The Business of Data Podcast (Business of Data by Corinium)

Ishita Majumda, VP, Data Analytics Platform at eBay, shares how the online retail giant’s strategy for establishing data-driven business practices has evolved in recent months

Data democratization has become a hot topic in recent years. Increasingly, enterprises want to empower non-data staff to use data-driven insights and embed data-driven business practices across their whole organizations.

For many companies, data democratization initiatives start with delivering programs to improve the data literacy of non-technical staff. But in this week’s Business of Data podcast, eBay’s VP, Data Analytics Platform, Ishita Majumdar, shares how this alone has not been sufficient to entrench data-driven business practices at the e-commerce giant.

As many companies do in the early stages of data transformation, eBay established an internal analytics university. It provides a series of courses taught by Majumdar’s team. But over time, it became clear this academy was not driving change at scale.

“Everyone attended the classes and ticked all the boxes,” Majumdar explains. “But they were also saying the product manager’s job [for example] is so complicated that, if they must make the time to write these very complex SQL queries, it becomes a two-person job. That’s when I suggested we take the data where the user is.”

Making Data Accessible for Non-Technical Staff

Rather than focusing just on upskilling non-technical staff, Majumdar and her team are now also working to simplify its analytics tooling and provide platforms that are easier for ordinary workers to use.

“It fell on my team to do more than just deliver platforms for the analyst and data scientist communities,” she says. “One of the areas I'm concentrating most on this year is democratizing data for the non-tech savvy community. How can we make sure we build tools and platforms that are easy to access, understand and create charts and visualizations?”

She adds: “We will modify our tools based on the user, rather than pushing the user to modify themselves. Tools should be easy to use. Nobody needs a Facebook tutorial.”

“There should be an abstraction layer to translate the queries,” she continues. “But, as a user, I should be able to click two or three buttons or write a simple English query.”

Of course, Majumdar is being mindful not to take functionality away from staff members who want to master more advanced analytics tooling. But it’s this combination of developing new self-service platforms for non-technical staff and improving data literacy via eBay’s data academy that will form the backbone of the company’s data democratization programs in 2022.

“There are many people who would still like to do their own abstraction, they want to go deep, which is great,” she concludes. “I’m not taking that option from anybody. But most people don’t want to deal with that. So as a platform team, we’ll take care of it and give you the interface and visualization engine which can cater to your needs.”

Key Takeaways

• Transform the culture around data. Many employees still see data as something used exclusively by data teams
• Do what works best for the organization. When eBay’s data academy didn’t yield the expected results, Majumda’s team reviewed its approach to data democratization
• Bring the data to the user. Developing self-service data platforms will make it easier for non-technical staff to harness data-driven insights

Allen Thompson, VP Data and Analytics at The Hanover Insurance Group, shares his advice for choosing which digital transformation projects to pursue and getting the right data foundations in place to ensure they succeed

Prior to COVID-19, the insurance sector was relatively slow to transform itself with data and analytics technologies. The pandemic has helped to change attitudes towards these investments. But executives will struggle to translate this newfound enthusiasm into business results without a clear strategy.

“Data is like the new toy everybody wants to play with, but they don’t want to read the instructions it comes with,” quips Allen Thompson, VP, Data and Analytics at the Hanover Insurance Group.

In this week’s Business of Data podcast, Thompson outlines how and why many insurance sector data and analytics leaders may benefit from going ‘back to basics’ and ensuring their companies have the right data foundations in place.

“Data is such a huge part of every company,” he says. “Without good data, you can't even get basic information about your business. You can't make good decisions. The foundational stuff is important, because what you don’t take care of upstream becomes expensive downstream.”

Laying the Right Data Foundations

Thompson believes there are three elements insurers must have in place to succeed with data and analytics: Internal data governance, third-party data governance and model governance. These pillars will dictate the ways an organization uses data, processes data and deals with other issues, such as data ownership, security and lineage.

Thompson argues that executives may feel the pressure to fast-track digital transformation projects based on pressure from company stakeholders or stories of advances that are being made at other companies. But he cautions against rushing to make technology investments without a clear picture of the value they will bring to the business.

