture market because on top of the Gen AI wave, we obviously had the macroeconomic shift. And so we've seen a lot of people are saying the companies that come out now are going to be great companies because they're a little bit more capital-constrained from the beginning, typically, and they'll grow more thoughtfully and really be thinking about how do they build an efficient business.”- Ellen Chisa (03: 22)
“We have this big technological shift around AI-enabled companies, and I think one of the things I’ve seen is, if you think back to a year ago, we saw a lot of early prototyping, and so there were like a couple of use cases that came up again and again.”-Ellen Chisa (3:42)
“I don't think I've heard many pitches from founders who consider themselves data scientists first. We definitely get some from ML engineers and people who think about data architecture, for sure..”- Ellen Chisa (05:06)
“I still prefer GUI interfaces to voice or text usually, but I think that might be an uncanny valley sort of thing where if you think of people who didn’t have technology growing up, they’re more comfortable with the more human interaction, and then you get, like, a chunk of people who are digital natives who prefer it.”- Ellen Chisa (24:51)
[Citing some excellent Boston-area restaurants!] “The Arc browser just shipped a bunch of new functionality, where instead of opening a bunch of tabs, you can say, “Open the recipe pages for Oleana and Sarma,” and it just opens both of them, and so it’s like multiple search queries at once.” - Ellen Chisa (27:22)
“The AI wave of technology biases towards people who already have products [in the market] and have existing datasets, and so I think everyone [at tech companies] is getting this big, top-down mandate from their executive team, like, ‘Oh, hey, you have to do something with AI now.’”- Ellen Chisa (28:37)
“I think it’s hard to really grasp what an LLM is until you do a fair amount of experimentation on your own. The experience of asking ChatGPT a simple search question compared to the experience of trying to train it to do something specific for you are quite different experiences. Even beyond that, there’s a tool called superwhisper that I like that you can take audio content and end up with transcripts, but you can give it prompts to change your transcripts as you’re going. So, you can record something, and it will give you a different output if you say you’re recording an email compared to [if] you’re recording a journal entry compared to [if] you’re recording the transcript for a podcast.”- Ellen Chisa (30:11)
137 - Immature Data, Immature Clients: When Are Data Products the Right Approach? feat. Data Product Architect, Karen Meppen
20 Feb 2024
00:44:50
This week, I'm chatting with Karen Meppen, a founding member of the Data Product Leadership Community and a Data Product Architect and Client Services Director at Hakkoda. Today, we're tackling the difficult topic of developing data products in situations where a product-oriented culture and data infrastructures may still be emerging or “at odds” with a human-centered approach. Karen brings extensive experience and a strong belief in how to effectively negotiate the early stages of data maturity. Together we look at the major hurdles that businesses encounter when trying to properly exploit data products, as well as the necessity of leadership support and strategy alignment in these initiatives. Karen's insights offer a roadmap for those seeking to adopt a product and UX-driven methodology when significant tech or cultural hurdles may exist.
Highlights/ Skip to:
I Introduce Karen Meppen and the challenges of dealing with data products in places where the data and tech aren't quite there yet (00:00)
Karen shares her thoughts on what it's like working with "immature data" (02:27)
Karen breaks down what a data product actually is (04:20)
Karen and I discuss why having executive buy-in is crucial for moving forward with data products (07:48)
The sometimes fuzzy definition of "data products." (12:09)
Karen defines “shadow data teams” and explains how they sometimes conflict with tech teams (17:35)
How Karen identifies the nature of each team to overcome common hurdles of connecting tech teams with business units (18:47)
How she navigates conversations with tech leaders who think they already understand the requirements of business users (22:48)
Using design prototypes and design reviews with different teams to make sure everyone is on the same page about UX (24:00)
Karen shares stories from earlier in her career that led her to embrace human-centered design to ensure data products actually meet user needs (28:29)
We reflect on our chat about UX, data products, and the “producty” approach to ML and analytics solutions (42:11)
Quotes from Today’s Episode
"It’s not really fair to get really excited about what we hear about or see on LinkedIn, at conferences, etc. We get excited about the shiny things, and then want to go straight to it when [our] organization [may not be ] ready to do that, for a lot of reasons." - Karen Meppen (03:00)
"If you do not have support from leadership and this is not something [they are] passionate about, you probably aren’t a great candidate for pursuing data products as a way of working." - Karen Meppen (08:30)
"Requirements are just friendly lies." - Karen, quoting Brian about how data teams need to interpret stakeholder requests (13:27)
"The greatest challenge that we have in technology is not technology, it’s the people, and understanding how we’re using the technology to meet our needs." - Karen Meppen (24:04)
"You can’t automate something that you haven’t defined. For example, if you don’t have clarity on your tagging approach for your PII, or just the nature of all the metadata that you’re capturing for your data assets and what it means or how it’s handled—to make it good, then how could you possibly automate any of this that hasn’t been defined?" - Karen Meppen (38:35)
"Nothing upsets an end-user more than lifting-and-shifting an existing report with the same problems it had in a new solution that now they’ve never used before." - Karen Meppen (40:13)
“Early maturity may look different in many ways depending upon the nature of business you’re doing, the structure of your data team, and how it interacts with folks.” (42:46)
Hakkōda, Karen's company, for more insights on data products and services:https://hakkoda.io/
136 - Navigating the Politics of UX Research and Data Product Design with Caroline Zimmerman
06 Feb 2024
00:44:16
This week I’m chatting with Caroline Zimmerman, Director of Data Products and Strategy at Profusion. Caroline shares her journey through the school of hard knocks that led to her discovery that incorporating more extensive UX research into the data product design process improves outcomes. We explore the complicated nature of discovering and building a better design process, how to engage end users so they actually make time for research, and why understanding how to navigate interdepartmental politics is necessary in the world of data and product design. Caroline reveals the pivotal moment that changed her approach to data product design, as well as her learnings from evolving data products with the users as their needs and business strategies change. Lastly, Caroline and I explore what the future of data product leadership looks like and Caroline shares why there's never been a better time to work in data.
Highlights/ Skip to:
Intros and Caroline describes how she learned crucial lessons on building data products the hard way (00:36)
The fundamental moment that helped Caroline to realize that she needed to find a different way to uncover user needs (03:51)
How working with great UX researchers influenced Caroline’s approach to building data products (08:31)
Why Caroline feels that exploring the ‘why’ is foundational to designing a data product that gets adopted (10:25)
Caroline’s experience building a data model for a client and what she learned from that experience when the client’s business model changed (14:34)
How Caroline addresses the challenge of end users not making time for user research (18:00)
A high-level overview of the UX research process when Caroline’s team starts working with a new client (22:28)
The biggest challenges that Caroline faces as a Director of Data Products, and why data products require the ability to navigate company politics and interests (29:58)
Caroline describes the nuances of working with different stakeholder personas (35:15)
Why data teams need to embrace a more human-led approach to designing data products and focus less on metrics and the technical aspects (38:10)
Caroline’s closing thoughts on what she’d like to share with other data leaders and how you can connect with her (40:48)
Quotes from Today’s Episode
“When I was first starting out, I thought that you could essentially take notes on what someone was asking for, go off and build it to their exact specs, and be successful. And it turns out that you can build something to exact specs and suffer from poor adoption and just not be solving problems because I did it as a wish fulfillment, laundry-list exercise rather than really thinking through user needs.” — Caroline Zimmerman (01:11)
“People want a thing. They’re paying for a thing, right? And so, just really having that reflex to try to gently come back to that why and spending sufficient time exploring it before going into solution build, even when people are under a lot of deadline pressure and are paying you to deliver a thing [is the most important element of designing a data product].” – Caroline Zimmerman (11:53)
“A data product evolves because user needs change, business models change, and business priorities change, and we need to evolve with it. It’s not like you got it right once, and then you’re good for life. At all.” – Caroline Zimmerman (17:48)
“I continue to have lots to learn about stakeholder management and understanding the interplay between what the organization needs to be successful, but also, organizations are made up of people with personal interests, and you need to understand both.” – Caroline Zimmerman (30:18)
“Data products are built in a political context. And just being aware of that context is important.” – Caroline Zimmerman (32:33)
“I think that data, maybe more than any other function, is transversal. I think data brings up politics because, especially with larger organizations, there are those departmental and team silos. And the whole thing about data is it cuts through those because it touches all the different teams. It touches all the different processes. And so in order to build great data products, you have to be navigating that political context to understand how to get things done transversely in organizations where most stuff gets done vertically.” – Caroline Zimmerman (34:37)
“Data leadership positions are data product expertise roles. And I think that often it’s been more technical people that have advanced into those roles. If you follow the LinkedIn-verse in data, it’s very much on every data leader’s mind at the moment: how do you articulate benefits to your CEO and your board and try to do that before it’s too late? So, I’d say that’s really the main thing and that there’s just never been a better time to be a data product person.” – Caroline Zimmerman (37:16)
135 - “No Time for That:” Enabling Effective Data Product UX Research in Product-Immature Organizations
23 Jan 2024
00:52:47
This week, I’m chatting with Steve Portigal, who is the Principal of Portigal Consulting and the Author of Interviewing Users. We discuss the changes that prompted him to release a second version of his book 10 years after its initial release, and dive into the best practices that any team can implement to start unlocking the value of data product UX research. Steve explains that the key to making time for user research is knowing what business value you’re after, not simply having a list of research questions. We then role-play through some in-depth examples of real-life experiences we’ve seen from both end users and leadership when it comes to implementing a user research strategy. Thhroughout our conversation, we come back to the idea that even taking imperfect action towards doing user research can lead to increased data product adoption and business value.
Highlights/ Skip to:
I introduce Steve Portigal, Principal of Portigal Consulting and Author of Interviewing Users (00:38)
What changes caused Steve to release a second edition of his book (00:58)
Steve and I discuss the importance of understanding how to conduct effective user research (03:44)
Steve explains why it’s crucial to understand that the business challenge and the research questions are two different things (08:16)
Brian and Steve role-play a common scenario that comes up in user research, and Steve explains an optimal workflow for user research (11:50)
The importance of provocation in performing user research (21:02)
How Steve would handle a situation where a member of leadership is preventing research being done with end users (24:23)
Why a consultative approach is valuable when getting buy-in for conducting user research (35:04)
Steve shares some of the major benefits of taking imperfect action towards starting user research (36:59)
The impact and value even easy wins in user research can have (42:54)
Steve describes the exploratory nature of user research and how to maximize the chance of finding the most valuable insights (46:57)
Where you can connect with Steve and get a copy of v2 of his book, Interviewing Users (49:35)
Quotes from Today’s Episode
“If you don’t know what you’re doing, and you don’t know what you should be investing effort-wise, that’s the inexperience in the approach. If you don’t know how to plan, what should we be trying to solve in this research? What are we trying to learn? What are we going to do with it in the organization? Who should we be talking to? How do we find them? What do we ask them? And then a really good one: how do we make sense of that information so that it has impact that we can take away?” — Steve Portigal (07:15)
“What do people get [from user research]? I think the chance for a team to align around something that comes in from the outside.” – Steve Portigal (41:36)
On the impact user research can have if teams embrace it: “They had a product that did a thing that no one [understood], and they had to change the product, but also change how they talked about it, change how they built it, and change how they packaged it. And that was a really dramatic turnaround. And it came out of our research, but [mostly] because they really leaned into making use of this stuff.” – Steve Portigal (42:35)
"If we knew all the questions to ask, we would just write a survey, right? It’s a lower time commitment from the participant to do that. But we’re trying to get at what we don’t know that we don’t know. For some of us, that’s fun!" – Steve Portigal (48:36)
134 - What Sanjeev Mohan Learned Co-Authoring “Data Products for Dummies”
09 Jan 2024
00:46:52
In this episode, I’m chatting with former Gartner analyst Sanjeev Mohan who is the Co-Author of Data Products for Dummies. Throughout our conversation, Sanjeev shares his expertise on the evolution of data products, and what he’s seen as a result of implementing practices that prioritize solving for use cases and business value. Sanjeev also shares a new approach of structuring organizations to best implement ownership and accountability of data product outcomes. Sanjeev and I also explore the common challenges of product adoption and who is responsible for user experience. I purposefully had Sanjeev on the show because I think we have pretty different perspectives from which we see the data product space.
