Download your copy of our latest research mentioned in this podcast here: Quantum Computing in Financial Services (coriniumintelligence.com) The speed of development of quantum technology is growing exponentially. And while the technology is in its infancy, it’s time for financial services firms to start paying attention to the opportunities and the risks it presents. In this conversation, Sergio Gago Huerta, Quantum Computing Lead at Moody’s Analytics discusses these issues considering a recent research report with 200 data analytics and innovation experts on how quantum research is already impacting the industry.

In this discussion:  

·        The evolution of quantum technology and where we are today

·        Key takeaways from our latest research on quantum computing in the financial services industry   

·        The near-term impacts of quantum computing

·        How financial services firms should be thinking about

In this week’s episode of the Business of Data podcast, Natwar Mall, the Chief Technology Officer at Fractal discusses the impact of ChatGPT on large enterprises and how he predicts it will transform the nature of data and analytics.

In this week's episode of the Business of Data podcast, host Catherine King talks with Gurpreet Muctor, Chief Architect & Data Officer, Smart Cities for Westminster City Council. Together they discuss the benefits of creating and maintaining a flowing data ecosystem.

In the discussion this week:

• Strategic roadmaps to achieve goals
• Data capabilities surrounding internal requirements
• Public sector challenges of attracting talent
• Complex data sources
• Exploring opportunities for data, they won't all be winners

Pranav Kapoor, Global Head of Decision Analytics Audit Innovation at Manulife, discusses how he’s evolving the insurance firm’s audit function to support continuous auditing and advanced analytics

Automation promises to revolutionize the internal auditing process by enabling teams to continually gather from process data that supports auditing activities.

As Manulife Global Head of Decision Analytics Audit Innovation Pranav Kapoor notes, this will enable auditors to provide their businesses with more regular assurance about risk management, governance and their internal control processes.

In this week’s episode of the Business of Data podcast, he talks about the work his team is doing to make this vision of the future a reality.

“The biggest opportunity we believe is to provide continuous assurance to the business,” he says. “If you can use automation to run these audits pretty much when you desire, or even in real-time, I think that’s the piece where continuous auditing processes become very interesting.”

“You can really see a high demand in internal audit teams to push in that direction,” he adds. “Everyone in the business sees the value around it.”

Pranav Kapoor, Global Head of Decision Analytics Audit Innovation, Manulife“We need to drive the innovation culture and embed digital skills and knowledge into all our auditors, and not just a small team that will be aware of these skills”

As a business function, internal audit (IA) is evolving rapidly. Companies including Manulife are looking at how IA can stop focusing purely on risk discovery and start using automation and analytics to drive innovation.

“We want to the be the innovative function in the audit group,” says Kapoor. “In my utopia, the auditors will have analytics skills and the data analytics group, which is my group, will become the innovation function.”

To achieve this, Kapoor has been working to ensure Manulife’s auditors have a common definition of what analytics is and educate them about the power of analytics to improve their productivity.

Of course, educating staff about the benefits of automation and securing buy-in for analytics projects is the first step in a much larger journey. Kapoor sees these efforts as a starting point for the more ambitious goal of enabling continuous auditing and assurance in the long-term.

Key Takeaways

• Automation is the future of IA. Continuous auditing will allow IA teams to provide the business with audit assurance more regularly
• Data literacy is key. Data-focused leaders must equip non-data staff with the right skills to drive business transformation
• Quick wins come first. Delivering smaller projects that make auditors’ lives easier is helping Kapoor secure buy-in for larger initiatives

Maria Tarasidou, Global Data Program Manager at Facebook, argues that legacy companies should follow the example of big tech to succeed with data-driven business transformation

Enterprises are increasingly open to investing in new data-driven technologies that are shaping the future of business. But as Facebook Global Data Program Manager Maria Tarasidou argues in this week’s Business of Data podcast, technology doesn’t drive business transformation by itself.

“You have to also be prepared to bring in the right people with the right mindset,” she says. “Everyone needs to understand data. Everybody needs to use it. Everybody needs to be able to go back and retrieve and extract the data they that need in the way that they need it and visualize it.”

In recent years, many companies established hubs that are separate from their legacy business to kickstart the data strategies or innovation projects. While this can make sense in the short-term, Tarasidou notes that data-driven ways of working must become embedded across an entire organization before meaningful transformations can occur.

“What happens in big tech companies is that there’s no role that is actually a Data Analyst role,” she says. “Everyone is an analyst.”

This chimes with the stories we hear from our wider data and analytics community. It’s those companies that invest in data literacy and integrate data-driven ways of working into the roles of staff across the business which get the most value out of data and analytics.

Maria Tarasidou, Global Data Program Manager, Facebook“If we say, ‘In 10 years do you expect for the current Data Analyst role to exist?’ I would say, ‘No’”

Tarasidou predicts that integrating data with business processes in this way will become so widespread within a decade that Data Analyst roles as we know them will cease to exist.

“If you want to force it, you bring in the right people and the right talent and you educate the business accordingly,” she suggests. “But it’s going to happen. It’s where we’re heading. This is the age of information.”

Enterprises that want to make the most of futuristic technologies such as the ‘data mesh’ must ensure their staff are committed to upskilling and changing how they work to drive successful data-driven business transformations.

Key Takeaways

• Technology by itself is not enough. Executives that focus their investments on shiny new tools will not succeed in driving meaningful business transformations
• Data literacy fuels digital transformation. Enterprises should focus on empowering staff to work with data efficiently to accelerate their data-driven business transformations
• Data skills are the future of business. The Data Analyst role could one day be phased out as data analysis skills become an integral part of all jobs in the workforce of the future

Carlos Rivero, Chief Data Officer of the Commonwealth of Virginia discusses how his team built a better data governance framework to help address the State’s opioid epidemic  

Drug overdose deaths in the United States have accelerated during the COVID-19 pandemic, according to the CDC. Synthetic opioids are driving this increase, nearly 40% more opioid-related deaths were reported year-on-year in May 2020.

In this week’s episode of the Business of Data Podcast, Carlos Rivero, Chief Data Officer of the Commonwealth of Virginia discusses how improved data sharing and governance has helped the State’s worsening opioid epidemic.  

“When you think of the opioid problem, it isn't one-dimensional. It isn't just a law enforcement problem, it isn't just a health science problem, it isn't just a community problem. It's an overall problem that has multiple facets to it,” says Rivero. “So, being able to connect with a council that has multiple representatives from each of these different industries participating in it, one of the biggest concerns was how do we share data?”

Creating a Data Governance Framework

Rivero is responsible for 63 executive branch agencies and 133 localities in the State. A top priority when he joined the agency in 2018 was building a data governance framework to make data sharing easier.

Rivero’s first task was to establish communication between stakeholders at all levels in the data management cycle to address complex multidisciplinary issues that one agency cannot address alone.

“The number one [priority] was to establish a governance framework that allowed people to participate in the discussion of how we best leverage our data assets,” he says. 

After that, Rivero focused on improving data discoverability and creating a data trust model that could be implemented across the State.

“The Commonwealth data trust is all about [creating a] legal framework that facilitates confidence and trust in our ability to manage these restricted use sensitive data assets,” Rivero explains.

How Data Use Evolved to Address Statewide Health Problems

One of Rivero’s biggest successes in the Commonwealth is a substance use disorder project focused on addiction analysis and community transformation.

Starting in Winchester, Virginia, a small community in the Northwest of the State, Rivero’s team implemented a pilot program that aimed to demonstrate the efficacy of data to address the region’s opioid problem.

“We were looking at that [community] as a microcosm for what happens in the larger scale across the Commonwealth with regards to data sharing, but then deriving intelligence from the data assets that are being collected from a wide variety of different organizations,” says Rivero.

Ultimately, the success of the project in facilitating data sharing and making intelligence available has seen it rolled out across four other regions of the commonwealth. Not only that, but the systems that Rivero’s team built were also implemented into the State’s pandemic response.

“We took all of that and implemented it for the COVID 19 pandemic response,” Rivero concludes. “So, what you're seeing is a very fast evolution of the data, trust, the governance framework, the technology platforms, and all of the components that go together to make data sharing analytics and intelligence possible.”

Key Takeaways

• Increasing communication amongst stakeholders is key. Implementing a data governance framework requires efficient cross-team communication

• Creating a data trust increases confidence in data. The legal framework of a data trust increases confidence around the use of sensitive data

• Apply your experience to new problems. Governance frameworks and technology platforms can be used to address new challenges

Matt Lovell, Former Data, Analytics & Insight Director at Eurostar explains how automation transformed their customer experience in the wake of the pandemic

On March 13, 2020, after two years of hard work, Eurostar replaced its 50-year-old ticketing system with a modern, data-driven platform.

On March 15, 2020, COVID-19 caused Eurostar’s passenger numbers to crash.

In this week’s episode of the Business of Data Podcast, Matt Lovell, former Data, Analytics, and Insight Director at Eurostar, explains why he reprioritized his data projects to improve customer experiences as pandemic disruption hit.

