Malcolm Hawker, Chief Data Officer at Profisee and host of CDO Matters Podcast, recently sparked a heated discussion on LinkedIn about where data catalogs are heading. His view? Data catalogs today are being commoditized and we need to pivot from data management to knowledge management. Join Malcolm, Tim, and Juan as they break down this debate and explore what other data professionals had to say about this shift in thinking. Enhance your listening experience with C&C Chat at data.world/podcasts

Andrea Gioia, CTO at Quantyca and Co-founder of Blindata, shares his insights on the real issues holding back data management. Spoiler: It's not the technology. Andrea discusses how the biggest challenges stem from people, collaboration, and the ways we handle knowledge. As AI continues to evolve, poor data management becomes an even bigger obstacle, making it clear that prioritizing knowledge management is more urgent than ever.

Enhance your listening experience with C&C Chat at data.world/podcasts

Dael Williamson, EMEA CTO at Databricks, joins us to reveal how interoperability is the key to unlocking expertise, exchanges, and ecosystems. Tune in for an honest, no-BS conversation on navigating the complexities of data ecosystems—and how standardization, context, and interoperability can be leveraged to tackle organizational challenges. Enhance your listening experience with C&C Chat at data.world/podcasts

In this episode of Catalog & Cocktails, Kristin Schooley from Learning Care Group sits down with Juan Sequeda and Tim Gasper to discuss the importance of teamwork and building relationships when it comes to scaling an analytics team within a large organization. The conversation covers a lot of ground, including the need to understand what reporting is needed and what story is being told, how to ensure scalability and compliance, as well as the importance of measuring usage and interpreting why certain data may not be utilized.

Key Takeaways:

• [00:00 - 01:10] Introduction & Cheers
• [01:15 - 03:11] What was your favorite class in school and why?
• [03:16 - 06:34] A data team of five
• [06:36 - 11:29] Partnering with business units and thinking about what is being learned
• [11:43 - 14:07] Data as a product and lifecycle management
• [14:22 - 15:13] Data literacy isn't necessarily the phrase we should be using
• [15:15 - 18:13] Business analytics and understanding how to present data
• [18:16 - 22:51] How organizations organize their data teams with checks and balances
• [22:52 - 26:18] Business literacy, centralized teams, and scaling beyond a bottleneck
• [26:20 - 30:31] Incentivizing for having documentation up to date
• [30:40 - 34:57] Lightning round
• [35:00 - 39:46] Tim & Juan's Takeaways
• [39:46 - 43:06] Three questions

Are we doing data and analytics correctly? Self service, centralization vs decentralization, analytics vs operations… so many aspects that data teams need to consider.

Join this week’s episode of Catalog & Cocktails with hosts Juan and Tim as they speak with special guest Bethany Lyons to discuss how we should have a separation between data operations an data analytics

Key Takeaways:

• [00:10 - 02:36] Meet Bethany Lyons, Chief Proctor Officer at KAWA Analytics
• [02:40 - 04:23] What's your favorite board game?
• [04:24 - 05:29] Encouraging self-service analytics is why Bethany thinks we are doing data analytics wrong
• [05:30 - 08:04] How the self-service analytics problem started
• [08:10 - 11:12] How business operators utilize data, and examples with Tableau deployment
• [11:13 - 14:09] Tying business logic to an analytics tools and what could go wrong
• [14:10 - 15:55] "Is this an analytics request or an operational request?"
• [15:55 - 16:35] Changing parameters and asking the right system
• [16:35 - 19:35] Evolving from self-service analytics to self-service governance programs
• [19:39 - 21:14] Spreadsheet data, security, and incentives
• [21:22 - 24:25] Self-service governance, shared accountability paired with great security
• [24:26 - 25:50] Data stewards and ownership over business areas
• [25:51 - 27:04] Sharing, auditing, and tracking
• [27:12 - 28:50] Decentralizing accountability and mindsets when dealing with data
• [28:50 - 32:40] How to make the switch to prioritizing decentralizing accountability over who writes SQL queries, and KAWA's role
• [32:41 - 35:15] Centralized policy frameworks and definitions
• [35:22 - 39:05] Thoughts on self-service operations and the centralization of analytics
• [39:06 - 40:52] Critical business logic and Excel spreadsheets
• [40:53 - 42:06] Centralizing business logic vs decentralizing business logic
• [42:06 - 44:33] Avoiding business logic being owned by one individual, and looking at decision models
• [44:33 - 45:45] Critical documents, and the idea of a Google Drive for data
• [45:49 - 52:41] Lightning round
• [52:42 - 57:26] Takeaways
• [57:44 - 01:00:16] Three questions

This week, we are having a throwback elixir with some very special surprise guests.

Tune in with hosts Juan and Tim to find out who.