“Companies spend a lot of money on technology, business intelligence, data scientists and information workers and they’re getting frustrated because things aren't happening fast enough,” he says. “I think this happens a lot because we really haven't focused on what problem we’re trying to solve.”

He acknowledges that the start of a transformation can be overwhelming but argues that understanding how data and analytics can support the organization helps to reveal the best path forward. The first step, Thompson says, is to roll-up one’s sleeves and work with company stakeholders to find valuable business cases for analytics.

“I advocate starting with an understanding of how the data strategy supports our company strategy,” Thompson recommends. “That’s how I prioritize what I need to fix. And a lot of times it's the basics – lineage, ownership and data quality. If you get those right, you can pretty much do anything down the road, but you have to roll up your sleeves.”

Key Takeaways

• Dealing with the pressure to transform. Legacy industries find themselves under  a great deal of pressure to transform their technology to make the most of emerging tech. But executives must ensure these investments will meet pressing business needs
• There is no data and analytics ‘magic pill’. Each organization’s data and analytics needs will be unique. The best way to develop your strategy is by working with business stakeholders to identify how data can best meet their needs
• Get the foundation right. Internal data governance, third-party data governance and model governance are the pillars that will manage how an organization uses and processes data

The co-founders of Black in Data join us to discuss why they founded a collaborative movement to promote equal opportunities and representation for people of color in data and analytics Our guests for this week’s podcast are on a mission. Devina Nembhard and Sadiqah Musa, both senior analysts at the British newspaper The Guardian, are co-founders of a newly-launched community, Black in Data, designed to accelerate the careers of people of color in data and analytics. Born of the turbulent events of 2020, including the murder of George Floyd in the US, Black in Data exists to provide mentorship, inspiration and a community to people of color seeking a career in data. “The idea is that we get data professionals of color together in one place to network, for example, to meet each other, to share ideas, tips, and hints about what they’re doing in their data world,” says Musa. “Overall, the idea is for us to increase ethnic representation within the data industry.” “I think it’s always been time for an organization like Black in Data,” adds Nembhard. “And it’s clear from people’s reactions when we invite them to the group that it’s something that everyone’s been really thirsty for.” Accelerating the Careers of People of Color It’s no secret that people of color are underrepresented in a range of professional and academic fields and in particular those that draw on graduates of science, technology, education and math (STEM). The trend manifested in a very personal way for Sadiqah Musa as she embarked on her career in data. “I had been working for well over 10 years and I had never worked with another black female. I just felt like I did not belong in any of the workspaces that I’ve been at,” says Musa. “And it’s not because of anything that I was doing wrong or anything that my colleagues were doing wrong. I’ve worked with some really amazing people. But something was just missing.” Black in Data provides a ready-made network for people of color to make connections, receive advice and support and even find employment opportunities. “If you want to access the diverse pool of candidates, you have to go to the right place, that’s why with Black in Data we have set up a jobs board,” says Musa. “We’ve got a fantastic group of people that are super qualified. We are here. Find us.” Providing Training and Mentorship Black in Data is about more than simply networking and finding new roles. Musa and Nembhard are also passionate about helping their members develop their data and analytics skills. “The mentoring, for me, I think is the part of the organization that I feel most passionate about,” says Musa. “I found when I started out I had nobody to reach out to, to ask questions.” She continues: “So, we are running a three-month mentoring program where we asked the mentors and the mentees to meet at least once every month for an hour. And it’s completely mentee-led.” “We offer a training program as well,” adds Nembhard. “The whole point of the training scheme is to give them those skills. Teach them SQL and Python, teach them advanced analytics, teach them how to visualize data. And then they can just make the leap into the data world a bit easier.” If you would like to join the Black in Data community, or if you are looking to support their initiative, you can find them at Black in Data. Key Findings Black in Data is a newly-created movement. Its mission is to support the careers of under-represented communities in data and analytics. It’s a place where you can develop your skills. From the ‘data visualization challenge’ to training and mentorship, Black in Data can help you develop your career. A ready-made community for people of color. It’s also a place to network, share tips and ideas, and make new friends.

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

Kulani Likotsi, Head of Data Management and Data Governance for a major South African Bank, talks with us about ensuring data privacy and security are at the heart of data processes

In this week's episode of the Business of Data podcast, host Catherine King talks with Kulani Likotsi, Head of Data Management and Data Governance for a major South African Bank, about ensuring data privacy and security are at the heart of data processes.