“At the moment all of the projects that we would normally work on are largely on hold. So, it does give you the options to do a bit of a reset, whether it’s adding rigorous processes, fixing systems, or restructuring data in a way that we want it,” he says. “These are things that normally wouldn’t get looked at.”

Reacting to Customer Demand in Real-Time

As lockdowns began, Eurostar customers needed a way to easily reschedule or cancel their journeys. Unfortunately, their voucher-based compensation system was not designed to deal with a pandemic.

“That created a whole new management scenario that we hadn’t necessarily planned for,” Lovell says. “There were a lot of things we had to systematically work through.”

The first job, he explains, was quick to take stock of the situation and prioritize key projects. Then, the team rapidly iterated on system modifications and introduced automation designed to improve customer experiences.

“We started to [ask] how we could gradually move to a point whereas much of this was automated as possible and as much of this was visible to the customer as possible.”

Automating key parts of the process helped Lowell to implement a convenient system for customers to switch tickets and claim refunds online. It also proved the value of automation to the business.

“The resource that was needed for us to do it manually at the beginning was so substantial,” he says. “[Now] we can build this in a way where it barely has any of that.”

“Not only is that reducing the stress on the business but it’s also improving the customer experience, so it’s really a win-win,” he concludes.

Key Takeaways

• Use ‘downtime’ to reevaluate data priorities. If your regular projects are on hold, take the opportunity to take a fresh look at your priorities
• Iterate for success. Even if a system is not perfect immediately, by iterating over time you can make incremental improvements
• Automation can create a win-win. By making systems more efficient, data leaders can improve customer experiences and prove business value at the same time.

Thanassis Thomopoulos, Head of Global Marketing and Commercial Analytics at eBay Classifieds Group, outlines how Apple’s ‘transparency framework’ and the looming death of cookies will affect his teams’ approach personalization

Data privacy regulations have been ratcheting gradually up globally since the EU’s General Data Protection Regulation (GDPR) came into effect two years ago. As we move into 2021, two looming developments will transform the way companies provide personalized customer experiences.

In this week’s episode of the Business of Data podcast, eBay Classifieds Group Head of Global Marketing and Commercial Analytics Thanassis Thomopoulos outlines what they are and how his company is preparing for them.

“It’s becoming more and more difficult to recognize people online,” he says. “What this has in terms of a second wave impact is, if you can’t recognize people online, then you will have more challenges in providing personalized experiences and also being able to measure whatever you’re doing online.”

Why eBay Classifieds Group is Preparing for a Cookie-Free World

After some initial disruption, European businesses have largely mastered the art of GDPR compliance. However, legislators are now moving to address the widely hated ‘cookie walls’ that have popped up on many websites as an unintended consequence of the regulations.

“A few months from now, the world will be cookie-less,” Thomopoulos predicts. “That’s very different form what we knew.”

Today, cookies are the main way companies including eBay Classifieds Group recognize people across websites to pass information between websites and provide joined-up experiences.

Thomopoulos warns: “This is something that’s going to be disappearing and, frankly, not everyone has all the answers as to how we’re going to be able to function after that.”

Customer Trust is Essential to the Future of Personalization

A second challenge Thomopoulos highlights is specific to the ‘transparency framework’ outlined in Apple’s iOS 14.

“In their own way, they will give a very obvious and vocal choice to the user on whether they are willing to share their identifier for advertising,” Thomopoulos says.

“We’ve been preparing for this at eBay Classifieds Group and we’ve run a few tests,” he adds. “What we can see is, there’s a sizeable chunk of people who will decline their consent.”

Companies will likely deliver campaigns to communicate the benefits of personalization to customers in response to this new challenge. But eBay Classifieds Group will also be focusing its efforts on getting more users to create and log into profiles on its website.

“To do that, you need to build trust,” Thomopoulos notes. “If I’m a shady website or a website that is well-known for, let’s say, having subpar practices around their information sharing, then I would be very reluctant to do that.”

He concludes: “If it’s a business that I trust – that I love – then I would be totally OK with giving some of my data to in exchange for a better experience. I will do this very gladly.”

Key Takeaways

Prepare for a cookie-free world. European companies should be planning for a world without advertising or cross-site cookies

Adapt to Apple’s transparency framework. Consider focusing on getting users to create customer accounts to enable personalization

Consumer trust is more important than ever. Changing attitudes around data privacy mean companies must work hard to earn their customers’ trust

Anne Merel Oosterbroek, Head of Data & Analytics, Financial Restructuring and Recovery at ABN AMRO Bank NV shares her tips on creating data literacy campaigns for senior executives

Successful digital transformations require careful planning and significant investments of time and money.

Demystifying data and analytics for senior leadership is an essential part of winning that financial investment, argues Anne Merel Oosterbroek, Head of Data and Analytics, Financial Restructuring and Recovery for ABN AMRO Bank NV in this week’s episode of the Business of Data Podcast.

Data teams steeped in data and analytics know their power and potential. However, educating senior leaders who may be less aware of the benefits is an important step toward successful digital transformations.

“Start with the top,” she recommends. “It’s very important to create those believers, not only at the lower levels of an organization but [also] at the top. If you have a few believers, [then] it is a lot easier to have those investments approved.”

By creating a data literacy campaign specifically for senior executives, data and analytics leaders can demonstrate how digital transformation will help their businesses to achieve their goals.

“We shouldn’t assume that our leadership team understands what we can do with data and analytics,” she says. “We should really start by answering the very basic questions. As soon as we’ve done that, we can then explain what is possible, so they actually get enthusiastic.”

Communicating data and analytics success stories is also an important part of winning hearts and minds across the business, Oosterbroek says.

“Invest your time in understanding the possibilities of data and analytics, and don’t be shy to make your colleagues [feel] enthusiastic about this fantastic world of data analytics,” she concludes.

Key Takeaways

• Don’t assume that senior executives understand the potential of data and analytics. Start with the basics and build toward more complex topics from there
• Educate senior leadership on the benefits to secure buy-in. Their support is essential to achieve successful digital transformations
• Become a data and analytics evangelist. Establish data and analytics literacy programs and prioritize education across business units to get people excited about the future

Dan Marzouk, Senior Vice President of Data Science at Aegis, explains how data science is shaping their approach to insurance

Wildfires are difficult to predict, grow rapidly and have the potential to cause damage worth tens of billions of US dollars.

This is a problem for insurers trying to price risk. The solution? Using data to develop a more complete understanding of risk, argues Aegis Insurance Senior Vice President of Data Science Dan Marzouk in this week’s episode of the Business of Data Podcast.

When evaluating, for example, the chances of a wildfire affecting a suburban home, there are a wide range of data points to consider and a variety of data sources to include. However, not all sources are of equal quality.

“The challenges are similar to comparing a Google review, a Yelp review and a Facebook review for a business. Each of those [reviews] have their pros and cons,” Marzouk notes. “Each of our data sources also have their pros and cons.”

The differing quality of data sources can lead to discrepancies in the data. That’s where data science comes in. Creating a consistent risk assessment requires building a model that quantifies the accuracy of input data.

“Over time we start to learn and utilize what we think is accurate from one dataset and continue on that path to build our own data integration system that understands what we believe to be the most accurate system,” says Marzouk.

Of course, weighing tens of thousands of data points takes time. However, as Marzouk explains, in the age of instant everything it is crucial to provide insights to decision-makers quickly.

“To do that, we have to both understand how to aggregate that data quickly and cull out what’s not as important or useful,” he says. “And be able to develop something that the underwriter can make a decision on quickly.”

Ultimately, to meet the business need the data must help to create a product that is appealing to the customer. That means that data scientists must also maintain a commercial awareness.

“Customers don’t buy things because you told them that the model says [they’re] going to buy it,” Marzouk quips. “That’s my advice to the data science community. Take a step back and say, ‘I know the data’s telling me this, but does it make sense?’”

Key Takeaways

• Understand the data you have. Is it granular enough? How reliable is the source? The answers should inform your model.
• Maximize your data points. Innovative technologies like image recognition can dramatically increase the number of data points available
• Take a step back. Remember to evaluate what the data is telling you in the light of all other available information

Rapid advances in data-driven technology and a precipitous rise in catastrophic flood events in the US presented an opportunity for this InsureTech startup

There are 62 Million homes at moderate or extreme risk of flooding in the US, according to insurance risk assessment firm Verisk.

Homeowners insurance does not typically cover flood damage and up to 50% of homes in high-risk areas have no flood insurance at all. This amounts to a serious problem, argues the founder of InsureTech startup Neptune Flood Insurance Jim Albert in this week’s episode of the Business of Data Podcast.

In the past, most flood insurance in the US was provided by the National Flood Insurance Program (NFIP). Now, powered by innovative technologies, nimble insurgent companies are shaking up the status quo.

“The NFIP has done an exceptional job over the years, but as with most government programs, technology has started to outstrip what has happened within the flood space,” says Albert. “And so, what I tried to create with Neptune when I founded it in 2016 was an ‘Amazon-like’ buying experience in flood insurance.”