Highlights/ Skip to:
I introduce Sanjeev Mohan, co-author of Data Products for Dummies (00:39)
Sanjeev expands more on the concept of writing a “for Dummies” book (00:53)
Sanjeev shares his definition of a data product, including both a technical and a business definition (01:59)
Why Sanjeev believes organizational changes and accountability are the keys to preventing the acceleration of shipping data products with little to no tangible value (05:45)
How Sanjeev recommends getting buy-in for data product ownership from other departments in an organization (11:05)
Sanjeev and I explore adoption challenges and the topic of user experience (13:23)
Sanjeev explains what role is responsible for user experience and design (19:03)
Who should be responsible for defining the metrics that determine business value (28:58)
Sanjeev shares some case studies of companies who have adopted this approach to data products and their outcomes (30:29)
Where companies are finding data product managers currently (34:19)
Sanjeev expands on his perspective regarding the importance of prioritizing business value and use cases (40:52)
Where listeners can get Data Products for Dummies, and learn more about Sanjeev’s work (44:33)
Quotes from Today’s Episode
“You may slap a label of data product on existing artifact; it does not make it a data product because there’s no sense of accountability. In a data product, because they are following product management best practices, there must be a data product owner or a data product manager. There’s a single person [responsible for the result]. — Sanjeev Mohan (09:31)
“I haven’t even mentioned the word data mesh because data mesh and data products, they don’t always have to go hand-in-hand. I can build data products, but I don’t need to go into the—do all of data mesh principles.” – Sanjeev Mohan (26:45)
“We need to have the right organization, we need to have a set of processes, and then we need a simplified technology which is standardized across different teams. So, this way, we have the benefit of reusing the same technology. Maybe it is Snowflake for storage, DBT for modeling, and so on. And the idea is that different teams should have the ability to bring their own analytical engine.” – Sanjeev Mohan (27:58)
“Generative AI, right now as we are recording, is still in a prototyping phase. Maybe in 2024, it’ll go heavy-duty production. We are not in prototyping phase for data products for a lot of companies. They’ve already been experimenting for a year or two, and now they’re actually using them in production. So, we’ve crossed that tipping point for data products.” – Sanjeev Mohan (33:15)
“Low adoption is a problem that’s not just limited to data products. How long have we had data catalogs, but they have low adoption. So, it’s a common problem.” – Sanjeev Mohan (39:10)
“That emphasis on technology first is a wrong approach. I tell people that I’m sorry to burst your bubble, but there are no technology projects, there are only business projects. Technology is an enabler. You don’t do technology for the sake of technology; you have to serve a business cause, so let’s start with that and keep that front and center.” – Sanjeev Mohan (43:03)
133 - New Experiencing Data Interviews Coming in January 2024
26 Dec 2023
00:02:33
Today I am sharing some highlights for 2023 from the podcast, and also letting you all know I’ll be taking a break from the podcast for the rest of December, but I’ll be back with a new episode on January 9th, 2024. I’ve also got two links to share with you—details inside!
Transcript
Greetings everyone - I’m taking a little break from Experiencing Data over December of 2023, but I’ll be back in January with more interviews and insights on leveraging UX design and product management to create indispensable data products, machine learning apps, and decision support tools.
Experiencing Data turned this year five years old back in November, with over 130 episodes to date! I still can’t believe it’s been going that long and how far we’ve come.
Some highlights for me in 2023 included launching the Data Product Leadership Community, finding out that the show is now in the top 2% of all podcasts worldwide according to ListenNotes, and most of all, hearing from you that the podcast, and my writing, and the guests that I have brought on are having an impact on your work, your careers, and hopefully the lives of your customers, users, and stakeholders as well!
So, for now, I’ve got just two links for you:
If you’re wondering how to either:
support the show yourself with a really fast review on Apple Podcasts,
to record a quick audio question for me to answer on the show,
or if you want to join my free Insights mailing lists where I share my bi-weekly ideas and thoughts and 1-page episode summaries of all the show drops that I put out here on Experiencing Data.
And secondly, if you need help increasing customer adoption, delight, the business value, or the usability of your analytics and machine learning applications in 2024, I invite you to set up a free discovery call with me 1 on 1.
You bring the questions, I’ll bring my ears, and by the end of the call, I’ll give you my best advice on how to move forward with your situation – whether it’s working with me or not. To schedule one of those free discovery calls, visitdesigningforanalytics.com/go
And finally, there will be some news coming out next year with the show, as well as my business, so I hope you’ll hop on the mailing list and stay tuned, that’s probably the best place to do that. And if you celebrate holidays in December and January, I hope they’re safe, enjoyable, and rejuvenating. Until 2024, stay tuned right here - and in the words of the great Arnold Schwarzenegger, I’ll be back.
132 - Leveraging Behavioral Science to Increase Data Product Adoption with Klara Lindner
12 Dec 2023
00:42:56
In this conversation with Klara Lindner, Service Designer at diconium data, we explore how behavioral science and UX can be used to increase adoption of data products. Klara describes how she went from having a highly technical career as an electrical engineer and being the founder of a solar startup to her current role in service design for data products. Klara shares powerful insights into the value of user research and human-centered design, including one which stopped me in my tracks during this episode: how the people making data products and evangelizing data-driven decision making aren’t actually following their own advice when it comes to designing their data products. Klara and I also explore some easy user research techniques that data professionals can use, and discuss who should ultimately be responsible for user adoption of data products. Lastly, Klara gives us a peek at her upcoming December 19th, 2023 webinar with the The Data Product Leadership Community (DPLC) where she will be going deeper on two frameworks from psychology and behavioral science that teams can use to increase adoption of data products. Klara is also a founding member of the DPLC and was one of—if not the very first—design/UX professionals to join.
Highlights/ Skip to:
I introduce Klara, and she explains the role of Service Design to our audience (00:49)
Klara explains how she realized she’s been doing design work longer than she thought by reflecting on the company she founded, Mobisol (02:09)
How Klara balances the desire to design great dashboards with the mission of helping end users (06:15)
Klara describes the psychology behind user research and her upcoming talk on December 19th at The Data Product Leadership Community (08:32)
What data product teams can do as a starting point to begin implementing user research principles (10:52)
Klara gives a powerful example of the type of insight and value even basic user research can provide (12:49)
Klara and I discuss a key revelation when it comes to designing data products for users, which is the irony that even developers use intuition as well as quantitative data when building (16:43)
What adjustments Klara had to make in her thinking when moving from a highly technical background to doing human-centered design (21:08)
Klara describes the two frameworks for driving adoption that she’ll be sharing in her talk at the DPLC on December 19th (24:23)
An example of how understanding and addressing adoption blockers is important for product and design teams (30:44)
How Klara has seen her teams adopt a new way of thinking about product & service design (32:55)
Klara gives her take on the Jobs to be Done framework, which she will also be sharing in her talk at the DPLC on December 19th (35:26)
Klara’s advice to teams that are looking to build products around generative AI (39:28)
Where listeners can connect with Klara to learn more (41:37)
158 - From Resistance to Reliance: Designing Data Products for Non-Believers with Anna Jacobson of Operator Collective
10 Dec 2024
00:43:41
After getting started in construction management, Anna Jacobson traded in the hard hat for the world of data products and operations at a VC company. Anna, who has a structural engineering undergrad and a masters in data science, is also a Founding Member of the Data Product Leadership Community (DPLC). However, her work with data products is more “accidental” and is just part of her responsibility at Operator Collective. Nonetheless, Anna had a lot to share about building data products, dashboards, and insights for users—including resistant ones!
That resistance is precisely what I wanted to talk to her about in this episode: how does Anna get somebody to adopt a data product to which they may be apathetic, if not completely resistant?
At the end of the episode, Anna gives us a sneak peek at what she’s planning to talk about in our final 2024 live DPLC group discussion coming up on 12/18/2024.
We covered:
(1:17) Anna's background and how she got involved with data products
(3:32) The ways Anna applied her experiences working in construction management to her current work with data products at a VC firm
(5:32) Explaining one of the main data products she works on at Operator Collective
(9:55) How Anna defines success for her data products
(15:21) The process of designing data products for "non-believers"
(21:08) How to think about "super users" and their feedback on a data product
(27:11) How a company's cultural problems can be a blocker for product adoption
(38:21) A preview of what you can expect from Anna's talk and live group discussion in the DPLC
(40:24) Closing thoughts from Anna
(42:54) Where you can find more from Anna
Quotes from Today’s Episode
“People working with data products are always thinking about how to [gain user adoption of their product]... I can’t think of a single one where [all users] were immediately on board. There’s a lot to unpack in what it takes to get non-believers on board, and it’s something that none of us ever get any training on. You just learn through experience, and it’s not something that most people took a class on in college. All of the social science around what we do gets really passed over for all the technical stuff. It takes thinking through and understanding where different [users] are coming from, and [understanding] that my perspective alone is not enough to make it happen.” - Anna Jacobson (16:00)
“If you only bring together the super users and don’t try to get feedback from the average user, you are missing the perspective of the person who isn’t passionate about the product. A non-believer is someone who is just over capacity. They may be very hard-working, they may be very smart, but they just don’t have the bandwidth for new things. That’s something that has to be overcome when you’re putting a new product into place.” - Anna Jacobson (22:35)
“If a company can’t find budget to support [a data product], that’s a cultural decision. It’s not a financial decision. They find the money for the things that they care about. Solving the technology challenge is pretty easy, but you have to have a company that’s motivated to do that. If you want to implement something new, be it a data product or any change in an organization, identifying the cultural barriers and figuring out how to bring [people in an organization] on board is the crux of it. The money and the technology can be found.” - Anna Jacobson (27:58)
“I think people are actually very bad at explaining what they want, and asking people what they want is not helpful. If you ask people what they want to do, then I think you have a shot at being able to build a product that does [what they want]. The executive sponsors typically have a very different perspective on what the product [should be] than the users do. If all of your information is getting filtered through the executive sponsor, you’re probably not getting the full picture” - Anna Jacobson (31:45)
“You want to define what the opportunity is, the problem, the solution, and you want to talk about costs and benefits. You want to align [the data product] with corporate strategy, and those things are fairly easy to map out. But as you get down to the user, what they want to know is, ‘How is this going to make my life easier? How is this going to make [my job] faster? How is it going to result in better outcomes?’ They may have an interest in how it aligns with corporate strategy, but that’s not what’s going to motivate them. It’s really just easier, faster, better.” - Anna Jacobson (35:00)
131 - 15 Ways to Increase User Adoption of Data Products (Without Handcuffs, Threats and Mandates) with Brian T. O’Neill
28 Nov 2023
00:36:57
This week I’m covering Part 1 of the 15 Ways to Increase User Adoption of Data Products, which is based on an article I wrote for subscribers of my mailing list. Throughout this episode, I describe why focusing on empathy, outcomes, and user experience leads to not only better data products, but also better business outcomes. The focus of this episode is to show you that it’s completely possible to take a human-centered approach to data product development without mandating behavioral changes, and to show how this approach benefits not just end users, but also the businesses and employees creating these data products.
Highlights/ Skip to:
Design behavior change into the data product. (05:34)
Establish a weekly habit of exposing technical and non-technical members of the data team directly to end users of solutions - no gatekeepers allowed. (08:12)
Change funding models to fund problems, not specific solutions, so that your data product teams are invested in solving real problems. (13:30)
Hold teams accountable for writing down and agreeing to the intended benefits and outcomes for both users and business stakeholders. Reject projects that have vague outcomes defined. (16:49)
Approach the creation of data products as “user experiences” instead of a “thing” that is being built that has different quality attributes. (20:16)
If the team is tasked with being “innovative,” leaders need to understand the innoficiency problem, shortened iterations, and the importance of generating a volume of ideas (bad and good) before committing to a final direction. (23:08)
Co-design solutions with [not for!] end users in low, throw-away fidelity, refining success criteria for usability and utility as the solution evolves. Embrace the idea that research/design/build/test is not a linear process. (28:13)
Test (validate) solutions with users early, before committing to releasing them, but with a pre-commitment to react to the insights you get back from the test. (31:50)
130 - Nick Zervoudis on Data Product Management, UX Design Training and Overcoming Imposter Syndrome
14 Nov 2023
00:48:56
Today I’m joined by Nick Zervoudis, Data Product Manager at CKDelta. As we dive into his career and background, Nick shares insights into his approach when it comes to developing both internal and external data products. Nick explains why he feels that a software engineering approach is the best way to develop a product that could have multiple applications, as well as the unique way his team is structured to best handle the needs of both internal and external customers. He also talks about the UX design course he took, how that affected his data product work and research with users, and his thoughts on dashboard design. We discuss common themes he’s observed when data product teams get it wrong, and how he manages feelings of imposter syndrome in his career as a DPM.