In the discussion this week:

• Collaboration with Data Security, Information Security, Fraud, and Audit
• The power of creating a "center of excellence"
• Governing data with the aim to generate business value
• Breaking down entrenched silos
• Working through the challenges of data breaches; understanding the processes in place

In today's episode, we’re diving into what it takes to connect businesses to the benefits of data and AI transformation and achieve AI readiness across an enterprise--featuring Catherine Masters of the insurance company Covea, and Joseph George of data consultancy Dufrain.

Jordan Levine, MIT Lecturer and Partner at Dynamic Ideas, outlines why he believes executives and regulators must do more to combat AI bias – and what they can do about it.

When the EU announced its proposed new AI legislation in April 2021, the bloc touted the new laws as a necessary step to ensure Europeans can trust AI technologies. But for Jordan Levine, Partner at consulting firm Dynamic Ideas, the proposals are something of a ‘blunt instrument’.

In this week’s Business of Data podcast, Levine argues that this kind of legislation is, at best, a starting point. It’s up to AI-focused executives to sit down and implement practical frameworks for ensuring AI is used responsibly in their organizations.

“I'm 100% supportive of the government getting involved in establishing the rules,” he says. “[But] I hope that both academics and business [and] society-conscious individuals get excited and say, ‘OK, how do we refine this?’”

In Levine’s experience, there are many things that can cause ethical issues when enterprises put AI or analytics models into production. That’s why much of the work he does at Dynamic Ideas is geared toward educating people about AI bias challenges.

He says it’s important for businesses to have both clear mitigation strategies to combat ethical issues such as biased decision-making and the right tools or technologies to orchestrate those strategies in practice.

“What I try to do is show how to mitigate those issues and then show actual techniques that exist today, [so] that you can leverage open-source software to do the processing,” he says.

Levine argues that business leaders must use a framework like the one he’s developed to make sure they are aware of the ethical issues that may arise from the ways they’re using AI and analytics. This will allow them to take steps to make sure these issues are addressed.

“I hope they can use this framework to actually challenge their analytics groups,” he says. “To actually sit down with the individuals writing the algorithms and confirming whether the issue does or does not exist.”

However, Levine concedes that no framework for combatting AI bias can ever really be complete. Technology is constantly evolving, and enterprises are constantly innovating with it. So, AI-focused executives must be vigilant and reevaluate their AI practices regularly with an ethics lens.

Levine concludes: “The more precise that we can get in terms of bias and ethics and the more, the more discrete issues we can identify and then think through how to mitigate them and show examples of mitigation, I think, the better we all are.”

Key Takeaways

· Regulatory compliance is not the same as ethical behavior.Enterprises must go beyond what’s required of them by law to ensure their AI practices are ethical

· Executives must be aware of potential ethical issues. If executives don’t know the specific risks that come with adopting AI technologies, they will struggle to ensure the right processes are in place to mitigate them

· AI ethics frameworks must be updated regularly. AI-focused executives must constantly reevaluate their AI ethics strategies to ensure their teams are following current industry best practices

Adebayosoye Awonaike, Head of Data at Legal & General Capital outlines his cloud transformation journey at the investment company 

Over recent months, investment firm Legal & General Capital has undertaken a cloud transformation journey to democratize data within the organization.

In this week’s Business of Data podcast, Legal & General Capital’s Head of Data Adebayosoye Awonaike, talks about his experiences leading this transformation, and the challenges faced along the way. The company’s data strategy requires readily available and usable data throughout the organization. 

He says: “We needed to centralize data, that’s really driven our conversations over the last few months because we had to shop across a range of suppliers. Ultimately, we decided we were going cloud because that’s just what would work for the business.” 

Finding The Right Partner for Cloud Transformation 

As cloud adoption continues across organizations and industries, finding the right solutions provider needs the transformation leader to be aware of their business’s unique needs. 

One of Awonaike’s initial challenges was in finding the best cloud partner to undertake the journey with. He based the decision on the alignment between Legal & General’s strategic direction and the vendor’s own roadmap and ambitions. Next, he focused on developing his team’s skills to maximize the benefit of the initiative. 