“You can get one-click buying for virtually everything else you do in life,” he continues. “So, we tried to make it easy to buy flood insurance in the US through the use of data analytics and a really simple online quoting platform.”

The game-changing, automated approach championed by Neptune Flood Insurance was not without its skeptics. In 2016 when the company was founded, the idea of digital insurance was even more revolutionary than it is today.

“There was a lot of skepticism about digital insurance [back then]. Could a digital model actually replace the traditional back room full of underwriters?” Albert recalls. “[Especially] when I explained that we don’t have any underwriters. In fact, the underwriter is the computer.”

What sets Neptune Flood Insurance apart from its competition is the speed that customers can get a quote and buy their flood insurance online.

We’ve proved in the model at this point,” Albert says. “We pull in about a hundred different data elements in one second when you enter the address, and we do the full evaluation right then and there.

The application of this technology could not be timelier. Not only are flood events likely to occur more often in the US, but due to the pandemic no-one wants to have an inspector in their home, nor to wait weeks for an estimate.

Do [customers] want to sign on to a days or weeks-long slog to finally get the information that they need?” Albert concludes. “Or [do they] want to go to one site that has seemingly all the information with a really good price and great coverage options? That’s what we see happening.”

Key Takeaways

• Many homes at high risk of flooding in the US are uninsured. A lack of awareness of the risks is one cause, but catastrophic damage can take years to recover from
• Data has paved the way for a better solution.  By pulling together data from a multitude of sources, Neptune Flood Insurance can provide a policy in seconds
• Hyper-personalization is on the way. Other types of insurance companies will soon take advantage of advanced, data-driven technology to provide highly personalized policies to their customers

The best way for companies to provide premium experiences to their customers is a cloud-enabled platform, argues USAA Assistant Vice President and Head of Information Management for P&C Allen Crane in this week’s podcast

USAA Assistant Vice President and Head of Information Management for P&C Allen Crane has a simple message for those companies yet to begin their cloud transformation journey. Start now.

The USAA built their cloud infrastructure from the ground up to provide services with the ‘wow’ factor, as Crane explains in this week’s episode of the Business of Data podcast.

However, cloud transformation initiatives are complex, challenging and require careful planning. A process that Crane compares to a pilot building an aeroplane in flight.

“We’re building a new plane in the sky that has to fly higher and faster than the one we’re already in,” Crane says. “And once we get that other plane flying, we have got to get all of the passengers off this plane and onto the new plane while it’s still in the air.”

In addition, Crane says, it is essential to obtain the support of senior leadership for such a long and complex transition to be a success.

“The most important thing in my mind is that the support starts at the top,” Crane says. “If you don’t have that level of support from the top you really won’t be successful.  You can’t do something at this scale at the grass-roots level.”

Companies must be able to provide their customers with premium experiences to remain competitive, argues Crane. Cloud transformation is an essential first step to achieving this.

“The world is moving to the cloud. Your user experiences will be enabled by the cloud. Machine learning and AI and all of that will be dependent on the cloud to deliver the kind of expectations that you want to deliver for you customers,” emphasizes Crane. “The sooner you get there the better off you will be.”

Key Takeaways

• Plan the transition carefully. Migration to the cloud requires a retooling of the foundations of your data infrastructure
• Win the support of senior leadership. Long and complex cloud transformation initiatives require the support of those at the top to be a success
• Get started early. The sooner your company starts the cloud transformation journey, the sooner your customers will reap the benefits

Sherene Jose, VP and Chief of Staff, Cyber and Intelligence Solutions at Mastercard explains how they reimagined their fraud detection teams as revenue-generating innovation machines You might not have heard of Mastercard’s cyber and intelligence solutions team, but you have probably used their technology. Chip and PIN, contactless payments and even biometric-secured purchases are all part of the growing arsenal of payment solutions they oversee at the financial services giant. In fact, creating innovative ways to make shopping safer and easier for their customers the team’s core purpose, explains Mastercard VP Chief of Staff Cyber and Intelligence Solutions Sherene Jose in this week’s episode of the Business of Data podcast. “Theoretically, the best way to achieve zero fraud loss is to just reject every transaction, right?” quips Jose. “[To prevent that] we have to intelligently find ways to navigate the consumer experience and minimize any security risks.” The Birth of Cyber and Intelligence Solutions at Mastercard Prior to 2014, Mastercard had fraud detection and management teams dotted around the business. These decentralized teams were primarily seen as a function of cost control, designed to minimize fraud losses for customers. Then came the big idea: Consolidate these departments with external expertise and create a new, revenue-generating cyber intelligence unit for the business. This unit is now responsible for protecting Mastercard’s payment ecosystem from fraud, creating innovative solutions for its customers and differentiating their core offerings. Of course, patching together a newly conceived cyber intelligence unit from a combination of disparate teams and newly acquired startups is easier said than done. “There was an evolution where teams working in specific verticals of authentication and fraud management and so on learned to come together and think across different verticals,” says Jose. “I could immediately sense the excitement, the sense that things are possible because of this paradigm shift. That mood continues to this day.” Now, the cyber and intelligence solutions unit is at the forefront of innovation and fraud prevention for the company. In the first eight months of last year alone, their AI-powered cybersecurity system ‘Safety Net’ blocked over $113 Million USD in fraudulent transactions in the US. Innovating Payments While Maintaining Customer Security The uptake of technologies like contactless payments, spearheaded at Mastercard by Jose’s team, has skyrocketed during the pandemic. For Jose, the goal is to continue to create seamless and safe ways for their customers to shop, whether that’s online or in-store. “An example of this would be digital wallets, right? You don’t have to key in your password or your PIN to just go ahead and [make] transactions,” she explains. “That’s the kind of seamless experience that we are trying to recreate in every channel.” To do this, it is vital that Jose’s team understands rapidly changing customer needs. By leveraging data and analytics, they are able to build a more complete picture to work from as they create highly secure and innovative payment solutions. “Mastercard as an organization has a very conservative and consumer-centric approach to data and analytics,” she explains. “We never want to store any personally identifiable data. The insights that we get from data in aggregate is what powers our solutions.” “What is top of mind for us is how do we keep our ecosystem safe and how do we keep our stakeholders safe in this environment?” she concludes. “There’s a lot more that we can do and we’re working hard towards it by leveraging the power of data and analytics.” Key Takeaways • Fraud management need not only be a ‘cost’. By leveraging their expertise, fraud teams can be turned into innovation engines • Seamless and secure payments are the heart of the customer experience. Seamless and safe transactions make for happy customers • AI is a powerful tool aga

Jason Smith, Chief Digital Officer, Data and Commerce Groupe, Publicis Groupe chats with us about the reality of data & ai behind the scenes. 

In this week’s episode of the Business of Data podcast, our host Catherine is joined by Jason Smith, Chief Digital Officer, Data and Commerce Groupe for French multinational advertising and public relations company Publicis Groupe. Together they walk through the challenges of Data & AI and the business' perception of what it means for them. 

In the discussion this week:

• AI and our internal bias
• Perfectionism is impossible
• Superiority complex and data

Di Mayze: WPP’s Community-Led Approach to Data and Analytics

Lisa Allen, Head of Data and Analytical Services at UK mapping agency Ordnance Survey, reveals how its data is helping the government respond to COVID-19

Ordnance Survey, Great Britain’s state-owned mapping agency, has a data culture that stretches back to its founding nearly 230 years ago. It supplies geospatial data and services to hundreds of customers from insurance companies to the police and local councils.

Innovation and data science are at the heart of everything Ordnance Survey does, as Ordnance Survey Head of Data and Analytical Services Lisa Allen says in this week’s episode of the Business of Data podcast.

“We manage one of the key national data assets for Great Britain,” Allen says. “The original purpose of [Ordnance Survey] was to collect [data] for cartographic purposes. But actually, now we want it to for analytical purposes.”

“The [Ordnance] Survey has been supplying data during the outbreak and we’ve been in great demand,” she continues. “We’ve really seen [the agency] come into its own.”

The Data Informing the UK’s COVID-19 Response

Thanks to its long heritage, Ordnance Survey boasts a world-class approach to geospatial data science. Its data stores contain more than 500 million geographical features and are updated 20,000 times a day.

Keeping such a crucial dataset up to date a huge responsibility and requires close collaboration between data scientists and surveyors, as well as the use of third-party data and machine learning techniques.

The events of 2020 have underscored how vital this work is. Thanks to the data at its fingertips, Ordnance Survey has been able to provide the British government with data and insights throughout the pandemic.

“COVID-19 has really shown the importance of data,” Allen remarks. “This epidemic is about, ‘Where are the outbreaks?’ And all the information you need to know is based on location.”

“What I’ve really seen during the epidemic is the OS come into its own,” she adds. “We’ve been asked questions about our mapping. We’ve been asked, ‘Where are the care homes? Where are the supermarkets? Where are the GP surgeries?’”