Highlights/ Skip to:
I introduce Nick, who is a Data Product Manager at CKDelta (00:35)
Nick’s mindset around data products and how his early career in consulting shaped his approach (01:30)
How Nick defines a data product and why he focuses more on the process rather than the end product (03:59)
The types of data products that Nick has helped design and his work on both internal and external projects at CKDelta (07:57)
The similarities and differences of working with internal versus external stakeholders (12:37)
Nick dives into the details of the data products he has built and how they feed into complex use cases (14:21)
The role that Nick plays in the Delta Power SaaS application and how the CKDelta team is structured around that product (17:14)
Where Nick sees data products going wrong and how he’s found value in filling those gaps (23:30)
Nick’s view on how a digital-first mindset affects the scalability of data products (26:15)
Why Nick is often heavily involved in the design element of data product development and the course he took that helped shape his design work (28:55)
The imposter syndrome that Nick has experienced when implementing this new strategy to data product design (36:51)
Why Nick feels that figuring things out yourself is an inherent part of the DPM role (44:53)
Nick shares the origins and information on the London Data Product Management meetup (46:08)
Quotes from Today’s Episode
“What I’m always trying to do is see, how can we best balance the customer’s need to get exactly the data point or insight that they’re after to the business need. ... There’s that constant tug of war between customization and standardization that I have the joy of adjudicating. I think it’s quite fun.” — Nick Zervoudis (16:40)
“I’ve had times where I was hired, told, 'You’re going to be the product manager for this data product that we have,' as if it’s already, to some extent built and maybe the challenge is scaling it or bringing it to more customers or improving it, and then within a couple of weeks of starting to peek under the hood, realizing that this thing that is being branded a product is actually a bunch of projects hiding under a trench coat.” — Nick Zervoudis (24:04)
“If I just speak to five users because they’re the users, they’ll give me the insight I need. […] Even when you have a massive product with a huge user base, people face the same issues.” — Nick Zervoudis (33:49)
“For me, it’s more about making sure that you’re bringing that more software engineering way of building things, but also, before you do that, knowing that your users' needs are going to [be varied]. So, it’s a combination of both, are we building the right thing—in other words, a product that’s flexible enough to meet the different needs of different users—but also, are we building it in the right way?” – Nick Zervoudis (27:51)
“It’s not to say I’m the only person thinking about [UX design], but very often, I’m the one driving it.” – Nick Zervoudis (30:55)
“You’re never going to be as good at the thing your colleague does because their job almost certainly is to be a specialist: they’re an architect, they’re a designer, they’re a developer, they’re a salesperson, whereas your job [as a DPM] is to just understand it enough that you can then pass information across other people.” – Nick Zervoudis (41:12)
“Every time I feel like an imposter, good. I need to embrace that, because I need to be working with people that understand something better than me. If I’m not, then maybe something’s gone wrong there. That’s how I’ve actually embraced impostor syndrome.” – Nick Zervoudis (41:35)
129 - Why We Stopped, Deleted 18 Months of ML Work, and Shifted to a Data Product Mindset at Coolblue
31 Oct 2023
00:35:21
Today I’m joined by Marnix van de Stolpe, Product Owner at Coolblue in the area of data science. Throughout our conversation, Marnix shares the story of how he joined a data science team that was developing a solution that was too focused on the delivery of a data-science metric that was not on track to solve a clear customer problem. We discuss how Marnix came to the difficult decision to throw out 18 months of data science work, what it was like to switch to a human-centered, product approach, and the challenges that came with it. Marnix shares the impact this decision had on his team and the stakeholders involved, as well as the impact on his personal career and the advice he would give to others who find themselves in the same position. Marnix is also a Founding Member of the Data Product Leadership Community and will be going much more into the details and his experience live on Zoom on November 16 @ 2pm ET for members.
Highlights/ Skip to:
I introduce Marnix, Product Owner at Coolblue and one of the original members of the Data Product Leadership Community (00:35)
Marnix describes what Coolblue does and his role there (01:20)
Why and how Marnix decided to throw away 18 months of machine learning work (02:51)
How Marnix determined that the KPI (metric) being created wasn’t enough to deliver a valuable product (07:56)
Marnix describes the conversation with his data science team on mapping the solution back to the desired outcome (11:57)
What the culture is like at Coolblue now when developing data products (17:17)
Marnix’s advice for data product managers who are coming into an environment where existing work is not tied to a desired outcome (18:43)
Marnix and I discuss why data literacy is not the solution to making more impactful data products (21:00)
The impact that Marnix’s human-centered approach to data product development has had on the stakeholders at Coolblue (24:54)
Marnix shares the ultimate outcome of the product his team was developing to measure product returns (31:05)
128 - Data Products for Dummies and The Importance of Data Product Management with Vishal Singh of Starburst
17 Oct 2023
00:53:01
Today I’m joined by Vishal Singh, Head of Data Products at Starburst and co-author of the newly published e-book, Data Products for Dummies. Throughout our conversation, Vishal explains how the variations in definitions for a data product actually led to the creation of the e-book, and we discuss the differences between our two definitions. Vishal gives a detailed description of how he believes Data Product Managers should be conducting their discovery and gathering feedback from end users, and how his team evaluates whether their data products are truly successful and user-friendly.
Highlights/ Skip to:
I introduce Vishal, the Head of Data Products at Starburst and contributor of the e-book Data Products for Dummies (00:37)
Vishal describes how his customers at Starburst all had a common problem, but differing definitions of a data product, which led to the creation of his e-book (01:15)
Vishal shares his one-sentence definition of a data product (02:50)
How Vishal’s definition of a data product differs from mine, and we both expand on the possibilities between the two (05:33)
The tactics Vishal uses to useful feedback to ensure the data products he develops are valuable for end users (07:48)
Why Vishal finds it difficult to get one on one feedback from users during the iteration phase of data product development (11:07)
The danger of sunk cost bias in the iteration phase of data product development (13:10)
Vishal describes how he views the role of a DPM when it comes to doing effective initial discovery (15:27)
How Vishal structures his teams and their interactions with each other and their end users (21:34)
Vishal’s thoughts on how design affects both data scientists and end users (24:16)
How DPMs at Starburst evaluate if the data product design is user-friendly (28:45)
Vishal’s views on where Designers are valuable in the data product development process (35:00)
Vishal and I discuss the importance of ensuring your products truly solve your user’s problems (44:44)
Where you can learn more about Vishal’s upcoming events and the e-book, Data Products for Dummies (49:48)
127 - On the Road to Adopting a “Producty” Approach to Data Products at the UK’s Care Quality Commission with Jonathan Cairns-Terry
03 Oct 2023
00:36:55
Today I’m joined by Jonathan Cairns-Terry, who is the Head of Insight Products at the Care Quality Commission. The Care Quality Commission is the the regulator for England for health and social care, and Jonathan recently joined their data team and is working to transform their approach to be more product-led and user-centric. Throughout our conversation, Jonathan shares valuable insights into what the first year of that type of shift looks like, and why it’s important to focus on outcomes, and how he measures progress. Jonathan and I explore the signals that told Jonathan it’s time for his team to invest in a designer, the benefits he’s gotten from UX research on his team, and the recent successes that Jonathan’s team is seeing as a result of implementing this approach. Jonathan is also a Founding Member of the Data Product Leadership Community and we discuss his upcoming webinar for the group on Oct 12, 2023.
Highlights/ Skip to:
I introduce Jonathan, who is the Head of Insight Products at the Care Quality Commission in the UK (00:37)
How Jonathan went from being a “maths person” to being a “product person” (01:02)
Who uses the data products that Jonthan makes at the Care Quality Commission (02:44)
Jonathan describes the recent transition towards a product focus (03:45)
How Jonathan expresses and measures the benefit and purpose of a product-led orientation, and how the team has embraced the transformation (07:08)
The nuance between evaluating outcomes and measuring outputs in a product-led approach, and how UX research has impacted Jonathan’s team (12:53)
What signals Jonathan received that told him it’s time to hire a designer (17:05)
How Jonathan’s team approaches shadowing users (21:20)
Some of the recent successes of the product-led approach Jonathan is implementing on his team (25:28)
What Jonathan would change if he had to start the process of moving to outcomes over outputs with his team all over again (30:04)
Get the full scoop on the topics discussed in this episode on October 12, 2023 when Jonathan presents his deep-dive webinar to the Data Product Leadership Community. Available to members only. Apply today.
126 - Designing a Product for Making Better Data Products with Anthony Deighton
19 Sep 2023
00:47:38
Today I’m joined by Anthony Deighton, General Manager of Data Products at Tamr. Throughout our conversation, Anthony unpacks his definition of a data product and we discuss whether or not he feels that Tamr itself is actually a data product. Anthony shares his views on why it’s so critical to focus on solving for customer needs and not simply the newest and shiniest technology. We also discuss the challenges that come with building a product that’s designed to facilitate the creation of better internal data products, as well as where we are in this new wave of data product management, and the evolution of the role.
Highlights/ Skip to:
I introduce Anthony, General Manager of Data Products at Tamr, and the topics we’ll be discussing today (00:37)
Anthony shares his observations on how BI analytics are an inch deep and a mile wide due to the data that’s being input (02:31)
Tamr’s focus on data products and how that reflects in Anthony’s recent job change from Chief Product Officer to General Manager of Data Products (04:35)
Anthony’s definition of a data product (07:42)
Anthony and I explore whether he feels that decision support is necessary for a data product (13:48)
Whether or not Anthony feels that Tamr qualifies as a data product (17:08)
Anthony speaks to the importance of focusing on outcomes and benefits as opposed to endlessly knitting together features and products (19:42)
The challenges Anthony sees with metrics like Propensity to Churn (21:56)
How Anthony thinks about design in a product like Tamr (30:43)
Anthony shares how data science at Tamr is a tool in his toolkit and not viewed as a “fourth” leg of the product triad/stool (36:01)
Anthony’s views on where we are in the evolution of the DPM role (41:25)
What Anthony would do differently if he could start over at Tamr knowing what he knows now (43:43)
125 - Human-Centered XAI: Moving from Algorithms to Explainable ML UX with Microsoft Researcher Vera Liao
05 Sep 2023
00:44:42
Today I’m joined by Vera Liao, Principal Researcher at Microsoft. Vera is a part of the FATE (Fairness, Accountability, Transparency, and Ethics of AI) group, and her research centers around the ethics, explainability, and interpretability of AI products. She is particularly focused on how designers design for explainability. Throughout our conversation, we focus on the importance of taking a human-centered approach to rendering model explainability within a UI, and why incorporating users during the design process informs the data science work and leads to better outcomes. Vera also shares some research on why example-based explanations tend to out-perform [model] feature-based explanations, and why traditional XAI methods LIME and SHAP aren’t the solution to every explainability problem a user may have.
Highlights/ Skip to:
I introduce Vera, who is Principal Researcher at Microsoft and whose research mainly focuses on the ethics, explainability, and interpretability of AI (00:35)
Vera expands on her view that explainability should be at the core of ML applications (02:36)
An example of the non-human approach to explainability that Vera is advocating against (05:35)
Vera shares where practitioners can start the process of responsible AI (09:32)
Why Vera advocates for doing qualitative research in tandem with model work in order to improve outcomes (13:51)
I summarize the slides I saw in Vera’s deck on Human-Centered XAI and Vera expands on my understanding (16:06)
Vera’s success criteria for explainability (19:45)
The various applications of AI explainability that Vera has seen evolve over the years (21:52)
Why Vera is a proponent of example-based explanations over model feature ones (26:15)
Strategies Vera recommends for getting feedback from users to determine what the right explainability experience might be (32:07)
The research trends Vera would most like to see technical practitioners apply to their work (36:47)
Summary of the four-step process Vera outlines for Question-Driven XAI design (39:14)
124 - The PiCAA Framework: My Method to Generate ML/AI Use Cases from a UX Perspective
22 Aug 2023
00:21:51
In this episode, I give an overview of my PiCAA Framework, which is a framework I shared at my keynote talk for Netguru’s annual conference, Burning Minds. This framework helps with brainstorming machine learning use cases or reverse engineering them, starting with the tactic. Throughout the episode, I give context to the preliminary types of work and preparation you and your team would want to do before implementing PiCAA, as well as the process and potential pitfalls you may run into, and the end results that make it a beneficial tool to experiment with.