Key Takeaways:

• [00:26 - 01:51] Episode intro: Data Day and What keeps you up at night in data?
• [02:04 - 02:54] Joe Reis, the economy and showing value
• [02:59 - 03:30] Omar Khawaja: Anything that ends with board, data dashboards
• [03:54 - 04:05] Vip Parmar: Jet lag & underutilized data
• [04:16 - 04:51] Laura Ellis: Data governance and change management
• [04:59 - 06:18] Mohammed Syed: Change management and data solutions
• [06:22 - 06:49] Tim: Determining focus in a sea of technology opportunities
• [06:50 - 07:58] Juan: Disconnect between executives and how data teams work with the business

Increasing adoption of data products, by design

Look no further than Brian T. O’Neill’s bio to tell you that’s what he does best.

Our special guest this week knows that low adoption of data products are enterprises biggest enemy. Not just in terms of quantity, but also quality of these investments. Why are teams so often creating technically right, effectively wrong data products? Why do people fail to adopt when it’s them post crucial part of becoming data driven organizations?

These burning questions have answers.

Join hosts Juan, Tim, and guest Brian T. O’Neill on this weeks episode of Catalog & Cocktails.

Key takeaways:

• [00:06 - 03:30] Intro & Cheers
• [03:33 - 04:54] What is your instrument of choice and what would your band name be?
• [04:59 - 07:55] Honest no BS definition of a data product
• [07:59 - 11:17] Alternative definitions of data as products
• [11:18 - 14:11] Data products can be many things, and definitions are broad
• [14:11 - 19:39] How human centered design interplays with defining data products
• [19:47 - 25:17] Data therapists, knowledge scientists and engineers
• [25:21 - 33:48] Where does the burden lay, and with whom
• [33:49 - 38:35] Brian's perspective on data product management versus software product management
• [38:38 - 43:59] How do you achieve good adoption?
• [44:05 - 46:39] What is the best way people can start learning about human-centered design?
• [46:42 - 47:20] Can dashboards be data products, and can machine learning be data products?
• [47:32 - 49:45] Should companies be investing in data managers
• [49:45 - 52:45] Value and adoption, should companies track data ROI
• [52:58 - 58:20] Takeaways
• [58:23 - 01:02:09] Three questions

Metadata management has been a topic for a while now. Lately, the industry is pushing that metadata is a knowledge graph problem. What does metadata in a pre and post graph world look like?

Join Juan Sequeda and Tim Gasper with special guest Mohammad Syed, Head of Data Architecture & Engineering at Capco to chat about metadata and knowledge graphs.

Key Takeaways:

• [00:06 - 02:40] Introduction & Cheers
• [02:43 - 03:55] What's your go-to karaoke song?
• [03:57 - 05:38] Metadata is a graph problem, yes or no?
• [05:55 - 08:23] What would we call metadata in a pre-graph world?
• [08:29 - 13:06] The biggest inflection point that has moved us collectively from a pre-graph world to a post-graph world
• [13:06 - 15:10] Use cases for metadata in finance
• [15:14 - 17:43] Different contexts in which you use data,
• [17:46 - 19:33] Metadata historically as a means of documentation
• [23:29 - 24:59] How we should be taking advantage of the graph structure
• [25:01 - 29:18] Applying basic graph techniques and algorithms to identify data use cases
• [28:11 - 30:30] The process of data governance and data protection
• [30:33 - 32:47] Outcomes of metadata graphs
• [33:04 - 35:19] Metadata management
• [35:26 - 39:59] How to get started with metadata mapping
• [40:05 - 43:30] Governance can't be living in an ivory tower
• [43:57 - 49:52] Lightning round
• [50:01 - 55:56] Takeaways
• [55:58 - 59:44] Three Questions

Entity resolution.Is it a single source of truth? Is it multiple sources of truth? Is it multiple data sources that refer to the same real-world thing?

How do we make sense of it? How can we harness the value of data through entity resolution? Talk to Jeff Jonas from Senzing on this week’s episode of Catalog & Cocktails and you’ll understand.

Key Takeaways

• [00:33 - 05:14] Episode intro & cheers
• [05:20 - 06:53] What is something you thought to be true in life that ended up being false?
• [06:58 - 08:15] Why don't we realize entity resolution if it is everywhere?
• [08:18 - 09:55] Tech stacks and tools for entity resolution
• [10:01 - 11:55] Record matching, link detection, fuzzy matching, and duplication
• [12:14 - 14:12] Master Data Management as the old school approach versus modern technology and its limits
• [14:18 - 18:38] Entity resolution is for the elite
• [18:48 - 21:11] Commoditizing entity resolution to the equivalent of a spell checker
• [21:14 - 25:41] Principles and Guidelines
• [25:51 - 28:48] The number of principles should fit on a single screen
• [28:50 - 33:03] Different levels of abstractions, and thinking about what kinds of things can solve similar problems
• [33:09 - 35:25] How does metadata tie into entity resolution?
• [35:32 - 40:45] Ontologies, semantics, and mapping
• [40:54 - 42:10] Thousands of transactions per second
• [42:14 - 46:23] You shouldn't have to tinker with any settings to be effective
• [46:28 - 47:44] ChatGPT
• [51:54 - 56:17] Takeaways
• [56:21 - 59:40] Three Final Questions

Responsible AI, what is it and who is responsible when it comes to AI? Mariam Halfhide, Responsible AI Expert at Xebia and former C&C guest, is here to discuss the answers with Tim and Juan on this week's episode. Enhance your listening experience with C&C Chat at data.world/podcasts

If there were a tombstone for this episode it would read “here lies Master Data Management,” But would you agree?