Lisa Allen, Head of Data and Analytics Services, Ordnance Survey“During an emergency we’re available 24 hours a day, every day of the year at no cost”

Ordnance Survey has a contract with the British government that sees it provide geospatial data and location data to public services organizations. It also provides services ranging from providing basic maps and identifying ‘points of interest’ on them to data matching.

“This is especially important for things like addressing,” says Allen. “So, during the pandemic, making sure the letters went out to the vulnerable [and] making sure those addresses were right.”

Following the news that the British government has become the first to authorize a COVID-19 vaccine for use, an end to the pandemic may be on the horizon. But the Ordnance Survey’s work is far from over. The agency will continue providing world-class data-driven services long after the crisis is over, just as it has for hundreds of years.

Meena Thanikachalam, Head of Data Architecture at Ally Bank explains how building a world-class data platform in the cloud will transform the customer experience and build loyalty

Traditional high-street banks were not at the forefront of the digital revolution. However, customers today demand instant access to high-quality digital experiences – a trend that has only been accelerated by the pandemic.

Banks must use their data to develop a better understanding of their customers’ needs, argues Meena Thanikachalam, Head of Data Architecture at online bank Ally in this week’s episode of the Business of Data podcast.

Thanikachalam heads up the team responsible for creating an innovative cloud-based data and analytics platform for the bank that is designed specifically with the customer experience in mind.

“We are building a world-class data platform that will help improve our customer experience,” says Thanikachalam. “And will also help deepen our customer relationships and increase customer loyalty.”

A core element of this customer relationship is to create an experience for the customer which feels bespoke. That is why Ally Bank have done the work to understand what their customers need and when they need it.

“This platform is also looking at integrating omni-channel data and also data that we have collected about customer preferences,” she says. “Based on that we would provide a targeted and personalized experience for them.”

Ally Bank is also using AI initiatives like cognitive computing and conversational AI to further enrich the customer experience and enable customers to do more without needing to speak to an agent.

“In banking specifically, cognitive computing is used predominantly to have human-like conversations,” Thanikachalam says. “That is one area [in banking] where I see AI penetrating a lot.”

Key Takeaways

• Develop a 360-degree view of the customer. Understanding what your customer needs and when they need it will help you shape your strategy.
• Write the data strategy to inform the customer experience. Identify what data is needed and which metrics will most effectively influence the customer.
• Don’t forget the guiding principles. Scalability, reliability, performance efficiency, and operational excellence should guide your architectural work.

Gillian Tomlinson, Director of Data and Analytics at Three UK, outlines how the telco is partnering with its holding company on futuristic data monetization projects that will harness the power of 5G

Public attitudes towards the UK’s 5G rollout may be mixed. But for data and analytics leaders at British telecoms companies, the technology represents an opportunity to explore new data monetization opportunities.

As Three UK Director of Data and Analytics Gillian Tomlinson explains in this week’s Business of Data podcast, this is something Three built into its digital transformation plans years ago.

“[We decided] we had to move our network and our IT stack into the cloud as soon as possible,” she recalls. “We [needed] the processing power in the future. [We] needed to be able to support all that 5G brings. That was really the spur for us.”

In recent years, the company has partnered with cloud providers to ensure its data infrastructure would meet its changing needs as it scaled its ambitions and integrated analytics capabilities.

“The ultimate goal is, you’ve got to compete,” she says. “You’ve got to understand how you’re going to compete in future and how the nature of the industry’s competition is going to change because of digitization.”

Tomlinson argues that success in today’s business landscape requires perpetual digital transformation. Companies must constantly innovate to keep step with their competitors, who are also perpetually innovating.

“There’s no such thing as an end-goal anymore,” she notes. “You’ve got to be constantly improving, testing, proving, scaling up [and] looking for how you’re going to make that next big leap.”

Three UK’s Data Monetization Plans

Enterprises that are sitting on valuable troves of proprietary data are increasingly exploring data monetization opportunities.

While the regulatory protections in place to protect consumer privacy when sharing sensitive information are a challenge, these projects are seen as a logical ‘next step’ for those looking to drive revenue with data.

For Three UK, this means partnering with CK Delta, a holding company within CK Hutchison Group, to provide data support on experimental monetization projects and initiatives.

“We’re incredibly mindful of anonymized customer information being absolutely critical and the compliance requirements around GDPR,” she says. “But the information we sit on, specifically from a network perspective, is incredibly valuable.”

“Our ability to understand, let’s say, people’s movements and provide that in an anonymized way to the City of London is a valuable insight,” she continues. “It’s got a real monetary value attached to it.”

She adds: “More recently, we have also looked at a proof-of-concept that we’ve been working with around flying taxis.”

This kind of innovation may be out of reach for enterprises that are still laying the foundations for advanced analytics. But the promise of data monetization is a tantalising one we expect to see more and more companies exploring in future.

Key Takeaways

Enterprises are eyeing data monetization opportunities. Using data to create valuable new products is a logical next step for advanced data-using companies.

Data monetization depends on proprietary data. Companies that can curate their own unique and valuable datasets will be best positioned to succeed with data monetization.

Enterprises will struggle without the right ‘data foundations’. Data leaders should ensure their organizations have the right level of data maturity before pursuing these projects.

Vauxhall Opel Global Pricing Operations and Strategy Manager Ricardo Rodrigues argues that data is playing a key role as the industry adapts to a generational change in demand for cars

The way people think about car ownership has changed, and data and analytics is helping to create the products and pricing strategies required to meet those changing customer needs.

In this episode of the Business of Data podcast, Vauxhall Opel Global Pricing Operations and Strategy Manager Ricardo Rodrigues outlines how data is driving customer-centric pricing strategies tailored to this new era.

Agility in pricing is key, he says. This is particularly true for younger generations, who increasingly prefer leasing vehicles and using flexible carsharing services over buying cars outright.

“We are now developing several business analytics and BI tools to help with pricing,” Rodrigues says. “Because, as the concept of ownership has changed across the generations, so has pricing.”

He continues: “You need to think about daily and weekly rates and how competitive other brands are, which means that we need a lot of data – a lot of competitor data and a lot of customer data.”

For Rodrigues, access to customer data is at the core of creating hyper-personalized products and experiences online. But he says businesses must be transparent about its use if they are to maintain the willingness of their customers to participate.

“As long as we are transparent, and we are, and the customer sees the added value of that, I think they are willing to share the data,” he says.

Rodrigues’ goal is to provide real-time insights based on the most recent data and market trends available. But his ability to do that relies solid data governance.

“Spend some time on your data governance and make sure that you get the right data for your business needs,” he recommends. “If you achieve that, I would say that 70% or more of your work is done.”

“You can have the best tools in the world,” he concludes. “But if the data is not aligned to your needs then those tools cannot do miracles.”

Key takeaways

• Customers expect products that meet their needs. Data and analytics are helping manufacturers adapt to rapidly changing customer demands
• Preserving access to customer data is key. Businesses must use data responsibly to maintain the trust of their customers
• Invest in strong data fundamentals. A focus on sound data governance will help to ensure that data initiatives provide accurate insights

Amica Life Insurance Assistant VP of Life Data Strategy Tracy McDonagh argues that insurers must provide more digital options to their customers to stay competitive in a fast-moving marketplace

The days of shopping for insurance through an agent are coming to an end. In a competitive B2C insurance marketplace, providing enhanced digital access to data and services has never been more important.

Upgrading data platforms to accommodate this shift in customer behavior is essential for forward-thinking insurers, as Amica Life Insurance Assistant VP of Life Data Strategy Tracy McDonagh argues in this week’s episode of the Business of Data podcast.

As McDonagh explains, the modern customer is more technologically savvy and has higher expectations of their insurance providers than ever before.

“We’ve definitely seen an uptick in terms of how people are looking [for insurance products] digitally,” she says. “They want to be able to log on, see what they’ve got, see what the offerings are and be able to start applications online.”

In her role at Amica, McDonagh has spearheaded digital initiatives that allow customers to manage transactions online and put policy information into their hands.

“What we do at Amica Life is provide products that are easy for our customers to navigate and we want to make sure we have not only a product but a process that allows us to do that,” she says.

While the insurance industry is sometimes slow to adopt innovation, the benefits of upgrading to a modern data platform helped to address ‘legacy thinking’ at Amica.

“There have been so many pain points in regard to our old systems that people really are looking to all of the positives of working with these new, modern platforms,” McDonagh notes.

She concludes: “From a user experience [perspective], in terms of the customers and the internal experience of using these systems and supporting the customers, there really has not been a lot of resistance to change.”

Key takeaways

• Digital transformation is key to meeting customer demand. Modern shoppers want seamless digital experiences
• Align your vision to business objectives. Overcoming legacy thinking requires a vision of the business’ future state
• Provide first-class insights. It all starts with the data. To be successful, the data must be clean, reliable and timely

A well-executed data literacy program is essential to make data-driven operations ‘business as usual’, argues Synchrony Financial VP of Data Analytics Edgar Abreu in this week’s podcast.

Data literacy is the cornerstone of a data-driven culture and facilitates effective communication between both technical and non-technical stakeholders at all levels of a business.