Highlights/ Skip to:
Where/ how you might implement the PiCAA Framework (1:22)
Focusing on the human part of your ideas (5:04)
Keynote excerpt outlining the PiCAA Framework (7:28)
Closing a PiCAA workshop by exploring what could go wrong (18:03)
123 - Learnings From the CDOIQ Symposium and How Data Product Definitions are Evolving with Brian T. O’Neill
08 Aug 2023
00:27:17
Today I’m wrapping up my observations from the CDOIQ Symposium and sharing what’s new in the world of data. I was only able to attend a handful of sessions, but they were primarily ones tied to the topic of data products, which, of course, brings us to “What’s a data product?” During this episode, I cover some of what I’ve been hearing about the definition of this word, and I also share my revised v2 definition. I also walk through some of the questions that CDOs and fellow attendees were asking at the sessions I went to and a few reactions to those questions. Finally, I announce an exciting development on the launch of the Data Product Leadership Community.
Highlights/ Skip to:
Brian introduces the topic for this episode, including his wrap-up of the CDOIQ Symposium (00:29)
The general impressions Brian heard at the Symposium, including a focus on people & culture and an emphasis on data products (01:51)
The three main areas the definition of a data product covers according to Brian’s observations (04:43)
Brian describes how companies are looking for successful data product development models to follow and explores where new Data Product Managers are coming from (07:17)
A methodology that Brian feels leads to a successful data product team (10:14)
How Brian feels digital-native folks see the world of data products differently (11:29)
The topic of Data Mesh and Human-Centered Design and how it came up in two presentations at the CDOIQ Symposium (13:24)
The rarity of design and UX being talked about at data conferences, and why Brian feels that is the case (15:24)
Brian’s current definition of a data product and how it’s evolved from his V1 definition (18:43)
Brian lists the main questions that were being asked at CDOIQ sessions he attended around data products (22:19)
Where to find answers to many of the questions being asked about data products and an update on the Data Product Leader Community that he will launch in August 2023 (24:28)
Quotes from Today’s Episode
“I think generally what’s happening is the technology continues to evolve, I think it generally continues to get easier, and all of the people and cultural parts and the change management and all of that, that problem just persists no matter what. And so, I guess the question is, what are we going to do about it?” — Brian T. O’Neill (03:11)
“The feeling I got from the questions [at the CDOIQ Symposium], … and particularly the ones that were talking about the role of data product management and the value of these things was, it’s like they’re looking for a recipe to follow.” — Brian T. O’Neill (07:17)
“My guess is people are just kind of reading up about it, self-training a bit, and trying to learn how to do product on their own. I think that’s how you learn how to do stuff is largely through trial and error. You can read books, you can do all that stuff, but beginning to do it is part of it.” — Brian T. O’Neill (08:57)
“I think the most important thing is that data is a raw ingredient here; it’s a foundation piece for the solution that we’re going to make that’s so good, someone might pay to use it or trade something of value to use it. And as long as that’s intact, I think you’re kind of checking the box as to whether it’s a data product.” — Brian T. O’Neill (12:13)
“I also would say on the data mesh topic, the feeling I got from people who had been to this conference before was that was quite a hyped thing the last couple years. Now, it was not talked about as much, but I think now they’re actually seeing some examples of this working.” — Brian T. O’Neill (16:25)
“My current v2 definition right now is, ‘A data product is a managed, end-to-end software solution that organizes, refines, or transforms data to solve a problem that’s so important customers would pay for it or exchange something of value to use it.’” — Brian T. O’Neill (19:47)
“We know [the product is] of value because someone was willing to pay for it or exchange their time or switch from their old way of doing things to the new way because it has that inherent benefit baked in. That’s really the most important part here that I think any data product manager should fully be aligned with.” — Brian T. O’Neill (21:35)
122 - Listener Questions Answered: Conducting Effective Discovery for Data Products with Brian T. O’Neill
25 Jul 2023
00:33:46
Today I’m answering a question that was submitted to the show by listener Will Angel, who asks how he can prioritize and scale effective discovery throughout the data product development process. Throughout this episode, I explain why discovery work is a process that should be taking place throughout the lifecycle of a project, rather than a defined period at the start of the project. I also emphasize the value of understanding the benefit users will see from the product as the main goal, and how to streamline the effectiveness of the discovery process.
Highlights/ Skip to:
Brian introduces today’s topic, Discovery with Data Products, with a listener question (00:28)
Why Brian sees discovery work as something that is ongoing throughout the lifecycle of a project (01:53)
Brian tackles the first question of how to avoid getting killed by the process overhead of discovery and prioritization (03:38)
Brian discusses his take on the question, “What are the ultimate business and user benefits that the beneficiaries hope to get from the product?”(06:02)
The value Brian sees in stating anti-goals and anti-personas (07:47)
How creative work is valuable despite the discomfort of not being execution-oriented (09:35)
Why customer and stakeholder research activities need to be ongoing efforts (11:20)
The two modes of design that Brian uses and their distinct purposes (15:09)
Brian explains why a clear strategy is critical to proper prioritization (19:36)
Why doing a few things really well usually beats out delivering a bunch of features and products that don’t get used (23:24)
Brian on why saying “no” can be a gift when used correctly (27:18)
How you can join the Data Product Leadership Community for more dialog like this and how to submit your own questions to the show (32:25)
Quotes from Today’s Episode
“Discovery work, to me is something that largely happens up front at the beginning of a project, but it doesn’t end at the beginning of the project or product initiative, or whatever it is that you’re working on. Instead, I think discovery is a continual thing that’s going on all the time.” — Brian T. O’Neill (01:57)
“As tooling gets easier and easier and we need to stand up less infrastructure and basic pipelining in order to get from nothing to something, I think more of the work simply does become the discovery part of the work. And that is always going to feel somewhat inefficient because by definition it is.” — Brian T. O’Neill (04:48)
“Measuring [project management metrics] does not tell us whether or not the product is going to be valuable. It just tells us how fast are we writing the code and doing execution against something that may or may not actually have any value to the business at all.” — Brian T. O’Neill (07:33)
“How would you measure an improvement in the beneficiaries' lives? Because if you can improve their life in some way—and this often means me at work— the business value is likely to follow there.” — Brian T. O’Neill (18:42)
“Without a clear strategy, you’re not going to be able to do prioritization work efficiently because you don’t know what success looks like.” — Brian T. O’Neill (19:49)
“Doing a few things really well probably beats delivering a lot of stuff that doesn’t get used. There’s little point in a portfolio of data products that is really wide, but it’s very shallow in terms of value.” — Brian T. O’Neill (23:27)
“Anytime you’re going to be changing behavior or major workflows, the non-technical costs and work increase. And we have to figure out, ‘How are we going to market this and evangelize it and make people see the value of it?’ These types of behavior changes are really hard to implement and they need to be figured out during the design of the solution — not afterwards.” — Brian T. O’Neill (26:25)
157 - How this materials science SAAS company brings PM+UX+data science together to help materials scientists accelerate R&D
26 Nov 2024
00:34:58
R&D for materials-based products can be expensive, because improving a product’s materials takes a lot of experimentation that historically has been slow to execute. In traditional labs, you might change one variable, re-run your experiment, and see if the data shows improvements in your desired attributes (e.g. strength, shininess, texture/feel, power retention, temperature, stability, etc.). However, today, there is a way to leverage machine learning and AI to reduce the number of experiments a material scientist needs to run to gain the improvements they seek. Materials scientists spend a lot of time in the lab—away from a computer screen—so how do you design a desirable informatics SAAS that actually works, and fits into the workflow of these end users?
As the Chief Product Officer at MaterialsZone, Ori Yudilevich came on Experiencing Data with me to talk about this challenge and how his PM, UX, and data science teams work together to produce a SAAS product that makes the benefits of materials informatics so valuable that materials scientists depend on their solution to be time and cost-efficient with their R&D efforts.
We covered:
(0:45) Explaining what Ori does at MaterialZone and who their product serves
(2:28) How Ori and his team help make material science testing more efficient through their SAAS product
(9:37) How they design a UX that can work across various scientific domains
(14:08) How “doing product” at MaterialsZone matured over the past five years
(17:01) Explaining the "Wizard of Oz" product development technique
(21:09) The importance of integrating UX designers into the "Wizard of Oz"
(23:52) The challenges MaterialZone faces when trying to get users to adopt to their product
(32:42) Advice Ori would've given himself five years ago
(33:53) Where you can find more from MaterialsZone and Ori
Quotes from Today’s Episode
“The fascinating thing about materials science is that you have this variety of domains, but all of these things follow the same process. One of the problems [consumer goods companies] face is that they have to do lengthy testing of their products. This is something you can use machine learning to shorten. [Product research] is an iterative process that typically takes a long time. Using your data effectively and using machine learning to predict what can happen, what’s better to try out, and what will reduce costs can accelerate time to market.” - Ori Yudilevich (3:47)
“The difference [in time spent testing a product] can be up to 70% [i.e. you can run 70% fewer experiments using ML.] That [also] means 70% less resources you’re using. Under the ‘old system’ of trial and error, you were just trying out a lot of things. The human mind cannot process a large number of parameters at once, so [a materials scientist] would just start playing only with [one parameter at a time]. You’ll have many experiments where you just try to optimize [for] one parameter, but then you might have 20, 30, or 100 more [to test]. Using machine learning, you can change a lot of parameters at once. The model can learn what has the most effect, what has a positive effect, and what has a negative effect. The differences can be really huge.” - Ori Yudilevich (5:50)
“Once you go deeper into a use case, you see that there are a lot of differences. The types of raw materials, the data structure, the quantity of data, etc. For example, with batteries, you have lots of data because you can test hundreds all at once. Whereas with something like ceramics, you don’t try so many [experiments]. You just can’t. It’s much slower. You can’t do so many [experiments] in parallel. You have much less data. Your models are different, and your data structure is different. But there’s also quite a lot of commonality because you’re storing the data. In the end, you have each domain, some raw materials, formulations, tests that you’re doing, and different statistical plots that are very common.” - Ori Yudilvech (11:24)
“We’ll typically do what we call the ‘Wizard of Oz’ technique. You simulate as if you have a feature, but you’re actually working for your client behind the scenes. You tell them [the simulated feature] is what you’re doing, but then measure [the client’s response] to understand if there’s any point in further developing that feature. Once you validate it, have enough data, and know where the feature is going, then you’ll start designing it and releasing it in incremental stages. We’ve made a lot of progress in how we discover opportunities and how we build something iteratively to make sure that we’re always going in the right direction” - Ori Yudilevich (15:56)
“The main problem we’re encountering is changing the mindset of users. Our users are not people who sit in front of a computer. These are researchers who work in [a materials science] lab. The challenge [we have] is getting people to use the platform more. To see it’s worth [their time] to look at some insights, and run the machine learning models. We’re always looking for ways to make that transition faster… and I think the key is making [the user experience] just fun, easy, and intuitive.” - Ori Yudilevich (24:17)
“Even if you make [the user experience] extremely smooth, if [users] don’t see what they get out of it, they’re still not going to [adopt your product] just for the sake of doing it. What we find is if this [product] can actually make them work faster or develop better products– that gets them interested. If you’re adopting these advanced tools, it makes you a better researcher and worker. People who [adopt those tools] grow faster. They become leaders in their team, and they slowly drag the others in.” - Ori Yudilevich (26:55)
“Some of [MaterialsZone’s] most valuable employees are the people who have been users. Our product manager is a materials scientist. I’m not a material scientist, and it’s hard to imagine being that person in the lab. What I think is correct turns out to be completely wrong because I just don’t know what it’s like. Having [material scientists] who’ve made the transition to software and data science? You can’t replace that.” - Ori Yudilevich (31:32)
121 - How Sainsbury’s Head of Data Products for Analytics and ML Designs for User Adoption with Peter Everill
11 Jul 2023
00:39:40
Today I’m chatting with Peter Everill, who is the Head of Data Products for Analytics and ML Designs at the UK grocery brand, Sainsbury’s. Peter is also a founding member of the Data Product Leadership Community. Peter shares insights on why his team spends so much time conducting discovery work with users, and how that leads to higher adoption and in turn, business value. Peter also gives us his in-depth definition of a data product, including the three components of a data product and the four types of data products he’s encountered. He also shares the 8-step product management methodology that his team uses to develop data products that truly deliver value to end users. Pete also shares the #1 resource he would invest in right now to make things better for his team and their work.