“It was really about building a team to deliver this solution, a team with a wide range of skills, from solutions architects to testers,” Awonaike says.  

He also cultivates his team's storytelling abilities to increase cross-functional engagement.  

“While a business product owner could reach out to just the scrum master, I would rather have the data engineer tell the story through his demo than for him to just do the technical bit,” he continues. 

Don’t Stop Reiterating Business Value 

Even with the cloud solution in place, data leaders still have to prove value in their investment. Awonaike finds that regular scrum sessions between his team and the rest of the organization ensure the transformation unfolds in line with evolving business needs.  

Awonaike explains: “Business requirements can be volatile. You can’t hear the requirements once and never check back in with business. You need to keep the engagement going, so we have two-week sprints that basically let us fail very quickly then get things right.” 

“No business leader wants to throw away money. So, the question is what value are you adding? If you're able to paint the picture of what value is, whether it’s tangible, in terms of revenue or intangible, there has to be a value the business can connect with.” he concludes. 

Key Takeaways 

• Finding the right cloud provider. The ideal solutions provider has to meet your business's unique set of needs 

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Justin Smith: In-between Transformations

In this week's episode of the Business of Data podcast, host Catherine King talks with Founder and Managing Director of data quality consultancy The Classification Guru about her personal journey into the data and analytics industry, and how she found her calling as a 'fixer of dirty data'.

In the discussion this week:

• The importance of fun in data culture
• Walsh's personal journey into the data and analytics industry
• The dangers of 'dirty data' and how to avoid them

This week, Vladimir Bendikow, CDO of FBN Bank Limited, joins us to discuss the proper preparation that is required to take self-service from ‘concept’ to ‘utopia’ – and how to avoid the most common implementation pitfalls.

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

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

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

Business of Data Podcast 

Business of Data Podcast

Syed Sameer Rahman, Director of Insight and Data Science at The Royal Mint, discusses how companies can start developing better business models with data

Over the course of his 17-year career, The Royal Mint Director of Insight and Data Science Syed Sameer Rahman has used data-driven techniques to solve a wide range of challenges. One of his key learnings in that time is that data is most useful when you look at it through the right lens.

This week on the Business of Data podcast, we invite him to share his views on why businesses must use the techniques to build propositions that are based on data-driven insights.

“You have a piece of data and you develop your business around that,” he explains. “That is what I call pivoting your business around data.”

Rahman gives the example of Klarna, the ‘buy now pay later company’, to illustrate how doing this successfully can allow businesses to identify and profit from gaps in the market.

He recounts how Klarna rose to prominence using the insight that the market for creditworthiness was drying up in the wake of the 2007 crash.

Its founders noticed that consumers still wanted to buy small things to cheer themselves up. So, Klarna identified a market gap – consumers with low risk of default who are interested in buying things now.

He says: “That’s a good example of [a company] using market insight, consumer insight and industry insight to identify the market gap and to develop a business out of it.”

To achieve this, he says enterprises must understand the business challenges they are trying to solve and build their data strategies around uncovering these insights.

Syed Sameer Rahman, Director of Insight and Data Science at The Royal Mint“The main barriers, I think, is really in understanding, in data literacy, self-awareness and triangulation”

He argues that insufficient data literacy is the greatest barrier to this kind of thinking in the business world today.

“One of the barriers really is the interpretation of data, which is linked to data literacy,” he says. “We see, quite often, people using data to manipulate data to get to the decision that they want.

“A good data person will help with triangulation, which is, they’ll look at the various different lenses that we have talked about and then come about in a very unbiased way to a conclusion.”

Key Takeaways

• Data helps companies find their competitive edge. Rahman says businesses should aim to pivot their business models around data
• Use the right data lens for the job. Business leaders should be clear on the problem they’re trying to solve to ensure they select the right analytical methods to solve it.
• Promote company-wide data literacy. Rahman argues that poor data literacy is the top barrier to data-driven thinking in business today.

In this week's episode of the Business of Data podcast, host Catherine King talks with Eugene Ras, Head of Data & Analytics, for multinational brewing and beverage company, Distell, about ensuring holistic data & analytics strategic alignment.