In this episode, join Tim, Juan, and special guest Malcolm Hawker from Profisee, will discuss the history of master data management, the buzzwords surrounding it, and where it’s headed, assuming it’s still alive.

Key Takeaways:

• [00:01 - 00:54] Introduction
• [02:31 - 04:55] Toast to sun on the beach
• [05:21 - 10:09] What's something in the world that is popular or trending that really needs to fade away and RIP?
• [10:36 - 13:33] How do we define MDM today?
• [16:43 - 19:52] How did we get here? Understanding the history of how MDM began
• [20:50 - 24:04] Governance: Where do we start? Approaches to figuring out how to decentralize
• [26:52 - 29:51] With the single version of the truth and for each context, how do we preserve the data integrity across independent or interconnected functions?
• [30:45 - 34:35] How data modeling and MDM apply to new capabilities such as data sharing and DBT
• [39:43 - 43:13] What does data ownership mean?
• [46:27 - 56:08] Lightning round
• [59:20 - 01:01:56] Advice, and important resources

Data modeling isn’t new. So why is it still a problem?

Maybe the problem isn’t data modeling itself, but rather there is no modern solution for companies, or the incentives are not well understood. It’s a learn-as-you-go type of thing, but that’s where the trouble lies.

Do you hire one data engineer and have them do everything? Do we encourage training in data modeling? OR… do we throw in the towel and keep doing things as they’ve always been done?

Join Tim, Juan, and special guest Anna Abramova from SqlDBM to answer these burning questions on this week’s episode of Catalog & Cocktails.

Key Takeaways

• [00:11 - 01:03] Introduction to Anna Abramova
• [01:03 - 04:03] Champagne vending machines & cheers all around
• [04:11 - 05:27] If you could model your home after one famous building or monument what would it be?
• [05:30 - 06:35] What's the deal with data modeling at SqlDBM
• [06:37 - 09:09] The origin story of SqlDBM
• [09:09 - 10:58] A modern approach to data modeling
• [10:58 - 12:04] What modern means in the context of data modeling
• [12:08 - 14:30] Why is data modeling now becoming such a hot topic?
• [14:33 - 17:24] Data modeling trends: startups and small businesses
• [17:25 - 20:41] Data modeling trends in medium-sized companies
• [20:43 - 21:32] Data modeling trends in large organizations
• [21:33 - 26:16] Summarizing data trends and real world applications
• [26:48 - 30:40] Metaphors around data modelings and architecting, problem solving
• [30:51 - 35:37] The biggest value triggers around data modeling
• [35:40 - 37:21] The pearl of the ocean
• [37:25 - 40:39] How are people learning data modeling?
• [40:42 - 43:17] A role or a skill?
• [43:18 - 45:10] Data modeling isn't the sexiest topic
• [45:12 - 46:16] A good foundation and resilience
• [46:26 - 49:39] Lightning round
• [49:55 - 55:19] Juan & Tim's Takeaways
• [55:37 - 58:51] Three questions about data and life

Imagine planting seeds and not watering them. Or putting water on the stove but not turning on the gas. Sounds silly, right?

Now take your organizations data and think about not leveraging it as an asset. It’s like taking the initial step of planting something in the garden and deciding not to provide nourishment.

Join Tim, Juan, and special guest Jane Urban from Takeda to understand the difference between a data asset and a data product and why companies still struggle finding the real value.

Key Takeaways

• [00:06 - 02:14] Introduction & Cheers
• [02:15 - 03:46] What New Year's resolutions have you stuck to so far?
• [03:49 - 05:14] Leveraging data as an asset
• [05:20 - 07:06] Definitions and examples of data as a product
• [07:13 - 09:42] Business needs and data value
• [09:43 - 11:49] The transition from something smaller to something growing, and lessons learned
• [11:50 - 14:58] Metaphors Jane uses to make communication around data value simple and relatable
• [15:00 - 16:42] A manufacturing process metaphor
• [16:46 - 20:46] Data stewards and the idea of finalizing a system versus ongoing system maintenance
• [20:46 - 24:35] Data value in the pharma perspective
• [24:45 - 27:02] Outcomes for data assets to data products, or are they unrelated?
• [27:05 - 28:57] Data as an asset on the balance sheet
• [29:01 - 32:30] How digital technology its in an organization, and potential as a revenue generator
• [32:46 - 38:08] Navigating how to be agile in pharma, which is not often agile
• [38:24 - 40:08] Find the astronauts
• [40:09 - 42:35] Leadership and executive evolution toward digital growth
• [42:45 - 47:01] The good, the bad, and the ugly of starting small and working towards global teams
• [47:15 - 55:47] Lightning round
• [55:51 - 01:01:16] Takeaways
• [01:01:24 - 01:04:08] Three questions

Those who don’t know their history are doomed to repeat it. If there is someone who can speak to data history, it’s Bill Inmon. How did data warehouses start? Why is the computing profession still immature?