Highlights/ Skip to:
I introduce Peter, who I met through the Data Product Leadership Community (00:37)
What the data team structure at Sainsbury’s looks like and how Peter wound up working there (01:54)
Peter shares the 8-step product management methodology that has been developed by his team and where in that process he spends most of his time (04:54)
How involved the users are in Peter’s process when it comes to developing data products (06:13)
How Peter was able to ensure that enough time is taken on discovery throughout the design process (10:03)
Who on Peter’s team is doing the core user research for product development (14:52)
Peter shares the three things that he feels make data product teams successful (17:09)
How Peter defines a data product, including the three components of a data product and the four types of data products (18:34)
Peter and I discuss the importance of spending time in discovery (24:25)
Peter explains why he measures reach and impact as metrics of success when looking at implementation (26:18)
How Peter solves for the gap when handing off a product to the end users to implement and adopt (29:20)
How Peter hires for data product management roles and what he looks for in a candidate (33:31)
Peter talks about what roles or skills he’d be looking for if he was to add a new person to his team (37:26)
Quotes from Today’s Episode
“I’m a big believer that the majority of analytics in its simplest form is improving business processes and decisions. A big part of our discovery work is that we align to business areas, business divisions, or business processes, and we spend time in that discovery space actually mapping the business process. What is the goal of this process? Ultimately, how does it support the P&L?” — Peter Everill (12:29)
“There’s three things that are successful for any organization that will make this work and make it stick. The first is defining what you mean by a data product. The second is the role of a data product manager in the organization and really being clear what it is that they do and what they don’t do. … And the third thing is their methodology, from discovery through to delivery. The more work you put upfront defining those and getting everyone trained and clear on that, I think the quicker you’ll get to an organization that’s really clear about what it’s delivering, how it delivers, and who does what.” – Peter Everill (17:31)
“The important way that data and analytics can help an organization firstly is, understanding how that organization is performing. And essentially, performance is how well processes and decisions within the organization are being executed, and the impact that has on the P&L.” – Peter Everill (20:24)
“The great majority of organizations don’t allocate that percentage [20-25%] of time to discovery; they are jumping straight into solution. And also, this is where organizations typically then actually just migrate what already exists from, maybe, legacy service into a shiny new cloud platform, which might be good from a defensive data strategy point of view, but doesn’t offer new net value—apart from speed, security and et cetera of the cloud. Ultimately, this is why analytics organizations aren’t generally delivering value to organizations.” – Peter Everill (25:37)
“The only time that value is delivered, is from a user taking action. So, the two metrics that we really focus on with all four data products [are] reach [and impact].” – Peter Everill (27:44)
“In terms of benefits realization, that is owned by the business unit. Because ultimately, you’re asking them to take the action. And if they do, it’s their part of the P&L that’s improving because they own the business, they own the performance. So, you really need to get them engaged on the release, and for them to have the superusers, the champions of the product, and be driving voice of the release just as much as the product team.” – Peter Everill (30:30)
On hiring DPMs: “Are [candidates] showing the aptitude, do they understand what the role is, rather than the experience? I think data and analytics and machine learning product management is a relatively new role. You can’t go on LinkedIn necessarily, and be exhausted with a number of candidates that have got years and years of data and analytics product management.” – Peter Everill (36:40)
120 - The Portfolio Mindset: Data Product Management and Design with Nadiem von Heydebrand (Part 2)
27 Jun 2023
00:41:35
Today I’m continuing my conversation with Nadiem von Heydebrand, CEO of Mindfuel. In the conclusion of this special 2-part episode, Nadiem and I discuss the role of a Data Product Manager in depth. Nadiem reveals which fields data product managers are currently coming from, and how a new data product manager with a non-technical background can set themselves up for success in this new role. He also walks through his portfolio approach to data product management, and how to prioritize use cases when taking on a data product management role. Toward the end, Nadiem also shares personal examples of how he’s employed these strategies, why he feels it’s so important for engineers to be able to see and understand the impact of their work, and best practices around developing a data product team.
Highlights / Skip to:
Brian introduces Nadiem and gives context for why the conversation with Nadiem led to a two-part episode (00:35)
Nadiem summarizes his thoughts on data product management and adds context on which fields he sees data product managers currently coming from (01:46)
Nadiem’s take on whether job listings for data product manager roles still have too many technical requirements (04:27)
Why some non-technical people fail when they transition to a data product manager role and the ways Nadiem feels they can bolster their chances of success (07:09)
Brian and Nadiem talk about their views on functional data product team models and the process for developing a data product as a team (10:11)
When Nadiem feels it makes sense to hire a data product manager and adopt a portfolio view of your data products (16:22)
Nadiem’s view on how to prioritize projects as a new data product manager (19:48)
Nadiem shares a story of when he took on an interim role as a head of data and how he employed the portfolio strategies he recommends (24:54)
How Nadiem evaluates perceived usability of a data product when picking use cases (27:28)
Nadiem explains why understanding go-to-market strategy is so critical as a data product manager (30:00)
Brian and Nadiem discuss the importance of today’s engineering teams understanding the value and impact of their work (32:09)
How Nadiem and his team came up with the idea to develop a SaaS product for data product managers (34:40)
Quotes from Today’s Episode
“So, data product management [...] is a combination of different capabilities [...] [including] product management, design, data science, and machine learning. We covered this in viability, desirability, feasibility, and datability. So, these are four dimensions [that] you combine [...] together to become a data product manager.” — Nadiem von Heydebrand (02:34)
“There is no education for data product management today, there’s no university degree. ... So, there’s nobody out there—from my perspective—who really has all the four dimensions from day one. It’s more like an evolution: you’re coming from one of the [parallel business] domains or from one of the [parallel business] fields and then you extend your skill set over time.” — Nadiem von Heydebrand (03:04)
“If a product manager has very good communication skills and is able to break down the needs in a proper way or in a good understandable way to its tech lead, or its engineering lead or data science lead, then I think it works out super well. If this bridge is missing, then it becomes a little bit tricky because then the distance between the product manager and the development team is too far.” – Nadiem von Heydebrand (09:10)
“I think every data leader out there has an Excel spreadsheet or a list of prioritized use cases or the most relevant use cases for the business strategy… You can think about this list as a portfolio. You know, some of these use cases are super valuable; some of these use cases maybe will not work out, and you have to identify those which are bringing real return on investment when you put effort in there.” – Nadiem von Heydebrand (19:01)
“I’m not a magician for data product management. I just focused on a very strategic view on my portfolio and tried to identify those cases and those data products where I can believe I can easily develop them, I have a high degree of adoption with my lines of business, and I can truly measure the added revenue and the impact.” – Nadiem von Heydebrand (26:31)
“As a true data product manager, from my point of view, you are someone who is empathetic for the lines of businesses, to understand what their underlying needs and what the problems are. At the same time, you are a business person. You try to optimize the portfolio for your own needs, because you have business goals coming from your leadership team, from your head of data, or even from the person above, the CTO, CIO, even CEO. So, you want to make sure that your value contribution is always transparent, and visible, measurable, tangible.” – Nadiem von Heydebrand (29:20)
“If we look into classical product management, I mean, the product manager has to understand how to market and how to go to the market. And it’s this exactly the same situation with data product managers within your organization. You are as successful as your product performs in the market. This is how you measure yourself as a data product manager. This is how you define success for yourself.” – Nadiem von Heydebrand (30:58)
Delight Software - the SAAS tool for data product managers to manage their portfolio of data products: https://delight.mindfuel.ai
119 - Skills vs. Roles: Data Product Management and Design with Nadiem von Heydebrand (Part 1)
13 Jun 2023
00:37:12
The conversation with my next guest was going so deep and so well…it became a two part episode! Today I’m chatting with Nadiem von Heydebrand, CEO of Mindfuel. Nadiem’s career journey led him from data science to data product management, and in this first, we will focus on the skills of data product management (DPM), including design. In part 2, we jump more into Nadiem’s take on the role of the DPM. Nadiem gives actionable insights into the realities of data product management, from the challenges of actually being able to talk to your end users, to focusing on the problems and unarticulated needs of your users rather than solutions. Nadiem and I also discuss how data product managers oversee a portfolio of initiatives, and why it’s important to view that portfolio as a series of investments. Nadiem also emphasizes the value of having designers on a data team, and why he hopes we see more designers in the industry.
Highlights/ Skip to:
Brian introduces Nadiem and his background going from data science to data product management (00:36)
Nadiem gives not only his definition of a data product, but also his related definitions of ‘data as product,’ ‘data as information,’ and ‘data as a model’ products (02:19)
Nadiem outlines the skill set and activities he finds most valuable in a data product manager (05:15)
How a data organization typically functions and the challenges a data team faces to prove their value (11:20)
Brian and Nadiem discuss the challenges and realities of being able to do discovery with the end users of data products (17:42)
Nadiem outlines how a portfolio of data initiatives has a certain investment attached to it and why it’s important to generate a good result from those investments (21:30)
Why Nadiem wants to see more designers in the data product space and the problems designers solve for data teams (25:37)
Nadiem shares a story about a time when he wished he had a designer to convert the expressed needs of the business into the true need of the customer (30:10)
The value of solving for the unarticulated needs of your product users, and Nadiem shares how focusing on problems rather than solutions helped him (32:32)
Nadiem shares how you can connect with him and find out more about his company, Mindfuel (36:07)
Quotes from Today’s Episode
“The product mindset already says it quite well. When you look into classical product management, you have something called the viability, the desirability, the feasibility—so these are three very classic dimensions of product management—and the fourth dimension, we at Mindfuel define for ourselves and for applications are, is the datability.” — Nadiem von Heydebrand (06:51)
“We can only prove our [data team’s] value if we unlock business opportunities in their [clients’] lines of businesses. So, our value contribution is indirect. And measuring indirect value contribution is very difficult in organizations.” — Nadiem von Heydebrand (11:57)
“Whenever we think about data and analytics, we put a lot of investment and efforts in the delivery piece. I saw a study once where it said 3% of investments go into discovery and 90% of investments go into delivery and the rest is operations and a little bit overhead and all around. So, we have to balance and we have to do proper discovery to understand what problem do we want to solve.” — Nadiem von Heydebrand (13:59)
“The best initiatives I delivered in my career, and also now within Mindfuel, are the ones where we try to build an end responsibility from the lines of businesses, among the product managers, to PO, the product owner, and then the delivery team.” – Nadiem von Heydebrand (17:00)
“As a consultant, I typically think in solutions. And when we founded Mindfuel, my co-founder forced me to avoid talking about the solution for an entire ten months. So, in whatever meeting we were sitting, I was not allowed to talk about the solution, but only about the problem space.” – Nadiem von Heydebrand (34:12)
“In scaled organizations, data product managers, they typically run a portfolio of data products, and each single product can be seen a little bit like from an investment point of view, this is where we putting our money in, so that’s the reason why we also have to prioritize the right use cases or product initiatives because typically we have limited resources, either it is investment money, people, resources or our time.” – Nadiem von Heydebrand (24:02)
“Unfortunately, we don’t see enough designers in data organizations yet. So, I would love to have more design people around me in the data organizations, not only from a delivery perspective, having people building amazing dashboards, but also, like, truly helping me in this kind of discovery space.” – Nadiem von Heydebrand (26:28)
118 - Attracting Talent and Landing a Role in Data Product Management with Kyle Winterbottom
30 May 2023
00:49:23
Today I’m chatting with Kyle Winterbottom, who is the owner of Orbition Group and an advisor/recruiter for companies who are hiring top talent in the data industry. Kyle and I discuss whether the concept of data products has meaningful value to companies, or if it’s in a hype cycle of sorts. Kyle then shares his views on what sets the idea of data products apart from other trends, the well-paid opportunities he sees opening up for product leaders in the data industry, and why he feels being able to increase user adoption and quantify the business impact of your work is also relevant in a candidate’s ability to negotiate higher pay. Kyle and I also discuss the strange tendency for companies to mistakenly prioritize technical skills for these roles, the overall job market for data product leaders, average compensation numbers, and what companies can do to attract this talent.