In the discussion this week:

• How to handle the curve balls of business
• Using strategic alignment as the catalyst for prioritization of projects and products
• How historic information impacts future strategy, post-pandemic
• Influencing each business unit
• Real-time alignment
• Data & Analytics Culture, and handling demand management

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

Business of Data Podcast

Thierry Grima, Group Chief Analytics Officer at ENGIE, reveals how he’s driving engagement with the company’s analytics strategy on the Business of Data podcast

Global electricity utility company ENGIE is transforming in more ways than one. Not only is it transitioning toward an operating model built on renewable energy sources, it’s reimagining its business practices for the age of data and analytics.

In this week’s Business of Data podcast episode, ENGIE Group Chief Analytics Officer Thierry Grima shares his experiences of leading the company on this journey and galvanizing staff around his analytics vision.

“We established a CDO community with 30+ members and we help them to provide data sources,” he says. “What we are here for, for me, is really to bring the ‘glue’ that will help people to connect together and see value in those connections to IT.”

For Grima, building awareness of what ENGIE is doing with data and how staff can use it to drive value for their own business units is an integral part of a Chief Analytics Officer’s role.

Driving Change with Gamification and eLearning

“Data is the new ‘sexy’, and it’s really important for us to ensure that our people know that, and they can actually play their role,” says Grima. “They all have to play a role.”

One of the most attention-grabbing ways ENGIE is driving awareness of its analytics strategy is through the creation of a ‘data game’.

The company created a mobile app that allowed people to challenge their colleagues to ‘duels’ where they answered questions about the company’s data and analytics initiatives. Winners were awarded points and competed for prizes on a company-wide leaderboard.

“Over three weeks we played something like 200,000 games,” Grima recalls. “It was really interesting to see how people were playing and gain some understanding of what ENGIE does with its data and how it helps to bring value to reduce cost, to find new revenue streams.”

Alongside the ‘data game’ the company is also delivering online training courses for staff across the organization to improve their data literacy levels. Grima says engagement with these courses has been particularly good throughout the pandemic.

Creating a Repository for Analytics Use Cases

Alongside working to raise awareness of what ENGIE’s staff can do with data, Grima’s team has created a ‘data marketplace’ to act as a central repository for the company’s analytics use cases.

“What you can find there is quite simple,” he says. “You find the definition of the program itself. So, what do we do? Why are we here? And what are the different parts that we cover from a strategy and organization standpoint, but also from a technology or data science standpoint?”

“We also gather all the use cases depending on their state in the lifecycle; so, the development state they are in,” he adds. “At the moment we have more than 300 use cases in this repository.”

“We share the use cases so that everyone knows exactly what other [staff are doing],” he continues. “But also, if they want to understand something, they [will] know exactly where it’s been developed already or anything that comes a bit close to what they want to build.”

Through breaking down the silos between ENGIE’s different business units in this way, Grima hopes to catalyze greater analytics innovation across the organization. His strategy is to couple this will his team’s broader data literacy and awareness initiatives to drive analytics adoption across the group.

Audrey Limery CEO & Founder of analytics platform Kweevo talks with Business of Data podcast host Catherine King about  her experiences of discrimination in the data and analytics community, and the impact they have had on her career

In this week's episode of the Business of Data podcast, host Catherine King talks with Audrey Limery CEO & Founder of analytics platform Kweevo about her journey into data and analytics, how racist and sexist experiences negatively impacted aspects of her confidence and professional development, and how she addressed these challenges and harnessed her passion for the industry to create something innovative.

In the discussion this week:

• Audrey Limery's journey into the data and analytics industry her early experiences with racism and xenophobia in France and the UK
• How micro-aggressions and unequal treatment in the workplace combine to create a toxic work environment for many people of color
• Limery's motivation and journey for creating her own start-up and the challenges she's faced along the way
• How Limery embraced doing what she loves to empower herself to create something innovative in the data and analytics space
• Advice for the next generation of Black leaders in data and analytics

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!

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

Adrian Pearce, Group Chief Data Officer at Credit Suisse, outlines how he balances consistency with flexibility while advancing his data strategy across the firm’s many and diverse business units

For organizations with tens of thousands of employees, getting everyone pulling in the same direction on data strategy can be a huge challenge. Orchestrating a group-wide vision of the future requires a delicate balance of consistency, transparency and flexibility.