Join Tim Gasper, Juan Sequeda and Bill Inmon, the father of the data warehouse and Founder of Forest Rim Technology, to learn about the past and present of data, and where things might be heading in the future.

Key Takeaways

• [00:05 - 01:00] Introduction to the Father of the data warehouse, Bill Inmon
• [01:11 - 02:28] Cocktails & Cheers
• [02:31 - 03:33] What actor would you cast for yourself?
• [03:39 - 06:47] Running in circles in the data world, a result of the profession's maturity
• [06:49 - 10:20] Bill's recent post about loyalty to technology versus loyalty to business
• [10:22 - 12:58] Specialists in computer technology, how programming has changed over time
• [13:06 - 15:08] Continuing education and certifications with programming paths
• [15:10 - 24:40] The history of computing: from army, to mafia, to your pocket
• [24:40 - 32:22] Key moments and milestones Bill has observed throughout his career in networking
• [32:28 - 33:59] Understanding the customer and looking after them
• [33:53 - 38:00] Microsoft Chatbot
• [38:00 - 41:04] A solution in search of a problem, showing business value
• [41:10 - 43:41] The Inmon and Kimball approaches
• [43:58 - 45:50] What's next for Bill?
• [46:11 - 58:46] Lightning round
• [58:53 - 01:04:16] Takeaways

Well, another incredible season of Catalog & Cocktails concludes this week with hosts Tim Gasper and Juan Sequeda.

Join in for the ultimate takeaways of the takeaways as Tim and Juan recap best moments, favorite hot takes, and the most controversial opinions over the last season.

Listeners, please submit your feedback: https://forms.gle/FdjMfarUaVnJ3SzB9

Key Takeaways

• [00:05 - 02:40] Introduction, end of year growth
• [02:43 - 04:54] Themes in culture, team structures, education opportunities
• [04:55 - 06:11] Training and Enablement
• [06:11 - 06:51] Shiny object syndrome, magpie syndrome
• [06:53 - 07:55] Loris Marini and culture, genuine conversations
• [07:56 - 09:09] Data problems with Laura Ellis, celebrating what people do with data
• [09:11 - 10:49] Culture, self-service, and shadow IT
• [10:54 - 11:09] Shared KPIs and alignment
• [11:12 - 12:40] Follow the money, and ask why you're working on what you're working on
• [12:40 - 14:29] Data projects don't fail for technical reasons, aligning business value, ROI is the key
• [14:35 - 15:09] How the business makes money, and where does it flow to organizationally
• [15:26 - 16:59] Who are the right people, and how do you continue to motivate and empower them
• [17:01 - 18:13] Ask for opinions and anecdotes, starting small
• [18:15 - 19:39] Loris on emphasizing connection and relationships, curriculum-driven development
• [19:40 - 21:35] Laura Ellis on data user experiences
• [21:39 - 23:19] The Chief Data Officer and managing data
• [23:24 - 26:17] What does real time streaming mean, and its impact on business, cost, reporting
• [26:45 - 29:59] AI and how it is already affecting data, knowledge, and more instant feedback
• [30:01 - 30:45] Where is AI funding coming from?
• [30:46 - 33:19] Horizontal and Vertical AI, value and use cases
• [33:19 - 34:00] Putting brain power toward ads and clicks instead of something like solving cancer
• [34:02 - 35:24] More education for people with diverse backgrounds, AI teams creating their own feature sets
• [35:27 - 37:16] "What should a catalog do?"
• [37:17 - 38:50] The spectrum of search, a lifecycle of data
• [38:52 - 40:19] Cross-functional collaboration and business expertise, strategies for metadata transformation and integration
• [40:21 - 41:32] Data modeling, cataloging, and semantics
• [41:34 - 43:29] Semantics with Dan Bennett, and data layers
• [43:32 - 45:58] Jumping into the pool with Allison Segraves
• [45:59 - 49:21] DGIQ, data governance, and closing out semantics
• [51:38 - 54:21] Predictions for the next year

Responsible AI, what is it and who is responsible when it comes to AI? Mariam Halfhide, Responsible AI Expert at Xebia and former C&C guest, is here to discuss the answers with Tim and Juan on this week's episode.

What better place to discuss the current state of data governance and what should the industry be focusing on than a live episode at the Data Governance and Information Quality (DGIQ) Conference.

Join Tim Gasper and Juan Sequeda LIVE from DGIQ in Washington, D.C. with special guests Anthony Algmin and Shannon Moore to have an honest no-bs discussion data governance and how it's all about people.