Highlights/ Skip to:
Kyle introduces himself and his company, Orbition Group (01:02)
Why Brian invited Kyle on the show to discuss the recruitment of technical talent for data & analytics teams (02:00)
Kyle shares what’s causing companies to build out data product teams (04:49)
The reason why viewing data as a product seems to be driving better adoption in Kyle’s view (07:22)
Does Kyle feel that the concept of data products is mostly hype or meaningful? (11:26)
The different levels of maturity Kyle sees in organizations that are approaching him for help hiring data product talent, and how soft skills are often overlooked (15:37)
Kyle’s views on who is successfully landing data product manager roles and how that’s starting to change (23:20)
What Kyle’s observations are on the salary bands for data product manager roles and the type of money people can make in this space (25:41)
Brian and Kyle discuss how the skills of DPMs can help these leaders improve earning potential (30:30)
Kyle’s observations and advice to companies seeking to improve the data product talent they attract (38:12)
How listeners can learn more about Kyle and Orbition Group (47:55)
Quotes from Today’s Episode
“I think data products, obviously, there’s starting to get a bit of hype around it, which I’ve got no doubt will start to lead organizations to look down that route, just because they see and hear about other organizations doing it. ... [but] what it’s helping organizations to do is to drive adoption.” — Kyle Winterbottom (05:45)
“I think we’re at a point now where it’s becoming more and more clear, day by day, week by week, the there’s more to [the data industry] than just the building of stuff.” – Kyle Winterbottom (12:56)
“The whole soft skills piece is becoming absolutely integral because it’s become—you know, it’s night and day now, between the people that are really investing in themselves in that area and how quickly they’re progressing in their career because of that. But yeah, most organizations don’t even think about that.” – Kyle Winterbottom (18:49)
“I think nine times out of ten, most businesses overestimate the importance of the technical stuff practically in every role. … Even data analysts, data scientists, all they’re bothered about is the tech stack that they’ve used, [but] there’s a lot more to it than just the tech that they use.” – Kyle Winterbottom (22:56)
“There’s probably a big opportunity for really good product people to move into the data space because it’s going to be well paid with lots of opportunity. [It’s] quite an interesting space.” – Kyle Winterbottom (24:05)
“As soon as you get to a point where if you can help to drive adoption and then you can quantify the commercial benefit of that adoption to the organization, that probably puts you up near the top in terms of percentile of being important to a data organization.” – Kyle Winterbottom (32:21)
“We’re forever talking in our industry about the importance of storytelling. Yeah, I’ve never seen a business once tell a good story about how good it is to work for them, specifically in regards to their data analytics team and telling a story about that.” – Kyle Winterbottom (39:37)
117 - Phil Harvey, Co-Author of “Data: A Guide to Humans,” on the Non-Technical Skills Needed to Produce Valuable AI Solutions
16 May 2023
00:39:39
Today I’m chatting with Phil Harvey, co-author of Data: A Guide to Humans and a technology professional with 23 years of experience working with AI and startups. In his book, Phil describes his philosophy of how empathy leads to more successful outcomes in data product development and the journey he took to arrive at this perspective. But what does empathy mean, and how do you measure its success? Brian and Phil dig into those questions, and Phil explains why he feels cognitive empathy is a learnable skill that one can develop and apply. Phil describes some leading indicators that empathy is needed on a data team, as well as leading indicators that a more empathetic approach to product development is working. While I use the term “design” or “UX” to describe a lot of what Phil is talking about, Phil actually has some strong opinions about UX and shares those on this episode. Phil also reveals why he decided to write Data: A Guide to Humans and some of the experiences that helped shape the book’s philosophy.
Highlights/ Skip to:
Phil introduces himself and explains how he landed on the name for his book (00:54)
How Phil met his co-author, Noelia Jimenez Martinez, and the reason they started writing Data: A Guide to Humans (02:31)
Phil unpacks his understanding of how he defines empathy, why it leads to success on AI projects, and what success means to him (03:54)
Phil walks through a couple scenarios where empathy for users and stakeholders was lacking and the impacts it had (07:53)
The work Phil has done internally to get comfortable doing the non-technical work required to make ML/AI/data products successful (13:45)
Phil describes some indicators that data teams can look for to know their design strategy is working (17:10)
How Phil sees the methodology in his book relating to the world of UX (user experience) design (21:49)
Phil walks through what an abstract concept like “empathy” means to him in his work and how it can be learned and applied as a practical skill (29:00)
Quotes from Today’s Episode
“If you take success in itself, this is about achieving your intended outcomes. And if you do that with empathy, your outcomes will be aligned to the needs of the people the outcomes are for. Your outcomes will be accepted by stakeholders because they’ll understand them.” — Phil Harvey (05:05)
“Where there’s people not discussing and not considering the needs and feelings of others, you start to get this breakdown, data quality issues, all that.” – Phil Harvey (11:10)
“I wanted to write code; I didn’t want to deal with people. And you feel when you can do technical things, whether it’s machine-learning or these things, you end up with the ‘I’ve got a hammer and now everything looks like a nail problem.’ But you also have the [attitude] that my programming will solve everything.” – Phil Harvey (14:48)
“This is what startup-land really taught me—you can’t do everything. It’s very easy to think that you can and then burn yourself out. You need a team of people.” – Phil Harvey (15:09)
“Let’s listen to the users. Let’s bring that perspective in as opposed to thinking about aligning the two perspectives. Because any product is a change. You don’t ride a horse then jump in a car and expect the car to work like the horse.” – Phil Harvey (22:41)
“Let’s say you’re a leader in this space. … Listen out carefully for who’s complaining about who’s not listening to them. That’s a first early signal that there’s work to be done from an empathy perspective.” – Phil Harvey (25:00)
“The perspective of the book that Noelia and I have written is that empathy—and cognitive empathy particularly—is also a learnable skill. There are concrete and real things you can practice and do to improve in those skills.” – Phil Harvey (29:09)
116 - 10 Reasons Your Customers Don’t Make Time for Your Data Product Initiatives + A Big Update on the Data Product Leadership Community (DPLC)
02 May 2023
00:45:56
Do you ever find it hard to get the requirements, problems, or needs out of your customers, stakeholders, or users when creating a data product? This week I’m coming to you solo to share reasons your stakeholders, users, or customers may not be making time for your discovery efforts. I’ve outlined 10 reasons, and delve into those in the first part of this episode.
In part two, I am going to share a big update about the Data Product Leadership Community (DPLC) I’m hoping to launch in June 2023. I have created a Google Doc outlining how v1 of the community will work as well as 6 specific benefits that I hope you’ll be able to achieve in the first year of participating. However, I need your feedback to know if this is shaping up into the community you want to join. As such, at the end of this episode, I’ll ask you to head over to the Google Doc and leave a comment. To get the document link, just add your email address to the DPLC announcement list at http://designingforanalytics.com/community and you’ll get a confirmation email back with the link.
115 - Applying a Product and UX-Driven Approach to Building Stuart’s Data Platform with Osian Jones
18 Apr 2023
00:45:19
Today I’m chatting with Osian Jones, Head of Product for the Data Platform at Stuart. Osian describes how impact and ROI can be difficult metrics to measure in a data platform, and how the team at Stuart has sought to answer this challenge. He also reveals how user experience is intrinsically linked to adoption and the technical problems that data platforms seek to solve. Throughout our conversation, Osian shares a holistic overview of what it was like to design a data platform from scratch, the lessons he’s learned along the way, and the advice he’d give to other data product managers taking on similar projects.
Highlights/ Skip to:
Osian describes his role at Stuart (01:36)
Brian and Osian explore the importance of creating an intentional user experience strategy (04:29)
Osian explains how having a clear mission enables him to create parameters to measure product success (11:44)
How Stuart developed the KPIs for their data platform (17:09)
Osian gives his take on the pros and cons of how data departments are handled in regards to company oversight (21:23)
Brian and Osian discuss how vital it is to listen to your end users rather than relying on analytics alone to measure adoption (26:50)
Osian reveals how he and his team went about designing their platform (31:33)
What Osian learned from building out the platform and what he would change if he had to tackle a data product like this all over again (36:34)
Quotes from Today’s Episode
“Analytics has been treated very much as a technical problem, and very much so on the data platform side, which is more on the infrastructure and the tooling to enable analytics to take place. And so, viewing that purely as a technical problem left us at odds in a way, compared to [teams that had] a product leader, where the user was the focus [and] the user experience was very much driving a lot of what was roadmap.” — Osian Jones (03:15)
“Whenever we get this question of what’s the impact? What’s the value? How does it impact our company top line? How does it impact our company OKRs? This is when we start to panic sometimes, as data platform leaders because that’s an answer that’s really challenging for us, simply because we are mostly enablers for analytics teams who are themselves enablers. It’s almost like there’s two different degrees away from the direct impact that your team can have.” — Osian Jones (12:45)
“We have to start with a very clear mission. And our mission is to empower everyone to make the best data-driven decisions as fast as possible. And so, hidden within there, that’s a function of reducing time to insight, it’s also about maximizing trust and obviously minimizing costs.” — Osian Jones (13:48)
“We can track [metrics like reliability, incidents, time to resolution, etc.], but also there is a perception aspect to that as well. We can’t underestimate the importance of listening to our users and qualitative data.” — Osian Jones (30:16)
“These were questions that I felt that I naturally had to ask myself as a product manager. … Understanding who our users are, what they are trying to do with data and what is the current state of our data platform—so those were the three main things that I really wanted to get to the heart of, and connecting those three things together.” – Osian Jones (35:29)
“The advice that I would give to anyone who is taking on the role of a leader of a data platform or a similar role is, you can easily get overwhelmed by just so many different use cases. And so, I would really encourage [leaders] to avoid that.” – Osian Jones (37:57)
“Really look at your data platform from an end-user perspective and almost think of it as if you were to put the data platform on a supermarket shelf, what would that look like? And so, for each of the different components, how would you market that in a single one-liner in terms of what can this do for me?” – Osian Jones (39:22)
114 - Designing Anti-Biasing and Explainability Tools for Data Scientists Creating ML Models with Josh Noble
04 Apr 2023
00:42:05
Today I’m chatting with Josh Noble, Principal User Researcher at TruEra. TruEra is working to improve AI quality by developing products that help data scientists and machine learning engineers improve their AI/ML models by combatting things like bias and improving explainability. Throughout our conversation, Josh—who also used to work as a Design Lead at IDEO.org—explains the unique challenges and importance of doing design and user research, even for technical users such as data scientists. He also shares tangible insights on what informs his product design strategy, the importance of measuring product success accurately, and the importance of understanding the current state of a solution when trying to improve it.
Highlights/ Skip to:
Josh introduces himself and explains why it’s important to do design and user research work for technical tools used by data scientists (00:43)
The work that TruEra does to mitigate bias in AI as well as their broader focus on AI quality management (05:10)
Josh describes how user roles informed TruEra’s design their upcoming monitoring product, and the emphasis he places on iterating with users (10:24)
How Josh approaches striking a balance between displaying extraneous information in the tools he designs vs. removing explainability (14:28)
Josh explains how TruEra measures product success now and how they envision that changing in the future (17:59)
The difference Josh sees between explainability and interpretability (26:56)
How Josh decided to go from being a designer to getting a data science degree (31:08)
Josh gives his take on what skills are most valuable as a designer and how to develop them (36:12)
Quotes from Today’s Episode
“We want to make machine learning better by testing it, helping people analyze it, helping people monitor models. Bias and fairness is an important part of that, as is accuracy, as is explainability, and as is more broadly AI quality.” — Josh Noble (05:13)
“These two groups, the data scientists and the machine-learning engineer, they think quite differently about the problems that they need to solve. And they have very different toolsets. … Looking at how we can think about making a product and building tools that make sense to both of those different groups is a really important part of user experience.” – Josh Noble (09:04)
“I’m a big advocate for iterating with users. To the degree possible, get things in front of people so they can tell you whether it works for them or not, whether it fits their expectations or not.” – Josh Noble (12:15)
“Our goal is to get people to think about AI quality differently, not to necessarily change. We don’t want to change their performance metrics. We don’t want to make them change how they calculate something or change a workflow that works for them. We just want to get them to a place where they can bring together our four pillars and build better models and build better AI.” – Josh Noble (17:38)
“I’ve always wanted to know what was going on underneath the design. I think it’s an important part of designing anything to understand how the thing that you are making is actually built.” – Josh Noble (31:56)
“There’s a empathy-building exercise that comes from using these tools and understanding where they come from. I do understand the argument that some designers make. If you want to find a better way to do something, spending a ton of time in the trenches of the current way that it’s done is not always the solution, right?” – Josh Noble (36:12)
“There’s a real empathy that you build and understanding that you build from seeing how your designs are actually implemented that makes you a better teammate. It makes you a better collaborator and ultimately, I think, makes you a better designer because of that.” – Josh Noble (36:46)
“I would say to the non-designers who work with designers, measuring designs is not invalidating the designer. It doesn’t invalidate the craft of design. It shouldn’t be something that designers are hesitant to do. I think it’s really important to understand in a qualitative way what your design is doing and understand in a quantitative way what your design is doing.” – Josh Noble (38:18)
113 - Turning the Weather into an Indispensable Data Product for Businesses with Cole Swain, VP Product at tomorrow.io
21 Mar 2023
00:38:53
Today I’m chatting with Cole Swain, VP of Product at Tomorrow.io. Tomorrow.io is an untraditional weather company that creates data products to deliver relevant business insights to their customers. Together, Cole and I explore the challenges and opportunities that come with building an untraditional data product. Cole describes some of the practical strategies he’s developed for collecting and implementing qualitative data from customers, as well as why he feels rapport-building with users is a critical skill for product managers. Cole also reveals how scientists are part of the fold when developing products at Tomorrow.io, and the impact that their product has on decision-making across multiple industries.