In this week’s episode of the Business of Data podcast, Credit Suisse Group Chief Data Officer Adrian Pearce shares his approach to striking this balance to achieve the firm’s data strategy goals.

“If you’re overly prescriptive, you end up with 80% of the people telling you why it doesn’t work for them,” he says. “The challenge is being flexible enough while making sure you drive a common direction.”

Balancing Data Strategy Consistency with Flexibility

Today, Credit Suisse is focusing on three data strategy objectives: 1) fixing data quality issues and democratizing the data, 2) industrializing data management processes and 3) ensuring data is source from the right places and used correctly.

While these goals are simple, executing them is not. Pearce gives the example of the firm’s investment banking division and its retail operation in Switzerland to illustrate the differences between how the company’s many divisions and business units use data.

“In an organization like Credit Suisse, data isn’t the same for everybody,” he says. “The way we interact with both of those client sets is just completely different.”

“You have to do [things] in a careful way,” he adds. “You can’t change direction. You can’t come up with a bigger, better goal every 10 minutes. You need to really be giving consistent information.”

For Pearce, the key to success lies in balancing the “non-negotiable” steps toward achieving these consistent organizational goals with flexibility in other areas. This helps divisional CDOs to buy into these big projects without compromising their ability to serve the needs of their units.

To illustrate this idea, he gives the example of Credit Suisse’s organization-wide data quality initiative.

“We have a tool called Data Quality Issue Management,” he says. “It’s non-negotiable. Everybody has to enter their data quality issues in it.”

“We’ve managed to drive that consistently across the firm,” he continues. “By being able to explain to the organization the benefits of fixing [data quality issues], the individual CDOs of each divisional function have clearly bought into it.”

Key Takeaways

• Consistency is key in large enterprises. It takes time for a big ship to turn. So, data leaders should pick clear goals that aren’t going to change or move too much
• Don’t be too prescriptive. Group data leaders much allow divisional or functional data teams the flexibility to meet the needs of different stakeholder groups across the enterprise
• Secure buy-in for key strategic projects. Affording data leaders flexibility in some areas can make it easier to secure buy-in for ‘non-negotiable’ objectives that will have tangible business benefits

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

Frans van Bruggen: Preparing for the EU’s Artificial Intelligence Act

Callum Staff, Head of Data Science and Analytics at Marks &
Spencer, discusses his plan to build on his team’s successes
over the past 24 months and integrate data further with the
retailer’s business processes
At UK retailer Marks & Spencer, most conversations in the past two years
involving data and analytics have revolved around COVID-19.
For Callum Staff, Head of Data Science and Analytics at Marks & Spencer,
the global crisis meant putting major data science projects on ice to better
support the retailer’s supply chain. He considers the move ‘a real shift’ in the
relationship between business and data science at M&S.
“It showed the sort of support that we can provide; I want to further embed
the data and analytics team in key M&S functions,” he says. “We've got
tools that allow us to do that, and we want to turn them into fully fledged
software. It will help bake my operating model into any project.”
In this week’s Business of Data    podcast   , Staff talks about how he’s working
to build on the ties the pandemic-era has helped his team build with
business stakeholders to integrate data and analytics further into the
company’s operating model.
Making M&S Staff Sweet on Data
One of the ways Staff’s team is already helping to optimize M&S’s supply
chain is through space planning in stores. They provide insights to improve
demand forecasting and optimize inventory levels.
The data science team has also been involved in planning for a new law
regulating the sale of fatty, sugary and salty foods that will come into effect
in the UK this April. It will mean retailers can no longer place these foods at
store entrances and other main points. Advertising these products on TV
and online will also be restricted.