Key Takeaways

• [00:09 - 03:39] Introduction, Cheers & Drinks
• [03:40 - 04:38] Is Die Hard a Christmas movie or not?
• [04:38 - 06:06] The State of Data Governance in 2022, making people care
• [06:06 - 08:28] Making data governance about people and how they measure success
• [08:29 - 10:54] The biggest opportunities for getting people to care more about data governance
• [10:54 - 12:56] Data governance is a means to an end for corporations, a hotel analogy
• [13:02 - 15:03] Think like a marketer, and data as a product
• [15:04 - 17:18] Providing a value proposition for data governance
• [17:18 - 19:37] We need to talk to our marketing friends
• [19:38 - 21:08] Making data governance an ongoing business function, understanding the inherent value
• [21:12 - 23:33] Governance is still very project-based, and not often holistic as a function of business
• [23:41 - 26:20] Data privacy regulation to comply with
• [27:31 - 30:41] Education pathways in data governance
• [30:42 - 34:46] Strategies within organizations, business and leadership goals
• [34:59 - 37:50] The most important priority for governance in 2023
• [37:59 - 40:16] What vendors should be doing differently in 2023
• [40:21 - 45:43] Lightning round
• [45:54 - 50:17] Takeaways
• 50:18 - 53:40[] Three final questions

Think back to when you were first learning to swim. How’d you do it? Chances are, you weren’t thrown into the ocean being circled by sharks. We sure hope not, anyway.

You probably picked it up under the watch of a lifeguard in most cases.But once it became second nature, the lifeguard didn’t hold SO much power. Well, data engineers are the lifeguard, and if there isn’t a checks and balances system between them and the business teams, the engineers will be determining your every move in AND out of the water.

Join Catalog & Cocktails hosts Juan and Tim, with special guest Allison Sagraves as they discuss how you get in the water by yourself without drowning.

Key Takeaways:

• [00:06 - 04:15] Introduction & Cheers
• [04:18 - 05:31] Beach, lake, or swimming pool?
• [05:35 - 07:17] Entering the data pool without drowning
• [07:28 - 09:40] The pool is like the new sandbox
• [09:42 - 13:53] Cutting through confusion and BS
• [14:00 - 17:03] Data literacy and understanding that everyone needs to be trained on all things
• [17:09 - 18:10] A dog paddle towards more technical areas of focus during a transition
• [18:16 - 20:36] The idea of data products
• [20:37 - 23:52] Why Allison cannot stand the phrase "data is the new oil"
• [23:54 - 29:34] Building data products is not an easy thing to do
• [29:37 - 32:33] Culture and a digital mindset
• [32:38 - 36:25] Allison's experience as the CDO at M&T Bank for 30 years
• [37:28 - 41:40] The data pool analogy, and the lifeguard for that data
• [44:14 - 51:08] Lightning round
• [51:12 - 56:00] Takeaways
• [56:06 - 01:02:21] Three questions
• [01:02:28 - 01:03:27] Next week's episode

You have to have a lot of data to get AI to work. But the data folks are not jumping on it as fast as they should.

So what happens when data teams aren’t up to speed, companies are hiring more data scientists than they are engineers, AND current data teams are focusing too much on biz reporting and not supporting AI?

This week on Catalog & Cocktails, join hosts Tim Gasper and Juan Sequeda as they chat with special guest, Theresa Kushner, Head of North America Innovation Center at NTT Data Services to discuss how the AI train is leaving the station and data teams can only run so fast.

Key Takeaways

• [00:10 - 02:25] Introduction & Cheers
• [02:28 - 04:12] What's your favorite way to travel and why?
• [04:15 - 07:01] Are data teams keeping up with AI teams?
• [07:01 - 08:50] Are data teams and AI teams helping each other or avoiding each other?
• [08:54 - 13:09] AI teams become a data set in themselves
• [13:10 - 14:45] Data ownership and control
• [14:51 - 17:20] Thoughts on purchasing data
• [17:20 - 20:53] Data products and observations
• [20:53 - 24:52] CDO versus the CDAO, definitions and comparisons
• [24:53 - 27:05] Should there be a CDO or a CDAO?
• [27:03 - 30:04] Data makes AI work
• [30:02 - 34:58] If you want results you have to collaborate
• [34:59 - 37:29] Creating culture tied to data quality
• [37:27 - 42:01] The skill sets for managing data products
• [42:02 - 46:09] Theresa's message of advice to data teams
• [46:12 - 52:36] Lightning Round
• [52:36 - 58:16] Takeaways
• [58:17 - 01:00:31] Three questions

Machines and people. Why can't we just speak the same language? The truth is we can, and doing so could make life demonstrably better for data scientists. Yet here we are, living in a world of rows and columns that few people outside of the data owner understand.

Join this weeks episode of Catalog & Cocktails as hosts, Juan Sequeda and Tim Gasper with special guest, Dan Bennett, tackle semantics and how to get everyone -- machines and people -- on the same page.