Highlights/ Skip to:
Cole describes what Tomorrow.io does (00:56)
The types of companies that purchase Tomorrow.io and how they’re using the products (03:45)
Cole explains how Tomorrow.io developed practical strategies for helping customers get the insights they need from their products (06:10)
The challenges Cole has encountered trying to design a good user experience for an untraditional data product (11:08)
Cole describes a time when a Tomorrow.io product didn’t get adopted, and how he and the team pivoted successfully (13:01)
The impacts and outcomes of decisions made by customers using products from Tomorrow.io (15:16)
Cole describes the value of understanding your active users and what skills and attributes he feels make a great product manager (20:11)
Cole explains the challenges of being horizontally positioned rather than operating within an [industry] vertical (23:53)
The different functions that are involved in developing Tomorrow.io (28:08)
What keeps Cole up at night as the VP of Product for Tomorrow.io (33:47)
Cole explains what he would do differently if he could come into his role from the beginning all over again (36:14)
Quotes from Today’s Episode
“[Customers aren't] just going to listen to that objective summary and go do the action. It really has to be supplied with a tremendous amount of information around it in a concise way. ... The assumption upfront was just, if we give you a recommendation, you’ll be able to go ahead and go do that. But it’s just not the case.” – Cole Swain (13:40)
“The first challenge is designing this product in a way that you can communicate that value really fast. Because everybody who signs up for new product, they’re very lazy at the beginning. You have to motivate them to be able to realize that, hey, this is something that you can actually harness to change the way that you operate around the weather.” – Cole Swain (11:46)
“People kind of overestimate at times the validity of even just real-time data. So, how do you create an experience that’s intuitive enough to be decision support and create confidence that this tool is different for them, while still having the empathy with the user, that this is still just a forecast in itself; you have to make your own decisions around it.” – Cole Swain (12:43)
“What we often find in weather is that the bigger decisions aren’t made in silos. People don’t feel confident to make it on their own and they require a team to be able to come in because they know the unpredictability of the scenarios and they feel that they need to be able to have partners or comrades in the situation that are in it together with them.” – Cole Swain (17:24)
“To me, there’s two super key capabilities or strengths in being a successful product manager. It’s pattern recognition and it’s the ability to create fast rapport with a customer: in your first conversation with a customer, within five minutes of talking with them, connect with them.” – Cole Swain (22:06)
“[It’s] not about ‘how can we deliver the best value singularly to a particular client,’ but ‘how can we recognize the patterns that rise the tide for all of our customers?’ And it might sound obvious that that’s something that you need to do, but it’s so easy to teeter into the direction of building something unique for a particular vertical.” – Cole Swain (25:41)
“Our sales team is just always finding new use cases. And we have to continue to say no and we have to continue to be disciplined in this arena. But I’d be lying to tell you if that didn’t keep me up at night when I hear about this opportunity of this solution we could build, and I know it can be done in a matter of X amount of time. But the risk of doing that is just too high, sometimes.” – Cole Swain (35:42)
112 - Solving for Common Pitfalls When Developing a Data Strategy featuring Samir Sharma, CEO of datazuum
07 Mar 2023
00:35:18
Today I’m chatting with Samir Sharma, CEO of datazuum. Samir is passionate about developing data strategies that drive business outcomes, and shares valuable insights into how problem framing and research can be done effectively from both the data and business side. Samir also provides his definition of a data strategy, and why it can be complicated to uncover whose job it is to create one. Throughout the conversation, Samir and I uncover the value of including different perspectives when implementing a data strategy and discuss solutions to various communication barriers. Of course, dashboards and data products also popped up in this episode as well!
Highlights/ Skip to:
How Samir defines a data strategy and whose job it is to create one (01:39)
The challenges Samir sees when trying to uncover and understand a company’s existing data strategy (03:39)
The problem with the problem statements that Samir commonly encounters (08:37)
Samir unpacks the communication challenges that lead to negative business outcomes when developing data products (14:05)
An example of how improving research and problem framing solved a problem for Samir’s first big client (24:33)
How speaking in a language your users understand can open the door to more exciting and valuable projects (31:08)
Quotes from Today’s Episode
“I don’t think business teams really care how you do it. If you can get an outcome—even if it’s quick and dirty. We’re not supposed to be doing these things for months on end. We’re supposed to be iterating quickly to start to show that result and add value and then building on top of that to show more value, more results.” — Samir Sharma (07:29)
“Language is so important for business teams and technical teams and data teams to actually be able to speak a common language which has common business constructs. Why are organizations trying to train 20,000 people on data literacy, when they’ve got a ten-person data team? Why not just teach the ten people in the data team business language?” — Samir Sharma (10:52)
“I will continuously talk about processes because there’s not enough done actually understanding processes and how data is an event that occurs when a process is kicked off. … If you don’t understand the process and how data is enabling that process, or how data is being generated and the trigger points, then you’re just building something without really understanding where I need to fit that product in or where I need to fit that workflow in.” – Samir Sharma (11:46)
“But I start with asking clear questions about if I built you this dashboard, what is the decision you’re going to make off the back of it? Nine times out of ten, that question isn’t asked, if I build you this widget on this dashboard, what decision or action are you going to make or take? And how is that going to be linked back to the map that strategic objective? And if you can ask that question, you can build with purpose.” – Samir Sharma (19:27)
“You show [users] a bit of value, you show them what they’ve been dying to have, you give them a little bit extra in that so they can really optimize their decisions, and suddenly, you’ve got both sides now speaking a language that is really based on business outcomes and results.” – Samir Sharma (32:38)
“If the people in that conversation are the developers on one side, the business team, and they’re starting to see a new narrative, even the developers will start to say, “Oh! Now, I know exactly why I’m doing this. Now, I know why I’m building it.” So, they’re also starting to learn about the business, about what impacts sales, and maybe how marketing then intertwines into that. It’s important that that is done, but not enough time has been taken on that approach.” – Samir Sharma (24:05)
The thing for me is, business teams don’t know what they don’t know, right? Most of the time, they’re asking a question. If I was on the data team and I’d already built a dashboard that would [answer that question], then I haven’t built it properly in the first instance. What I’ve done is I’ve built it for the beauty and the visualization instead of the what I would class is the ugliness and impact that I need.” – Samir Sharma (17:05)
156-The Challenges of Bringing UX Design and Data Science Together to Make Successful Pharma Data Products with Jeremy Forman
14 Nov 2024
00:41:37
Jeremy Forman joins us to open up about the hurdles– and successes that come with building data products for pharmaceutical companies. Although he’s new to Pfizer, Jeremy has years of experience leading data teams at organizations like Seagen and the Bill and Melinda Gates Foundation. He currently serves in a more specialized role in Pfizer’s R&D department, building AI and analytical data products for scientists and researchers. .
Jeremy gave us a good luck at his team makeup, and in particular, how his data product analysts and UX designers work with pharmaceutical scientists and domain experts to build data-driven solutions.. We talked a good deal about how and when UX design plays a role in Pfizer’s data products, including a GenAI-based application they recently launched internally.
Highlights/ Skip to:
(1:26) Jeremy's background in analytics and transition into working for Pfizer
(2:42) Building an effective AI analytics and data team for pharma R&D
(5:20) How Pfizer finds data products managers
(8:03) Jeremy's philosophy behind building data products and how he adapts it to Pfizer
(12:32) The moment Jeremy heard a Pfizer end-user use product management research language and why it mattered
(13:55) How Jeremy's technical team members work with UX designers
(18:00) The challenges that come with producing data products in the medical field
(23:02) How to justify spending the budget on UX design for data products
(24:59) The results we've seen having UX design work on AI / GenAI products
(25:53) What Jeremy learned at the Bill & Melinda Gates Foundation with regards to UX and its impact on him now
(28:22) Managing the "rough dance" between data science and UX
(33:22) Breaking down Jeremy's GenAI application demo from CDIOQ
(36:02) What would Jeremy prioritize right now if his team got additional funding
(38:48) Advice Jeremy would have given himself 10 years ago
(40:46) Where you can find more from Jeremy
Quotes from Today’s Episode
“We have stream-aligned squads focused on specific areas such as regulatory, safety and quality, or oncology research. That’s so we can create functional career pathing and limit context switching and fragmentation. They can become experts in their particular area and build a culture within that small team. It’s difficult to build good [pharma] data products. You need to understand the domain you’re supporting. You can’t take somebody with a financial background and put them in an Omics situation. It just doesn’t work. And we have a lot of the scars, and the failures to prove that.” - Jeremy Forman (4:12)
“You have to have the product mindset to deliver the value and the promise of AI data analytics. I think small, independent, autonomous, empowered squads with a product leader is the only way that you can iterate fast enough with [pharma data products].” - Jeremy Forman (8:46)
“The biggest challenge is when we say data products. It means a lot of different things to a lot of different people, and it’s difficult to articulate what a data product is. Is it a view in a database? Is it a table? Is it a query? We’re all talking about it in different terms, and nobody’s actually delivering data products.” - Jeremy Forman (10:53)
“I think when we’re talking about [data products] there’s some type of data asset that has value to an end-user, versus a report or an algorithm. I think it’s even hard for UX people to really understand how to think about an actual data product. I think it’s hard for people to conceptualize, how do we do design around that? It’s one of the areas I think I’ve seen the biggest challenges, and I think some of the areas we’ve learned the most. If you build a data product, it’s not accurate, and people are getting results that are incomplete… people will abandon it quickly.” - Jeremy Forman (15:56)
“ I think that UX design and AI development or data science work is a magical partnership, but they often don’t know how to work with each other. That’s been a challenge, but I think investing in that has been critical to us. Even though we’ve had struggles… I think we’ve also done a good job of understanding the [user] experience and impact that we want to have. The prototype we shared [at CDIOQ] is driven by user experience and trying to get information in the hands of the research organization to understand some portfolio types of decisions that have been made in the past. And it’s been really successful.” - Jeremy Forman (24:59)
“If you’re having technology conversations with your business users, and you’re focused only the technology output, you’re just building reports. [After adopting If we’re having technology conversations with our business users and only focused on the technology output, we’re just building reports. [After we adopted a human-centered design approach], it was talking [with end-users] about outcomes, value, and adoption. Having that resource transformed the conversation, and I felt like our quality went up. I felt like our output went down, but our impact went up. [End-users] loved the tools, and that wasn’t what was happening before… I credit a lot of that to the human-centered design team.” - Jeremy Forman (26:39)
“When you’re thinking about automation through machine learning or building algorithms for [clinical trial analysis], it becomes a harder dance between data scientists and human-centered design. I think there’s a lack of appreciation and understanding of what UX can do. Human-centered design is an empathy-driven understanding of users’ experience, their work, their workflow, and the challenges they have. I don’t think there’s an appreciation of that skill set.” - Jeremy Forman (29:20)
“Are people excited about it? Is there value? Are we hearing positive things? Do they want us to continue? That’s really how I’ve been judging success. Is it saving people time, and do they want to continue to use it? They want to continue to invest in it. They want to take their time as end-users, to help with testing, helping to refine it. Those are the indicators. We’re not generating revenue, so what does the adoption look like? Are people excited about it? Are they telling friends? Do they want more? When I hear that the ten people [who were initial users] are happy and that they think it should be rolled out to the whole broader audience, I think that’s a good sign.” - Jeremy Forman (35:19)
111 - Designing and Monetizing Data Products Like a Startup with Yuval Gonczarowski
21 Feb 2023
00:33:15
Today I’m chatting with Yuval Gonczarowski, Founder & CEO of the startup, Akooda. Yuval is a self-described “socially capable nerd” who has learned how to understand and meet the needs of his customers outside of a purely data-driven lens. Yuval describes how Akooda is able to solve a universal data challenge for leaders who don’t have complete visibility into how their teams are working, and also explains why it’s important that Akooda provide those data insights without bias. Yuval and I also explore why it’s so challenging to find great product leaders and his rule for getting useful feedback from customers and stakeholders.