That is why Edgar Abreu VP of Data Analytics at retail credit card company Synchrony Financial created the Data Intelligence Academy in his organization as he explains in this week’s episode of the Business of Data Podcast.

“In order to make data and analytics ‘business as usual’ everyone need to not only be on board with it but also speak the same language,” explains Abreu. “So there needs to be a certain level of data literacy in the organization.”

While most enterprise businesses now understand that being data-driven is the way forward many still struggle to instil a truly data-driven culture.

Abreu thinks that this may be driven by a lack of corporate focus on the development of data literacy programs and notes a recent study by market research firm Gartner which found that 80% of businesses are only now rolling out their data literacy programs in 2020.

For Abreu, the key is to develop data literacy at all levels of an organization, including for senior leadership, and to start early to help promote a data-driven culture.

“Start a data literacy program early,” Abreu advises. “It will make the whole data and analytics journey much more successful, impactful, and more efficient.”

Key Takeaways

• Start your data literacy program early. Implementing a data literacy program early will enhance your whole data and analytics journey
• Formalize the process. Make sure you have a dedicated resource and funding to develop your data literacy curriculum
• Close the communication gap. By increasing your data literacy organization-wide you will drive your data-driven culture forward

Santiago Castro, CDO at FBN Bank, discusses how the bank’s digital transformation prepared it for the challenges of COVID-19 and how he’s building even more adaptability into its resilience strategy going forward

When FBN Bank CDO Santiago Castro was named the bank’s Interim COO in January, he was unaware of the events that would unfold in the months ahead. Luckily, he had kickstarted FBN’s digital transformation two years previously.

In this episode of the Business of Data podcast, Castro outlines how the COVID-19 pandemic tested FBN Bank’s business continuity and shares how this has changed the way he thinks about its data strategy.

“In two weeks, we had to move all the operations to work remotely,” he recalls. “This pandemic has [been] a real test scenario about operational resilience and business continuity because all organizations have had to ensure and prove that [they] can still work.”

He adds: “We managed to actually work in all our business processes and business services without interruption, which shows that in real, extreme scenarios, we’re still resilient.”

How Digitization Improves Business Resilience

Most business continuity strategies focus on scenarios where company buildings or systems are compromised. But COVID-19 is also ‘people’ crisis – something that many organizations hadn’t planned for.

“Not only we had to adapt, [but] we had to also adapt in times where some people were falling sick,” says Santiago. “So, we needed to cope with mental health, with stress [and] fatigue.”

Luckily, FBN Bank had already embarked on its data and digitization journey. Castro’s team has created a data hub, data strategy and governance policies, so the bank could phase out many of its old analogue processes.

“We started the journey two years ago to start [digitizing] and start automating a lot of the processes to start bringing business intelligence, reporting, analytics and, most importantly, data flags (or what we call ‘automation of exemptions’),” he says.

“If two years ago we [didn’t do] this, it would have been very difficult to work from home,” he continues. “Definitely, having started the journey has enabled us to be resilient.”

This experience has also changed Santiago’s perspective on the concept of ‘operational resilience’. He now views FBN Bank’s continuity strategy as more than a collection of defensive goals and policies.

“This challenge allowed us to also open our mind to flexibility to explore new ideas, explore new ways of doing [things] and of course being progressive,” he concludes. “Now, we’re also putting in our strategy the emphasis of flexibility and adaptability, and actually that also makes us resilient.”

Key Takeaways

• COVID-19 has been a test for business resilience. Organizations that depend on analogue or in-person processes have been severely disrupted by the pandemic
• Digitization can improve operational resilience. Automating manual processes and digitizing analogue ones allows businesses to provide services more flexibly
• Data strategy advancement facilitates digitization. Successful digitization projects depend on a solid foundation of data strategy, governance and skills

Russell Barker, Global Head of Macro Data Strategies at Morgan Stanley, outlines how he partnered with colleagues at all levels of the business to identify and refine the four strands of the firm’s data strategy

Executive sponsorship may be a vital ingredient for data strategy success. But in an enterprise as large as Morgan Stanley, developing that strategy through a purely ‘top down’ approach is a recipe for trouble.

In this episode of the Business of Data podcast, Morgan Stanley Global Head of Macro Data Strategies Russell Barker reveals how he consulted extensively with business stakeholders to develop the financial services giant’s approach to data.

He says this process was essential for aligning the firm’s strategy with the needs of different departments and business units, as well as identifying the common threads that tied everything together.

“[Business] users really understand what they want to do,” he says. “The bit they may not get is how they can do it with data and new techniques.”

“The very first thing we did was talk to a lot of people,” he continues. “I probably talked to more than 100 people, asking what their current data usage was, what they currently liked, what their current pain points were and what they thought could be the ‘big wins’ if we did some new stuff.”

In this way, Russell learned that data discovery, data accessibility, data quality and data governance concerns were the four threads that tied the pain points of stakeholders across the business together.

He adds: “Then, we cherrypicked very specific business [use] cases that allowed us to deliver something directly to their desks, but also allowed us to start building the infrastructure around it.”

For Russell, this consultative approach is the best way to align a company’s data team with the wider business. He views communicating the needs of the wider business to his executive sponsors as an integral part of his role.

“One of the things I’ve found [to be] great in my role at Morgan Stanley is that my bosses trust me,” he says. “They have faith in me and the [people] I work with know that I understand their businesses and that I will listen.”

“It’s all about letting the business needs drive the strategy,” he concludes. “Unless you have buy-in from the people on the trading floor, [a] top-down approach isn’t going to help.”

Key Takeaways

• Executive sponsorship alone is not enough. Enterprises must gather input from across the organization to ensure their data strategies meet the needs of their staff
• Consult extensively with company stakeholders. Data leaders must develop a deep understanding of the company pain points that data can address and use this as the basis for their strategies
• Build a data roadmap around the common threads. Start with specific use cases that can deliver ‘quick wins’, but ensure they feed into an overarching framework that will benefit everyone

Martin Campbell, CIO at World Vision, outlines why he’s doubling down on the children’s charity digital transformation in the age of COVID-19

The charity sector tends to lag behind other industries when it comes to digital transformation, due to its conservative approach to innovation. But as Martin Campbell, CIO at children’s charity World Vision, says, the sector can no longer afford to ignore the benefits of going digital.

In this episode of the Business of Data podcast, he outlines his vision for digital transformation at the charity and why he’s accelerating his strategy in response to the COVID-19 pandemic.

“My brief here at World Vision UK, as Chief Information Officer, is digital transformation,” he says. “What that means is really taking a look at how recent changes in marketing and communications have transformed many industries.”

He adds: “These days, we know digital works. We know that data analytics is fundamental to good decision-making. So, we’re now making very big strides into that area.”

The 2020 pandemic has disrupted all the in-person channels charities traditionally use to raise money, such as through organizing fun runs, concerts or auctions. Campbell says this has underscored why it’s critical for charities to be making the most of opportunities in the digital space.

“We saw an increase in supporters online during lockdown,” he notes. “I’ve been saying for years, ‘The writing’s on the wall. Digital’s going to be our biggest channel before too long.’ It was already getting to be our biggest channel [for] reaching new supporters before COVID-19.”

Ultimately, 2020 has proven to be a learning opportunity for World Vision. Campbell now plans to build on the experiences of the past few months to equip staff with better insights about the charity’s supporters and develop new data-driven marketing capabilities.

“We’re looking at how we can do more multichannel-type communications with our potential supporters and having conversations with people across a number of touchpoints,” he explains. “We’re also looking at, ‘What data to we need to enable that?’”

He adds: “We just in the process now of launching a new digital marketing platform that has analytics at the heart of it.”

We wish World Vision all the best as they embark on the next stage of its digital transformation journey. Anyone who is interested in donating to the charity can find out more about the work it’s doing here.

Key Takeaways

• Harnessing the power of digital is vital in 2020. Digital channels were already becoming World Vision’s top way of reaching new supporters before the pandemic
• Data is the foundation that underpins digital transformation. Quality data and sound analytics is the key to making better decisions and optimizing digital marketing campaigns
• Personalized multichannel communications are the future. Data-focused leaders in all sectors can drive business results through helping their brands communicate more effectively

We want to wish all our listeners a very happy holiday! 

We'll be back in January with more brilliant episodes. 

Harvinder Atwal, Group Data Director at MoneySupermarket, shares how DataOps principles have dramatically enhanced the productivity of the group’s data function

Adopting DataOps practices has helped MoneySupermarket’s data function drive significant productivity gains in recent years. The results have been so good, MoneySupermarket Group Data Director Harvinder Atwal decided to chronicle his experiences in his book, Practical DataOps: Delivering Agile Data Science at Scale.

In this episode of the Business of Data podcast, he outlines the key principles that define DataOps and shares how adopting a ‘data products’ mindset is helping his team drive business results more effectively.

“For us, DataOps is data analytics – in its broadest sense, including data science and AI – combined with ‘lean thinking’,” he explains. “The creation of data products is key.”