“Our data quality team's been involved in the conversations around this new
law from the start,” Staff says.
But Staff believes he must be mindful of his approach as he works towards
greater integration between data science and business teams. He wants to
ensure that the models and systems his team builds meet business needs
and integrate with staff workflows.
“It's all very well building a new dashboard or building a new machine
learning model,” he says. “But if you just plonk it somewhere and leave it,
it's never going to grow. We’re spending a lot of time thinking about how this
works day-to-day with people and where it impacts the steps they need to
take.”
Success will require constant communication and re-evaluation to ensure
Staff’s team’s priorities remain aligned with those of the wider business. But
Staff is confident that the greater cross-functional collaboration he’s seen
recently will help his team drive further organizational change in the coming
months and years.
“It’s really good to have the quants and the tech teams working alongside
sales and the traders,” he concludes. “It seems like a really good way of
working. COVID-19 showed me that, and hopefully it showed that to other
areas of the businesses as well.”
Key Takeaways
The pandemic has changed perspectives around data. COVID-19
helped Staff and his team show their colleagues how data science can
embed itself within the company and add value
Provide consultation on key business initiatives. M&S’s data team is
playing a strategic role as the organization reformulates thousands of
products to meet the UK’s new dietary regulations
Focus on alignment and adoption. Staff is keen to ensure his team is
developing the right tools and capabilities and that these can be integrated
effectively with staff workflows

Paul Lodge, Chief Data Officer for the Department for Work and Pensions in the UK joins us to chat about what it takes to keep a country running with data

In this week's episode of the Business of Data podcast, host Catherine King talks with Paul Lodge, Chief Data Officer for the Department for Work and Pensions. Together they discuss how data made an impact on some of the UK's biggest events in the last 10 years, including the Grenfell Tower tragedy, Brexit, and the covid-19 pandemic.

In the discussion this week:

• Connecting the dots between business and data
• Surveys and official statistics
• Moving quickly and effectively through times of stress and change
• Data security, privacy, and fraud

Louise Maynard-Atem, Data Insights Lead at GBG, shares her tips on implementing agile methodology to drive innovation in the wake of the pandemic

Born in the realm of software development, agile methodology has been growing in popularity across a wide range of business functions in recent years.

In this week’s episode of the Business of Data Podcast, Louise Maynard-Atem, Data Insights Lead at identity verification, location intelligence and fraud prevention company GBG argues that an iterative, collaborative approach to data and analytics will help to drive innovation and demonstrate business value as we emerge from the pandemic.

“[Agile] helps us innovate faster. It helps us to surface the problem quicker and utilize data more effectively,” says Maynard-Atem. “But it wasn't until we really had to put agility into practice quickly, because necessity meant that we had to, that we realized the importance of it.”

The Pandemic Highlighted the Importance of Business Agility

If there’s one thing we’ve learned in the last 12 months, it’s that you never know when you might need to transform the way your business operates.

“Agility really is king. It’s king because you never know when you are going to have to make a pivot, make changes to your business model, make changes to your ways of working and make changes to what you're doing with data,” says Maynard-Atem. “It’s taken the global pandemic, I suppose, to really bring the need for agility into clear focus.”

The advantages of rapid action in a turbulent market have highlighted the advantages of agile thinking to business leaders.

“I think it wasn't until we had to put agility into practice quickly, because necessity meant that we had to, that we realized the importance of it,” says Maynard-Atem.

Driving Innovation with Agile Methodology

However, as things begin to settle, Maynard-Atem says that agile thinking and, more specifically, agile methodology, will drive innovation in data and analytics.

I think innovation, agile thinking and agile practices go hand-in-hand because innovation is ultimately [about] trying to do something new,” says Maynard-Atem.

She continues: “We want to make sure that we're not just taking a waterfall approach. We're taking small incremental steps and pulling in the feedback loops – and that’s ultimately what agile teaches you.”

However, for organizations used to long development cycles and multi-year digital transformation initiatives, the fast-paced iterative nature of agile can seem like an unlikely partner.

“It seems as though a lot of organizations feel like they're under pressure to deliver a big transformation program, but I don't think that's the best way to deliver in terms of data and analytics,” says Maynard-Atem. “And certainly not from an agile perspective.”

Instead, Maynard-Atem recommends looking for manageable, well-defined experiments to test hypotheses, and pulling in feedback loops.

“It's just breaking it down to those manageable chunks and being really specific about what each experiment is going to deliver, what that value means, and then how [you will define] success,” she says.

Key findings

• Agility was a critical success factor for businesses during the pandemic. As companies rapidly pivoted their operations, the ability to think and act quickly was a key differentiator.
• Agile methodology empowers innovation. Experimentation and rapid iterations are the hallmarks of both innovative thinking and agile methodology.
• Break down large initiatives into manageable chunks. By looking for the smallest experiment possible you can demonstrate value more quickly.