Key Takeaways:

• [00:01 - 02:47] Intro & Cheers
• [02:49 - 04:57] If you were the picture for a word in the dictionary, which word would it be?
• [04:58 - 08:35] The Greatest Sin of Tabular Data
• [08:40 - 11:02] Examples of semantics missing inside of tabular data and their utility
• [11:03 - 12:39] Adding context and profiling data
• [12:43 - 14:55] How are constraints and semantics being defined, and what is a scaleable approach?
• [15:02 - 16:24] Data producers and enriching data
• [16:30 - 17:58] Enrichment that travels with the data
• [18:00 - 20:14] What are the tools we use, the data dictionary, and standardizing
• [20:16 - 22:34] Metadata and the bridge to the semantic world
• [22:36 - 24:57] Innovation and Dan's thoughts on relational model table relationships
• [24:57 - 28:09] Solving the same problems over and over again
• [28:12 - 30:19] Network effect, the marketplace of ideas and social spheres
• [30:21 - 33:56] Diving into the network effect and the semantic world
• [33:58 - 36:48] Why redefine if an option exists that can be used, and thoughts on simple ideas being the best solutions
• [36:52 - 40:11] Figuring out supply and demand curves for S&P Global
• [40:12 - 44:34] The business value of data and data literacy in accurate findings
• [44:44 - 46:19] Advice to data leaders and vendors
• [46:37 - 50:14] Lightning Round
• [50:29 - 56:48] Takeaways
• [56:52 - 59:44] Three final questions

Data and business teams become a convoluted intersection, and when they struggle to communicate, it leads to bigger problems than awkward water-cooler talk.

So what comes first? Translation? Data literacy? Company culture? The chicken? The egg?

Co-founders of Preql, Gabi Steele and Leah Weiss, join hosts Tim and Juan to discuss how to put the business in charge of their own data and how this leads to the answers AND massive alignment between data and biz teams.

Key Takeaways

• [00:05 - 04:15] Introduction and Cheers to Tim
• [04:17 - 06:50] What is the wildest or weirdest thing you've seen while stopped at a red light?
• [06:55 - 10:29] Putting businesses in charge of their own data
• [10:29 - 12:04] Inviting the business into the process and building community
• [12:05 - 15:21] Developing a curriculum for data, teaching SQL and data visualization
• [15:22 - 19:17] Refining the model and curriculum, tailoring the fit
• [19:17 - 21:41] Identifying the people who wanted to be part of data modeling
• [21:43 - 22:55] Teaching interested parties another way to handle data, using an application process to find people
• [22:57 - 24:17] Stewardship in handling data
• [24:17 - 27:04] The process of engaging and completing data modeling: a brain for architecture, python, and sql
• [27:05 - 28:32] Interesting problems to solve, and you have to be creative to get there
• [28:36 - 30:40] Understanding technical debt and how to support engineering teams, skills for good data modeling stewardship
• [30:42 - 33:18] DBT and building robust analytics teams
• [33:30 - 37:35] Tooling solutions for helping business be in charge of their own data
• [37:42 - 38:52] Focus on the technical side, and a no-code semantic layer
• [38:53 - 42:31] Bringing in a technical analytics engineer to the business team
• [42:31 - 45:33] The knowledge gap and bridging it with a framework
• [45:33 - 49:39] Alignment and building the semantic layer
• [49:43 - 56:36] Lightning Round
• [56:38 - 01:01:30] Takeaways
• [01:01:32 - 01:05:50] Three Questions

You can’t buy a predictive model off the shelf. And if you COULD… would you? It’s the old “build versus buy” debate and with data in a constant state of change, building and deploying AI is more challenging than ever.

Join Tim, Juan, and Andrew Eye, CEO at ClosedLoop.ai, to discuss the challenges of AI deployment, AIOps, and maintaining models as data changes.

Key Takeaways

• [00:08 - 01:45] Cheers for AI
• [01:48 - 04:44] Taking advantage of incoming data and considering the data footprint
• [04:45 - 05:55] Building the most accurate and useful model, no best solution
• [06:01 - 09:26] AI strategy and developing prediction models
• [09:33 - 15:17] Should you build or should you buy?
• [15:21 - 20:02] Verticalized AI versus Horizontal data science and AI-oriented solutions
• [20:04 - 23:16] Build a custom predictive model
• [23:42 - 25:24] The concept of "feature drift" in AI
• [25:26 - 30:02] Vertical AI and the opportunity with healthcare
• [30:07 - 33:52] The concept of a click and the idea of semantic contracts
• [33:53 - 38:08] Great minds designing AI for Facebook instead of healthcare
• [38:12 - 39:41] Designing AI for good is addictive
• [39:47 - 41:27] The next generation and the promise of AI that can really impact lives
• [41:41 - 47:57] Lightning Round
• [48:02 - 52:03] Takeaways
• [52:05 - 53:24] Three questions

Amalia Child, Data Manager and Librarian, joins us to explore how librarians have evolved in the digital age—trading shelves of books for data repositories. We’ll discuss her post “The Five Laws of Data Enablement: How the father of library science would make his data team indispensable” and why library science is essential in data organizations.

The most effective way to understand your business is the special balance between things like tribal knowledge and extracted knowledge, technical teams and business teams, confidence and skepticism.

Join Tim, Juan, and Loris Marini, CEO of Discovering Data, as they zoom in to find that balance and the humility it may take.