Highlights/ Skip to:
Yuval describes what Akooda does (00:35)
The types of technical skills Yuval had to move away from to adopt better leadership capabilities within a startup (02:15)
Yuval explains how Akooda solves what he sees as a universal data problem for anyone in management positions (04:15)
How Akooda goes about designing for multiple user types (personas) (06:29)
Yuval describes how using Akooda internally (dogfooding!) helps inform their design strategy for various use cases (09:09)
The different strategies Akooda employs to ensure they receive honest and valuable feedback from their customers (11:08)
Yuval explains the three sales cycles that Akooda goes through to ensure their product is properly adapted to both their buyers and the end users of their tool (15:37)
How Yuval learned the importance of providing data-driven insights without a bias of whether the results are good or bad (18:22)
Yuval describes his core leadership values and why he feels a product can never be simple enough (24:22)
The biggest learnings Yuval had when building Akooda and what he’d do different if he had to start from scratch (28:18)
Why Yuval feels being the first Head of Product that reports to a CEO is both a very difficult position to be in and a very hard hire to get right (29:16)
Quotes from Today’s Episode
“Re: moving from a technical to product role: My first inclination would be straight up talk about the how, but that’s not necessarily my job anymore. We want to talk about the why and how does the customer perceive things, how do they look at things, how would they experience this new feature? And in a sense, [that’s] my biggest change in the way I see the world.” — Yuval Gonczarowski (03:01)
“We are a very data-driven organization. Part of it is our DNA, my own background. When you first start a company and you’re into your first handful of customers, a lot of decisions have to be made based on gut feelings, sort of hypotheses, scenarios… I’ve lived through this pain.” — Yuval Gonczarowski (09:43)
“I don’t believe I will get honest feedback from a customer if I don’t hurt their pocket. If you want honest feedback [from customers], you got to charge.” — Yuval Gonczarowski (11:38)
“Engineering is the most expensive resource we have. Whenever we allocate engineering resources, they have to be something the customer is going to use.” – Yuval Gonczarowski (13:04)
When selling a data product: “If you don’t build the right collateral and the right approach and mindset to the fact that it’s not enough when the contract is signed, it’s actually these three sales cycles of making sure that customer adoption is done properly, then you haven’t finished selling. Contract is step one, installation is step two, usage is step three. Until step three is done, haven’t really sold the product.” — Yuval Gonczarowski (16:59)
“By definition, all products are too complex. And it’s always tempting to add another button, another feature, another toggle. Let’s see what we can remove to make it easier.” – Yuval Gonczarowski (26:35)
110 - CDO Spotlight: The Value and Journey of Implementing a Data Product Mindset with Sebastian Klapdor of Vista
07 Feb 2023
00:32:52
Today I’m chatting with Dr. Sebastian Klapdor, Chief Data Officer for Vista. Sebastian has developed and grown a successful Data Product Management team at Vista, and it all began with selling his vision to the rest of the executive leadership. In this episode, Sebastian explains what that process was like and what he learned. Sebastian shares valuable insights on how he implemented a data product orientation at Vista, what makes a good data product manager, and why technology usage isn’t the only metric that matters when measuring success. He also shares what he would do differently if he had to do it all over again.
Highlights/ Skip to:
How Sebastian defines a data product (01:48)
Brian asks Sebastian about the change management process in leadership when implementing a data product approach (07:40)
The three dimensions that Sebastian and his team measure to determine adoption success (10:22)
Sebastian shares the financial results of Vista adopting a data product approach (12:56)
The size and scale of the data team at Vista, and how their different roles ensure success (14:30)
Sebastian explains how Vista created and grew a team of 35 data product managers (16:47)
The skills Sebastian feels data product managers need to be successful at Vista (22:02)
Sebastian describes what he would do differently if he had to implement a data product approach at a company again (29:46)
Quotes from Today’s Episode
“You need to establish a culture, and that’s often the hardest part that takes the longest - to treat data as an asset, and not to treat it as a byproduct, but to treat it as a product and treat it as a valuable thing.” – Sebastian Klapdor (07:56)
“One source of data product managers is taking data professionals. So, you take data engineers, data scientists, or former analysts, and develop them into the role by coaching them [through] the product management skills from the software industry.” – Sebastian Klapdor (17:39)
“We went out there and we were hiring people in the market who were experienced [Product Managers]. But we also see internal people, actually grooming and growing into all of these roles, both from these 80 folks who have been around before, but also from other areas of Vista.” – Sebastian Klapdor (20:28)
“[Being a good Product Manager] comes back to the good old classics of collaborating, of being empathetic to where other people are at, their priorities, and understanding where [our] priorities fit into their bigger piece, and jointly aligning on what is valuable for Vista.” – Sebastian Klapdor (22:27)
“I think there’s nothing more detrimental than saying, ‘Yeah, sure, we can deliver things, and with data, it can do everything.’ And then you disappoint people and you don’t stick to your promises. … If you don’t stick to your promise, it will hurt you.” – Sebastian Klapdor (23:04)
“You don’t do the typical waterfall approach of solving business problems with data. You don’t do the approach that a data scientist tries to get some data, builds a model, and hands it over to data engineer who should productionize that. And then the data engineer gets back and says certain features can’t be productionized because it’s very complex to get the data on a daily basis, or in real time. By doing [this work] in a data product team, you can work actually in Agile and you’re super fast building what we call a minimum lovable product.” – Sebastian Klapdor (26:15)
“That was the biggest learning … whom do we staff as data product managers? And what do we expect of a good data product manager? How does a career path look like? That took us a really long time to figure out.” – Sebastian Klapdor (30:18)
“We have a big, big, big commitment that we want to start stuffing UX designers onto our [data] product teams.” - Sebastian Klapdor (21:12)
109 - The Role of Product Management and Design in Turning ML/AI into a Valuable Business with Bob Mason from Argon Ventures
24 Jan 2023
00:32:43
Today I’m chatting with Bob Mason, Managing Partner at Argon Ventures. Bob is a VC who seeks out early-stage founders in the ML/AI space and helps them inform their go-to-market, product, and design strategies. In this episode, Bob reveals what he looks for in early-stage data and intelligence startups who are trying to leverage ML/AI. He goes on to explain why it’s important to identify what your strengths are and what you enjoy doing so you can surround yourself with the right team. Bob also shares valuable insight into how to earn trust with potential customers as an early-stage startup, how design impacts a product’s success, and his strategy for differentiating yourself and creating a valuable product outside of the ubiquitous “platform play.”
Highlights/ Skip to:
Bob explains why and how Argon Ventures focuses their investments in intelligent industry companies (00:53)
Brian and Bob discuss the importance of prioritizing go-to-market strategy over technology (03:42)
How Bob views the career progression from data science to product management, and the ways in which his own career has paralleled that journey (07:21)
The role customer adoption and user experience play for Bob and the companies he invests in, both pre-investment and post-investment (11:10)
Brian and Bob discuss the design capabilities of different teams and why Bob feels it’s something leaders need to keep top of mind (15:25)
Bob explains his recommendation to seek out quick wins for AI companies who can’t expect customers to wait for an ROI (19:09)
The importance Bob sees in identifying early adopters during a sales cycle for early-stage startups (21:34)
Bob describes how being customer-centric allows start-ups to build trust, garner quick wins, and inform their product strategy (23:42)
Bob and Brian dive into Bob’s belief that solving intrinsic business problems by vertical increases a start-up’s chance of success substantially over “the platform play” (27:29)
Bob gives insight into product trends he believes are going to be extremely impactful in the near future (29:05)
Quotes from Today’s Episode
“In a former life, I was a software engineer, founder, and CTO myself, so I have to watch myself to not just geek out on the technology itself because the most important element when you’re determining if you want to move forward with investment or not, is this: is there a real problem here to be solved or is this technology in search of a problem?” — Bob Mason (01:51)
“User-centric research is really valuable, particularly at the earliest stages. If you’re just off by a degree or two, several years down the road, that can be a really material roadblock that you hit. And so, starting off on the right foot, I think is super, super valuable.” – Bob Mason (06:12)
“I don’t think the technical folks in an early-stage startup absolve themselves of not being really intimately involved with their go-to-market and who they’re ultimately creating value for.” – Bob Mason (07:07)
“When we’re making an investment decision, startups don’t generally have any customers, and so we don’t necessarily use the signal of long-term customer adoption as a driver for our initial investment decision. But it’s very much top of mind after investment and as we’re trying to build and bring the first version of the product to market. Being very thoughtful and mindful of sort of customer experience and long-term adoption is absolutely critical.” – Bob Mason (11:23)
“If you’re a scientist, the way you’re presenting both raw data and sort of summaries of data could be quite different than if you’re working with a business analyst that’s a few years out of college with a liberal arts degree. How you interpret results and then present those results, I think, is actually a very interesting design problem.” – Bob Mason (18:40)
“I think initially, a lot of early AI startups just kind of assumed that customers would be patient and let the system run, [waiting] 3, 6, 9, 12 months [to get this] magical ROI, and that’s just not how people (buyers) operate.” – Bob Mason (21:00)
“Re: platform plays: Obviously, you could still create a tremendous platform that’s very broad, but we think if you focus on the business problem of that particular vertical or domain, that actually creates a really powerful wedge so you can increase your value proposition. You could always increase the breadth of a platform over time. But if you’re not solving that intrinsic problem at the very beginning, you may never get the chance to survive.” – Bob Mason (28:24)
108 - Google Cloud’s Bruno Aziza on What Makes a Good Customer-Obsessed Data Product Manager
10 Jan 2023
00:50:43
Today I’m chatting with Bruno Aziza, Head of Data & Analytics at Google Cloud. Bruno leads a team of outbound product managers in charge of BigQuery, Dataproc, Dataflow and Looker and we dive deep on what Bruno looks for in terms of skills for these leaders. Bruno describes the three patterns of operational alignment he’s observed in data product management, as well as why he feels ownership and customer obsession are two of the most important qualities a good product manager can have. Bruno and I also dive into how to effectively abstract the core problem you’re solving, as well as how to determine whether a problem might be solved in a better way.
Highlights / Skip to:
Bruno introduces himself and explains how he created his “CarCast” podcast (00:45)
Bruno describes his role at Google, the product managers he leads, and the specific Google Cloud products in his portfolio (02:36)
What Bruno feels are the most important attributes to look for in a good data product manager (03:59)
Bruno details how a good product manager focuses on not only the core problem, but how the problem is currently solved and whether or not that’s acceptable (07:20)
What effective abstracting the problem looks like in Bruno’s view and why he positions product management as a way to help users move forward in their career (12:38)
Why Bruno sees extracting value from data as the number one pain point for data teams and their respective companies (17:55)
Bruno gives his definition of a data product (21:42)
The three patterns Bruno has observed of operational alignment when it comes to data product management (27:57)
Bruno explains the best practices he’s seen for cross-team goal setting and problem-framing (35:30)
Quotes from Today’s Episode
“What’s happening in the industry is really interesting. For people that are running data teams today and listening to us, the makeup of their teams is starting to look more like what we do [in] product management.” — Bruno Aziza (04:29)
“The problem is the problem, so focus on the problem, decompose the problem, look at the frictions that are acceptable, look at the frictions that are not acceptable, and look at how by assembling a solution, you can make it most seamless for the individual to go out and get the job done.” – Bruno Aziza (11:28)
“As a product manager, yes, we’re in the business of software, but in fact, I think you’re in the career management business. Your job is to make sure that whatever your customer’s job is that you’re making it so much easier that they, in fact, get so much more done, and by doing so they will get promoted, get the next job.” – Bruno Aziza (15:41)
“I think that is the task of any technology company, of any product manager that’s helping these technology companies: don’t be building a product that’s looking for a problem. Just start with the problem back and solution from that. Just make sure you understand the problem very well.” (19:52)
“If you’re a data product manager today, you look at your data estate and you ask yourself, ‘What am I building to save money? When am I building to make money?’ If you can do both, that’s absolutely awesome. And so, the data product is an asset that has been built repeatedly by a team and generates value out of data.” – Bruno Aziza (23:12)
“[Machine learning is] hard because multiple teams have to work together, right? You got your business analyst over here, you’ve got your data scientists over there, they’re not even the same team. And so, sometimes you’re struggling with just the human aspect of it.” (30:30)
“As a data leader, an IT leader, you got to think about those soft ways to accomplish the stuff that’s binary, that’s the hard [stuff], right? I always joke, the hard stuff is the soft stuff for people like us because we think about data, we think about logic, we think, ‘Okay if it makes sense, it will be implemented.’ For most of us, getting stuff done is through people. And people are emotional, how can you express the feeling of achieving that goal in emotional value?” – Bruno Aziza (37:36)