What DataOps and DevOps Have in Common

There are two strands to the DataOps concept of ‘lean thinking’. One is about looking at processes, making them more efficient and adapting to change. The other is DevOps.

Atwal explains that DevOps has its roots in the historic tension between software developers and operations professionals. While developers want to innovate and improve applications, this can create challenges for operations people, who need to make sure things run in production reliably.

“The challenge was that you get to a place where the operations people are maintaining a really brittle product in production,” he explains. “DevOps is there to make sure that these things [don’t] happen.”

Instead of developing apps as giant ‘monoliths’, DevOps breaks them down into independent constituent parts. These can then be iterated rapidly to incrementally improve their performance.

Harvinder notes that a key step in applying ‘lean thinking’ principles to data and analytics is making the switch from a ‘project’ to a ‘product’ mindset. Rather than starting with data and trawling it for insights, data teams should start with a ‘desired outcome’ and go from there.

“Traditionally, the way people have approached using data is to think about actionable insights,” he says. “ So, ‘What can we find in the data that will produce some insights and create a recommendation?’”

“It’s about flipping everything on its head,” he continues. “We’ll take an outcome and say, ‘What kind of data product can we build that will deliver that outcome?’”

Key Takeaways

· Adopt a ‘data products’ mindset. Data teams should start with a business challenge and design a data product that achieves a predefined desired outcome

· Streamline the data product pipeline. Use ‘lean thinking’ principles to find bottlenecks in existing business processes and find ways to make the data pipeline more efficient

· Continuously integrate; continuously develop. Rapidly iterate data products to add in new features, reduce model scoring latency and drive better business outcomes

Data leaders must update their strategies continually to stay aligned with business objectives, argues Dr Aleksandar Lazarevic, VP of Advanced Analytics and Data Engineering at Stanley Black and Decker

When Dr Aleksandar Lazarevic joined industrial tools manufacturer Stanley Black and Decker 18 months ago, he set to work updating its analytics strategy to drive company-wide analytics adoption.

In this episode of the Business of Data podcast, he outlines his approach to creating an analytics roadmap that accounts for data availability, data quality and business readiness.

“Most analytics projects, in my opinion, fail because they don’t have proper business partnership,” he says. “It requires a lot of iteration, working with the business together, trying to figure out what the right business needs are.”

For Dr Lazarevic, data science is an iterative process. As such, data leaders must work closely with partners in IT and specific business units to assess the business’ needs and prioritize projects accordingly.

“What is the right business problem to solve?” he asks. “What is the right analytical solution? What are the right KPIs you need to track? And, how you can quantify this and make sure the business adopts your solution?”

Importantly, Dr Lazarevic argues that data leaders must ask these questions continually and update their strategies in line with changing circumstances. This idea has been particularly salient with respect to delivering predictive analytics projects in the age of COVID-19.

Dr Lazarevic explains: “In addition to using the data from last year, which could not be relevant, we tried to use other economic indicators, financial indicators, stock indexes or particular trends and tried to incorporate them into the models we were trying to build.”

COVID-19 has accelerated digital transformations across the world. But despite the increased demand for data-driven insights, Dr Lazarevic says enterprises must still deal with the challenges that held them back before the pandemic.

“There is a lack of understanding or lack of comprehending [about] what the art of the possible within the data and analytics world is,” he notes. “I believe considering the business aspects and people recognizing how particular analytics techniques would be relevant to them is the key.”

He concludes: “What we’re trying to do is to provide not only those technical courses, but also to work with several vendors to [coach] people on how they can analytically think for some of the use cases.”

Key Takeaways

• Revisit your data strategy roadmap regularly. Data leaders must continually update their strategies to keep them aligned with business objectives
• Building strong business partnerships is key. Data teams must work closely with IT and business unit stakeholders to develop effective data-driven tools
• Teach the art of the possible. Sharing case studies and success stories and building data literacy is essential for nurturing demand for data within an organization

Every Enterprise Must Define Its Own Data Management Journey

Laura Hahn, Director, Enterprise Data Management at TD Ameritrade, shares key learnings from her experiences implementing the financial services company’s master data management platform

Every company must forge its own path when it comes to data management.

As TD Ameritrade Director, Enterprise Data Management Laura Hahn says in this week’s episode of the Business of Data podcast, there’s no one approach that works for everyone.

“You have to be careful of borrowing another company’s ‘why’,” she warns. “Start with the company’s strategy and the company’s initiatives and map those onto data capabilities.”

“Be willing for that to look different from any other company that you know about,” she continues. “You really have to figure out the ‘why’ you want to do it that’s specific to your organization.”

TD Ameritrade’s ‘why’ is to arm staff with useful insights about how clients are managing their money by connecting the data across all their accounts.

Hahn explains: “The journey for us has been about moving from account centricity to really understanding the client and all of the money they have some sort of responsibility for and what their goals are.”

The company recently implemented a master data management platform to achieve this goal. Hahn views this platform as being central to the company’s data management strategy going forwards, but says securing support for the project was a challenge.

“I would say, sexier tends to get started sooner,” she quips. “It’s all about appetite in your company to adopt [data-driven] capabilities and bring them into the norms of the firm.”

She says that the key to developing this appetite at TD Ameritrade was identifying who would do their jobs differently if they had access to those insights. From there, it was about speaking the language of those stakeholders to show them what they were missing.

“We really had to work on examples and actually draw from the data itself,” she recalls. “How many accounts does Laura Hahn have with us? Look at all the things she’s doing.”

“You have to drop the data management jargon,” she concludes. “Going around and selling people a ‘single source of truth’ or a ‘mastered client record’ doesn’t mean anything to [most staff].”

Key Takeaways

• Cater to the needs of your organization. Successful data management strategy starts with understanding your business, not copying others
• Make data management relatable. Translate the benefits of what you’re doing into the language of the business users you want to help
• Find a needle you can move. Prioritize projects that align with corporate goals and where there’s sufficient demand from the business for them to drive real value

Advances in data and technology may herald a step-change in commercial insurance, Antton Peña and Rayno Mostert from drone insurance company Flock argue in this week’s podcast

From self-driving cars to drone deliveries, practical applications for advanced autonomous vehicles are rapidly increasing around the world. Innovation is at the core of Flock Founder Antton Peña’s mission to reinvent drone insurance, as he explains in this week’s episode of the Business of Data podcast,

“It’s no longer simply about entering a few input fields and getting a price,” Peña says. “It’s about understanding how the technology you have got changes risk and about how [that technology] will change risk in the future.”

Drones are now delivering tests and medical supplies in the UK and around the world. This new application of drone technology has only become more urgent in the wake of the global pandemic.

“There has been a big push to use this technology,” explains Peña. “Things like drug delivery tests for the NHS that have been done by drone.”

Central to Flock’s innovative approach to drone insurance is a fresh take on how to appropriately assess risk in the absence of relevant historical data. The company’s approach has been to develop a simulation-like approach that accounts for a vast number of factors to quantify the probability of a crash.

“If you don’t have historical claims data you need something more granular, something that models the risk from the ground up,” explains Flock Actuarial Data Scientist Rayno Mostert.

As well as innovating in technology Flock is leading the field in use-based insurance plans. Mostert believes that the pandemic has highlighted the need for the insurance industry to account for use in the way it bills its clients.

“We’ve seen in the past few months motor insurers refunding clients due to reduced exposure, Mostert concludes. “I see that essentially as recognition that the [insurance] industry says the way to fairly price insurance is by looking at how much your customer is using.”

Key Takeaways

• With new applications come new challenges. As autonomous vehicles increase in number around the globe so will the need to find innovative approaches to regulation
• Innovating requires educating. It is not enough to build sophisticated technology; you need to educate your partners and your customers on the benefits too
• The insurance of the future accounts for use. The pandemic has prevented many businesses from operating commercially – is it time for a new approach to insurance payments?

COVID-19 has made a focus on the fundamentals of data literacy and accuracy more crucial than ever, Bane Care Management Director of Data and Performance Analytics Tim Carey argues in this week’s podcast

Covid-19 has had a dramatic effect on in-person care in the US healthcare system and the simultaneous shake-up of data priorities has been no less extreme.

Responding to the changes has been a key priority for Bane Care Management’s Director of Data and Performance Analytics, Tim Carey, in the last six months as he explains in this week’s episode of the Business of Data podcast.

“Pre-Covid-19 we were sending huge data dump files with tons of metrics,” he says. “Is 95% of that even being used? If the answer is no, then why are we doing it?

This reevaluation of data priorities has brought the immediate need for relevant, understandable metrics for healthcare practitioners into sharp relief.

“Our motto is ‘keep it simple’,” he explains. “Have the data in a graph that is visually easy to comprehend. So, within 3–5 seconds you know exactly what that graph is telling you.”

Carey thinks that although the benefits of advanced technologies like AI and advanced analytics have promise, they need to be relatable to people at the level of clinics and hospitals.

“I think there is a lot of work to be done in healthcare,” he says. “When you look at other industries it feels that they are light-years ahead.”