Key Takeaways

• [00:11 - 03:10] Introduction & Toasts
• [03:13 - 05:32] What's the weirdest place you've ever misplaced your keys?
• [05:44 - 08:01] The most effective way to follow the business is to follow the money
• [08:02 - 10:35] Thinking about business literacy and how to learn that perspective
• [10:37 - 12:35] How do we make change happen?
• [12:37 - 13:39] Having genuine conversations with people
• [13:44 - 18:19] Coaching, psychological safety, and communicating better at work
• [18:20 - 22:10] The definition of creating knowledge within an organization
• [22:11 - 25:34] Knowledge, insight, and levels of knowledge distributed in your team
• [25:35 - 28:52] You've got to spend time and you have to have a platform
• [29:10 - 31:48] We can do better than "just the smartest will survive"
• [31:57 - 35:56] Incentives are about subtle things like a team that listens, not just pay and flexible time
• [35:59 - 39:35] A data therapist and becoming more aware of issues
• [39:43 - 41:32] Advice to engineers who write the data pipeline, engineer the SQL queries
• [42:06 - 45:24] Lightning Round
• [45:28 - 51:10] Takeaways
• [52:06 - 53:57] Three questions

Data needs to be in the hands of the people who really need it. Sounds simple, right? So where is the disconnect? If your teams aren’t partnering to get projects done, it’s often due to a lack of understanding of each others pain points. That’s where empathy is a must.

Join Tim, Juan and special guest Laura Ellis from Rapid7 as they discuss supporting all the cooks in the kitchen when it comes to data projects, why that’s a hill to die on and HOW to actually get that done.

Key Takeaways:

• [00:06 - 02:09] Intros & Toasts
• [02:09 - 03:49] What is the most controversial hill you'll die on?
• [03:52 - 05:30] Getting data into the hands of people who need it
• [05:30 - 07:01] Biggest problems for collaborating and making data easier to work with across the organization
• [07:05 - 09:32] Take a business problem, break it into a data problem
• [09:32 - 11:00] Breaking down problems, making it less intimidating
• [11:00 - 12:34] A user experience problem around data
• [12:30 - 13:52] Who is responsible for figuring out how the pieces of the puzzle fit together
• [13:55 - 17:22] Understanding centralization, decentralization, and embracing ownership
• [17:25 - 20:48] Identifying issues and opportunities research provides for enabling data access
• [20:48 - 22:31] Internal user research and Rapid7's "data therapist"
• [22:31 - 24:33] Business literacy and data literacy, cross-functional learning
• [24:28 - 26:39] More technology and more tools
• [26:37 - 33:14] Putting data into the hands of people who need it, making money and saving money
• [33:20 - 36:36] Gathering community and team content from passionate people
• [36:34 - 38:24] Brainstorming definitions of success
• [38:24 - 41:31] Start talking to people, do surveys, understanding the monetary impact
• [41:39 - 43:31] Dive into the details
• [43:36 - 47:23] Lightning Round
• [47:27 - 54:09] Takeaways
• [54:10 - 57:57] Three questions

AI and ML are both at the center of many applications today from autonomous vehicles to healthcare. But where is this ship heading?

Join Tim, Juan, and Patrick Bangert, VP of AI at Samsung SDS where they will discuss where the focus of AI is today and where it should be tomorrow.

Key Takeaways

• [00:04 - 01:06] Episode introduction/trailer
• [01:18 - 02:22] Drinks & Toasts
• [02:23 - 04:40] What is the most unfocused situation you've ever been in?
• [04:49 - 10:08] Where is the focus of AI today? Business application.
• [10:09 - 13:10] The technical side of AI, thinking about AI behind the wheel
• [13:43 - 15:15] The problems outside autonomous driving that AI can solve
• [15:16 - 20:09] AI and the impact on healthcare
• [20:15 - 21:35] AI will allow doctors to become better caregivers
• [21:45 - 27:08] Open issues and gaps in technology with AI currently
• [27:13 - 30:41] Keeping data clean, and identifying bias and context
• [30:46 - 36:18] Effective strategies companies are taking to label data better
• [36:37 - 38:56] Using AI to help build AI, "AI squared"
• [38:58 - 43:25] Inserting knowledge and representation, a solvable problem with better data
• [43:25 - 46:23] Chat bots, autonomous driving, and complexity of tasks
• [46:43 - 48:36] Doug Lennick, lessons from the 80s, and the state of GPT-3
• [48:47 - 52:16] The role of venture capital in AI development
• [52:31 - 57:34] Lightning round
• [57:40 - 01:05:58] Takeaways and the current state of AI
• [01:06:06 - 01:08:03] Three questions

You have been asked to start the Data Governance program in your organization. Sounds easy, right? How do you start? How do you define success? Who needs to be part of the team?

Join Tim, Juan and Rupal Sumaria, Head of Governance of Penguin Random House UK to discuss the steps for a successful data governance program and what to avoid.