While some healthcare companies may be doing revolutionary work in data and analytics at the corporate level, the models are rarely operational at the level of clinics and hospitals.

“[these technologies] need to be a lot easier for people to comprehend,” he concludes. “The benefits have to trickle down to the people who are closest to the work.”

Key Takeaways

•Keep it simple. Focusing on understandable data and relevant metrics will help end-users better contextualize their place in the wider business picture

•Take it back to basics. A focus on the fundamentals of data literacy and accuracy will provide a solid base to grow on

•Reevaluate your tools. Has your Electronic Medical Records system (EMR) kept up with the times? If not, it may be bottlenecking the analytical capability of your teams. 

‘Never Stop Experimenting,’ Implores Rising Stars Foundation CDO Larry Shiller COVID-19 has been a stark reminder of why data leaders must embrace creativity and experimentation, Rising Stars Foundation CDO Larry Shiller argues in this week’s podcast

It’s been a tough year for Rising Stars Foundation. Roughly 70% of home schooling curricula sales come from in-person conferences between March and July. So, COVID-19 forced the charity to rethink its whole plan for 2020.

However, the foundation’s CDO, Larry Shiller, seems to have taken this unprecedented disruption in his stride. In this week’s episode of the Business of Data podcast, he shares how he helped the organization swiftly pivot its strategy to avert disaster.

“The pandemic [has] just accelerated existing trends toward better data leverage,” he argues. “It’s prompting more creative thinking or more lateral thinking.”

Faced with a potentially catastrophic loss of revenue, Rising Stars Foundation rolled out a new strategy built around promoting its digital curricula, creating YouTube tutorials and driving social media engagement.

“Data analysis of all that stuff was obviously critical to determining its success,” Shiller says. “We used data and analytics to grow revenue and reduce risk and expense.”

“So far, our sales have been down 10% year over year,” he adds. “But that could easily have gone down 70% if we didn’t take these proactive steps early on in the pandemic.”

This is not the first time the charity has had to turn on a dime. Shiller says he initially thought the first iteration of his algorithm for measuring a person’s ‘grit’ was a failure. The project started life as a study program that was too tough for most kids to complete. When universities told Shiller they wanted to know about the determined few who got all the way through, he realized the value of what he’d created.

He says: “I like to tell people, ‘I have a new idea. I’m excited about it. But be forewarned, 99% of my ideas suck.'”

“That’s OK, because eventually we’re going to find the one that really does work,” he concludes. “So, let’s keep making those mistakes, so we can find the next transformative analysis or breakthrough.”

Key Takeaways

• Update your data strategy regularly. This year has proven that data leaders must sometimes totally rethink their plans in light of new business priorities
• Create a safe space for innovation. Not all experiments result in immediate success. But trying new things is essential for driving company innovation
• Look for the good in bad outcomes. Negative results are still results, and they provide insights that point the way to the breakthroughs you’re looking for

Besa Bauta, CDO at children’s charity MercyFirst, argues that better collaboration between health and social care data leaders will be needed to meet patient expectations in the post-pandemic ‘new normal’

Patient and employee expectations around data sharing and accessibility are soaring as a result of COVID-19, MercyFirst CDO Besa Bauta argues in this week’s Business of Data podcast episode.

The pandemic has thrown the benefits of free-flowing patient data between healthcare settings into stark relief. But the industry’s data leaders will need to collaborate more effectively to make this vision a reality.

“We can’t think, ‘I’m the best hospital and I have the best system,’ and not think about your neighbor’s hospital,” Dr Bauta argues. “Your patients are going to go from hospital to hospital and service to service.”

“Hospitals and other systems have to react to that consumer demand,” she adds. “So, each of us has to work together to ensure that all our systems are working together the way that they should.”

The current incompatibility between different Electronic Health Records and other healthcare data systems remains a key obstacle on the industry’s path to data maturity.

“There’s plenty of data,” Dr Bauta explains. “The problem is that it’s coming from all over the place.”

“We don’t have a complete picture because it’s fragmented in four different systems,” she continues. “That’s a challenge, and each time I’m in a meeting I’m finding that there’s new information somewhere else that we should be aware of.”

For this reason, she says breaking down data silos, cataloguing what data exists and determining what data is ‘mission-critical’ will remain top priorities for the sector’s data leaders going into 2021.

Key Takeaways

• Demand for healthcare data is soaring. The COVID-19 pandemic has thrown the need for accurate and timely patient and operational data into stark relief
• Healthcare data leaders must facilitate data sharing. Patients increasingly expect their data to flow seamlessly between healthcare settings, depending on where they’re being treated
• Technical challenges are slowing progress. Poor systems interoperability and data silos are making it hard for CDOs to lay the foundations for their data strategies

Liberty Global Director of Data Dee Samra reveals how she’s changing perceptions around data governance at the telecoms company

Less-data-driven organizations often view data governance as a bit of a headache. But as Dee Samra, Director of Data at telecoms firm Liberty Global, says, it doesn’t have to be that way.

In this debut episode of our brand-new Business of Data podcast, Samra talks to Corinium’s Catherine King about how she’s establishing data governance as a key value driver at the company.

“[Data governance] is an enabler,” she says. “Focusing on the benefits that data governance will bring to leverage new technologies – that’s where you’ll get that buy-in and get that ‘sell’ to get [staff] to engage.”

“It’s people on the customer-facing parts of your workforce that are going to notice [data quality] issues,” she adds. “The first step is really just making people think about it.”

“Now, somebody has to be accountable for data,” she continues. “That’s never had to happen before. Because that accountability is there, it starts making people question the data that they have.”

Key Takeaways

• Data governance is a value creator – promoting data governance as a key enabler for exciting new technologies such as AI is the key to securing buy-in for it
• Build a strong business case – focus on linking good data governance to better business outcomes while also stressing the company’s regulatory obligations
• Raising awareness is key – establishing data ownership and getting people to question where the data they’re using comes from are essential data governance ‘first steps’

FICO CAO Scott Zoldi outlines how he believes enterprises can ensure they’re using AI ethically and responsibly in episode two of the Business of Data podcast

AI ethics emerged as a key barrier to enterprise AI adoption when analytics company FICO commissioned Corinium to survey 100 CDOs, CAOs and CDAOs about their AI strategies. So for the second episode of the Business of Data podcast, we invite FICO CAO Scott Zoldi to join us and share his views about the findings of this research.

“The hype cycle of AI is over and the hard work has begun,” he says. “To the extent that the data which is around our society it biased (which it is), you need models that you can demonstrate do not necessarily reflect those biases.”

For Zoldi, the buck for AI ethics stops with a company’s CDO or CAO. It’s up to them to get ethics recognized as a board-level issue and ensure there are processes in place to ensure ethical AI usage.

“They have to define one standard within their organization,” he explains. “They need to make sure it aligns from a regulatory perspective. They need to align all their data scientists around a centralized management or standardization of how you do that. And that takes a lot of work.”

Crucially, Zoldi stresses that enterprises must monitor AI systems on an ongoing basis to be sure they’re using AI ethically. Our research shows that just 33% of AI-using enterprises currently do this.

“Look at the pandemic,” Zoldi argues. “[The pandemic] affects different protected and ethnic groups differently, based on their exposure to the virus and the types of work that they’re forced to do. That means, [certain] models that may have been ethical at the time they were built are no longer ethical today.”

He concludes: “You’re not done with the model when you’re done building it. You’re done with the model when it ceases to be used.”

Key Takeaways

• AI ethics is a board-level issue. It’s up to a company’s data and analytics leadership to ensure executives prioritize ethical considerations around AI usage
• Ethics policies must be enforced. Strong AI governance policies are needed to enforce AI ethics standards across the organization
• AI models require continuous monitoring. Data scientists must monitor the performance of AI models to ensure their decisions don’t become unfair

In this week's episode of the Business of Data podcast, host Catherine King talks with Carolina Azar, Senior Director, Product Strategy at Moody’s Analytics. Together they discuss the benefits of assessing risk holistically to gain better insight into the vendor landscape, that in turn helps business leaders across the globe make better business decisions.

In the discussion this week:

● Business trends of current decision making

● Cultural mindset challenges

● Importance of trustworthy third-party data for supplier risk management

● Holistic risk assessments, including ESG metrics

● Market trends

Today’s podcast episode was made possible by our partnership with Moody’s!

Moody’s Analytics provides financial intelligence and analytical tools to help business leaders make better, faster decisions. Our deep risk expertise, expansive information resources, and innovative application of technology help our clients confidently navigate an evolving marketplace. We are known for our industry-leading and award-winning solutions, made up of research, data, software, and professional services, assembled to deliver a seamless customer experience. We create confidence in thousands of organizations worldwide, with our commitment to excellence, open mindset approach, and focus on meeting customer needs. For more information about Moody’s Analytics, visit our websiteor connect with us on Twitter and LinkedIn.

Moody's Analytics, Inc. is a subsidiary of Moody's Corporation (NYSE: MCO). Moody's Corporation reported revenue of $6.2 billion in 2021, employs over 14,000 people worldwide and maintains a presence in more than 40 countries.

For information on Moody’s Analytics Supply Chain Catalyst, click here

In this week’s episode of the Business of Data podcast, live at Chief Data & Analytics Officer, Nordics in Stockholm – our host Catherine is joined by Maija Hovila, Chief Analytics Officer for an engineering company, Kone. Together they walk through Maija’s journey into thought leadership and her experiences of getting data into the business.

In the discussion this week:

• Data literacy as a challenge and enabler
• Data Security and Privacy
• Talent Shortage
• Sneak peek into the CDAO Nordics event

Steve Kleinmann, Industry Practice Lead, Master Data Solutions for Moody’s Analytics talks to us about how organizations around the world are embracing third-party data to enrich their internal data and knowledge and grow their businesses.

In this week's episode of the Business of Data podcast, host Catherine King talks with Steve Kleinmann, Industry Practice Lead, Master Data Solutions for financial intelligence and analytical tooling company, Moody’s Analytics. Together they walk through how organizations around the world are embracing third-party data to enrich their internal data and knowledge and grow their businesses.

In the discussion this week:

• Importance of augmenting decision-making with data
• The difference between data clarity and data chaos
• Compliance and risk activities, and how they can become enablers of growth
• Market trends

Today’s podcast episode was made possible by our partnership with Moody’s Analytics.
Moody’s Analytics Solutions for Third-Party Master Data
As organizations search for ways to optimize their data management strategies, master data management (MDM) rises to the top as an effective, secure, and scalable solution. Third-party master data, when used in conjunction with an enterprise MDM system, enables an organization to control its data and unlock numerous financial and productivity growth opportunities. Moody’s Analytics provides master data, including firmographics, company size metrics, and corporate hierarchies. With industry-leading coverage of over 448 million entities across a broad range of entity types, our pre-mastered data is available via a wide range of applications, APIs and proprietary connectors to ensure seamless delivery to virtually any third-party or in-house data management system.
For more information, click here.

In this week's episode of the Business of Data podcast, host Catherine King talks with Jay Como, Chief Financial Data Officer for the biggest bank in Silicon Valley, Silicon Valley Bank! Together they walk through where the CDO should sit in relation to the business, and what the implications are.

In the discussion this week:

• Senior Data Leadership
• Data Culture
• Understanding the Business needs
• Data trust

In this week's episode of the Business of Data podcast, host Catherine King talks with Murtz Daud, Chief Data Officer for a large independent charity, St Andrew's Healthcare. Together they walk through the future aspirations of predictive data solutions, and the challenges of cultural pushback to data-driven decisions.

In the discussion this week:

• Augmented decision making
• Predictive data solutions
• Self-service adoption
• People & Culture

Loretta Franks, VP & Chief Data Analytics Officer for Kellogg company talks to us about her organization's data capabilities and the business value data can provide

In this week's episode of the Business of Data podcast, host Catherine King talks with Loretta Franks, VP & CDAO for American multinational food manufacturing company, Kellogg. Together they walk through the challenges and benefits of storytelling, business value, and collaboration.

In the discussion this week:

• Communicating complex information to the business
• Blurring the lines between the business and data
• Unlocking the joint value creation
• The impact of seeing yourself as a 'business' leader, not just a data leader
• The creation of a data academy
• Getting more people into STEM

Chris Parmer, Chief Product Officer and Co-Founder of Plotly talks to us about how he’s working to ensure that even the most advanced analytic insights are accessible by everyone – whether or not they know how to code!

In this week's episode of the Business of Data podcast, host Catherine King talks with Chris Parmer, Chief Product Officer and Co-Founder of software company, Plotly. Together they walk through the challenges and benefits of empowering data scientists with data visualization and data apps.

In the discussion this week:

• Communicating complex information to the business
• Role development for data scientists, enabling data scientists to publish and share their work
• How visualization is going to shape data adoption in the market
• The benefit of interactive data apps as a technology

Today’s podcast episode was made possible by our partnership with Plotly!
Plotly is a software company whose mission is to enable every company to build data apps. Their product, Dash Enterprise, is a platform of best-in-class development tools to quickly and easily analyze and visualize data with Python from virtually any data source. With customers across the Fortune 500, Plotly is a category-defining leader in enabling data-driven decisions from advanced analytics, machine learning, and artificial intelligence. For more information, visit Plotly here.

Helen Louwrens, Director of Data & Insight for the Care Quality Commission, an independent regulator for health and social care services in the UK, reveals takeaways from going through an organization transformation, as well as managing functional change management against the background of the global pandemic.

In this week's episode of the Business of Data podcast, host Catherine King talks with Avinash Tripathi, Vice President of Analytics - Marketing Analytics and Marketing Sciences, University of Phoenix. Together they walk through the challenges and benefits of approaching analytics as a profit and revenue-generating opportunity, rather than a cost center.

In the discussion this week:

• Making analytics simple
• Practical tips on approaching business problems with analytical solutions
• Accessibility
• Profit generation as a mindset

Victoria Gamerman, Global Head of Data Governance and Insights, Boehringer Ingelheim talks with us about her experience and journey into data thought leadership

In this week's episode of the Business of Data podcast, live at Chief Data & Analytics Officers, Fall in Boston - our host Catherine is joined by Victoria Gamerman, Global Head of Data Governance and Insights, for one of the world's largest pharmaceutical companies, Boehringer Ingelheim. Together they walk through Victoria's journey into thought leadership, and her advice on networking, mentoring, and getting your name out there.

In the discussion this week:

• How to become a trusted source
• Networking top tips; virtual and in-person
• Helping mentor future talent
• Behind the scenes of the first-ever North American Business of Data Awards!

Suresh Martha, Head of Data-Driven Innovation & Analytics for EMD Serono Inc talks with us about how additional datasets can provide much-needed market context to the business

In this week's episode of the Business of Data podcast, host Catherine King talks with Suresh Martha, Head of Data-Driven Innovation & Analytics for the healthcare business of Merck KGaA, Darmstadt, Germany, EMD Serono Inc. Together they walk through the challenges and benefits of third party data, and how it can add in market context which allows you to stay competitive.

In the discussion this week:

• Data quality challenges of third-party data
• Ethics and access to third-party data
• Thinking outside the box of traditional datasets
• Marketing intelligence benefits

In this week's episode of the Business of Data podcast, host Catherine King talks with Dora Boussias, Senior Director, Data Strategy & Architecture for American multinational medical technologies corporation Stryker. Together they walk through the process of streamlining operations to make data & analytics simple and effective.

In the discussion this week:

• Enabling data access ease
• Understanding data's journey through the business
• How to simplify processes
• What it takes to have an end-to-end view of data
• What it takes to re-train the current business habits

In this week's episode of the Business of Data podcast, host Catherine King talks with Bobbi Jo Allan, Vice President, NF Digital Product Management and Innovation for  Fortune 100 company Nationwide. Together they walk through the importance of placing your customer at the center of your thinking, and how you can impact the bigger business picture.

In the discussion this week:

• Hybrid models of innovation
• Understanding your customer's thinking with data
• Retaining top talent
• How industry-specific knowledge can really empower a data team

Munyaradzi Nyikavaranda, Group Executive Head: Digital Analytics & Marketing Technology at MultiChoice Group talks with us about how his team is using behavioral science and big data to create shortcuts to business value

In this week's episode of the Business of Data podcast, host Catherine King talks with Munyaradzi Nyikavaranda, Group Executive Head: Digital Analytics & Marketing Technology from South African Media company, MultiChoice Group. Together they walk the complexities of human nature, and how behavioral science can provide quantitative answers to business questions.

In the discussion this week:

• The quantitative data behavioral science can provide
• The risk element to influencing customers' behavior
• Challenges of a value-driven mindset
• Successful examples of behavioral science making a difference

Daniel Cox, VP of Data Visualization and Insights for Barclays talks with us about how he found his way into the world of visualization and what he's up to at the moment

In this week's episode of the Business of Data podcast, host Catherine King talks with Daniel Cox, VP of Data Visualization and Insights for British multinational universal bank, Barclays. Together they walk through Dan's career journey from baker to data, and some of his key learnings when it comes to creating meaningful data visualization and insights.

In the discussion this week:

• What it's like to come into the world of data with a non-technical background
• The impact visualization can make internally
• Cultural challenges of being "data-driven"
• Successful use-cases for data insights

Steven Totman, Chief Strategy Officer for Privitar talks with us about the ethical considerations we need to make with our data & analytics projects

In this week's episode of the Business of Data podcast, host Catherine King talks with Steven Totman, Chief Strategy Officer, for data privacy software company, Privitar. Together they walk through some of Steve's horror stories of ethics gone wrong, and what leaders should be considered in the world of data & analytics ethics.

In the discussion this week:

• Data quality & its impact on the success of data projects & products
• The impact of an agile approach
• Cultural expectations of location
• Data breaches and their impact
• Data Policies

This episode was brought to you in collaboration with Privitar!