Key Takeaways

• [00:06 - 03:16] Introduction and Toasts
• [03:17 - 04:51] If you could only keep three apps on your phone, what would they be?
• [04:52 - 07:33] Struggles in setting up a data governance program
• [07:40 - 09:42] Rupal's presentation at the data.world summit
• [09:46 - 13:03] Networking and communicating early while creating the data governance program
• [13:07 - 15:06] Who Rupal was collaborating with while setting up the data governance program
• [15:06 - 17:34] When was the last time you spoke to sales and marketing?
• [17:34 - 19:32] A tailored approach to reaching other departments
• [19:32 - 22:56] Tips for when you're struggling with engagement
• [22:56 - 26:42] Change the game, don't make it boring
• [26:48 - 32:14] Companies have to measure ROI
• [34:14 - 36:54] A four step process to data governance
• [36:54 - 39:10] Where to start, departmentally
• [39:13 - 40:28] Advice for those "stuck" in data governance roles
• [40:28 - 41:34] Is there anything Rupal would have done differently, two years down the line?
• [41:51 - 42:58] Data governance technology
• [43:24 - 45:41] Rupals tip's for navigating processing technology tools
• [45:43 - 48:59] Lightning round
• [49:02 - 56:51] Takeaways
• [57:03 - 59:25] Three questions

Amalia Child, Data Manager and Librarian, joins us to explore how librarians have evolved in the digital age—trading shelves of books for data repositories. We’ll discuss her post “The Five Laws of Data Enablement: How the father of library science would make his data team indispensable” and why library science is essential in data organizations.

Answering critical business questions relies on integrating data from a variety of systems. But it takes a lot of work to understand what the disparate data means and how it all fits together. How do we make data as reliable as an electricity?

Join Tim Gasper, Juan Sequeda and Fraser Harris, VP of Product at Fivetran, as they celebrate the 100th live episode of Catalog & Cocktails and discuss how #metadata, #datacatalogs, and #dataintegration act as the power source for your connected enterprise

Key Takeaways:

• [02:02 - 03:49] Cheers to 100th episode, good health, children, and sky miles
• [04:10 - 05:11] Keeping it 100, Millennial and Gen Z slang
• [05:12 - 07:05] What metadata means to Fraser, the data about the data
• [07:07 - 10:00] Fivetran's new metadata API
• [11:32 - 13:33] Action, enforcement, and results in understanding data management
• [13:52 - 17:51] Data contracts and the interface
• [17:53 - 20:07] Upstream notifications and transforming data
• [20:55 - 24:01] Perspectives on having a system and record owner for data contracts
• [24:31 - 30:38] Representing business process change in contract evolutions
• [30:40 - 31:58] Cultures around data at newer companies
• [32:15 - 34:34] The two main use cases of Fivetran's data and the impact analysis
• [34:34 - 36:25] Two dimensions to data proactivity, data maturity and company size
• [36:26 - 41:26] Steering data complexity to simplicity, business value behavior and technology costs
• [41:28 - 43:47] Reliability and data pipeline
• [43:52 - 45:10] What Fraser wants to see happen around metadata
• [45:10 - 47:15] The process of migrating to the cloud and adopting new data policies
• [48:47 - 55:25] Lightning round
• [55:28 - 01:00:43] Tim & Juan's takeaways
• [01:00:57 - 01:02:49] Three questions for Fraser
• [01:04:43 - 01:05:14] Next week's guest, Rupal Sumaria from Penguin Random House

Data Catalogs are at the center of every enterprise’s data strategy. It’s important to explore the current state and how data catalogs are evolving. Who better to talk about Data Catalog than the author of the upcoming O’Reilly book “The Enterprise Data Catalog”, Ole Olesen-Bagneux from GN.

Join Tim, Juan, Ole to discuss the future of data catalogs and why knowledge is at the center.

Conversation highlights:

• [00:07 - 03:06] Intro & Drinks
• [03:10 - 05:11] Warm up, if data catalog were a cocktail what would it be
• [05:19 - 07:06] Where the hell are data catalogs going
• [07:07 - 09:01] What the library and information sciences world is bringing to data catalogs
• [09:03 - 10:43] Don't think that catalogs should move away from a Google Paradigm
• [10:43 - 12:14] Finding more valuable data sets, and Boolean queries
• [12:16 - 16:23] Accessing what is inside a catalog and thinking about metadata
• [16:27 - 23:36] The "shopping experience" and expressive ways of searching
• [23:44 - 26:55] How cataloging intersects and impacts search and metadata
• [26:55 - 29:00] Catalogs will evolve into repositories and a thesaurus
• [29:01 - 32:05] The lifecycles of data and system life cycles
• [32:24 - 35:19] The POSMAD Framework
• [35:26 - 38:56] Data monetization and modern data architecture
• [38:57 - 42:11] The potential for POSMAD and practical relation to data catalogs
• [42:16 - 43:15] Here's the data, what are you going to do with it?
• [43:19 - 44:30] Data to Information to knowledge to wisdom
• [44:40 - 47:06] Cataloging ontologies and knowledge layers
• [47:07 - 50:00] Knowledge graphs and combining with simple search and browse features
• [50:18 - 56:34] Lightning round
• [56:35 - 01:02:12] Tim and Juan talk takeaways
• [01:02:17 - 01:05:55] Three questions about data, life, and the show's next guest