Nedbank Executive, Group Data Services Paul Morley shares his experiences of building company-wide data communities and how he believes organizations can benefit from them

For companies to truly embrace data and analytics, data leaders need to move conversations around data literacy beyond their departments and into the broader organization.

For Paul Morley, Executive, Group Data Services at South African bank Nedbank, one of the best ways to build data literate organizations is to make data conversations a part of daily business operations.

In this week’s Business of Data podcast episode, he shares his experiences around building organization-wide data communities and how companies can benefit from them.

“I focus a lot on internal education and collaboration to build awareness and create enthusiasm around data,” Morley says. “I probably spend about two or three hours a day doing just that.”

“If I could do more, I would,” he adds. “It's very important to make people who don't understand data understand it because, for an organization, working with data is like a team sport. As much as we might naturally want to focus on just the data team, it’s actually not about us. It’s about taking the whole company with you and inculcating that knowledge.”

Three Tips for Promoting Data Literacy

Gartner’s 2020 Execution Gap Survey found that 67% of employees don’t understand their role when new growth initiatives are rolled out. To address this challenge and drive the value of data literacy all the way to the grassroots, Morley recommends the following:

• Embrace repetition. The only way to instill a data literate business culture is repetition, repetition, repetition. It’s like playing a new sport, Morley says. It takes practice to get good at it
• Appoint the right leaders. Morley says it’s important for data leaders to cultivate good communication skills. He attributes his own success in part to his natural extroversion, and recommends building teams full of leaders who are strong communicators
• Get down in the trenches. Morley encourages data leaders to spend more time talking to the people at the coalface than sitting in leadership strategy sessions. If frontline staff don’t buy into the journey, you’re going to fail. With them on-side, you’ll have a better chance of enacting the change you’d like to see

Be Mindful of the Headhunter Threat

Building a strong internal data community may be about more than those working directly with data. But the challenge of finding and retaining staff with the right skills persists. Talent poaching remains a reality for many companies.

Morley explains: “Two other local banks are actively hunting our employees. We’ve lost about 30% of our staff this year alone in our group, across professions. It is concerning and it’s something we’re discussing at the executive level. But we’re also still attracting a lot of new blood; that’s testament to Nedbank’s culture.”

Seeing off this threat is about making your company as attractive a place to work as possible. Morley says providing opportunities for training and personal development has a role to play, here

‘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

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

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’

BoD Pod

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.

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

Danone Global Data and Analytics Transformation Director Camilla Schwartz-Björkqvist explains how she is creating the next generation of data and analytics evangelists at Danone

Large businesses have digitized rapidly over the last few years. As a result, the data and analytics function is more important to business success than ever.

However, some people in businesses of all sizes are still skeptical, even fearful, of the ongoing information revolution.

In this week’s episode of the Business of Data Podcast, Danone Global Data and Analytics Transformation Director Camilla Schwartz-Björkqvist argues that to address the fear, businesses must help their staff see the benefits of digital transformation by improving their data literacy.

“There's this population, I think, that's in every company [that] will have a healthy skepticism,” she says. “I think the first challenge is [addressing] the skepticism and the fear.”

She continues: “You have to make data and analytics accessible to people [because] it creates a lot of fear in the organization when you hear ‘oh, we're going to automate, we're going to introduce machine learning, and AI is coming.’”

Raising Awareness About Data and Analytics

A key part of the transformational work that Schwartz-Björkqvist is doing at Danone is getting more business partners interested in data and analytics.To do this, she created a ‘data bar’ on Danone’s internal social media where she could share educational data and analytics content.

“That was one of the key cornerstones of what we did first, [creating] a space where people could come and find us, and find information that they're looking for,” she says. “We wanted to create a place where people wanted to come and hang out.”

At the data bar, team members at Danone can listen to podcasts, do some light reading, or even take a masterclass on key data and analytics topics.

“When I set up the first masterclass, I was expecting we would have 20 to 40 people attend, but we had 200!” she recalls.

Creating Unique Training Journeys

While raising awareness about the positive benefits of data and analytics is an important first step, realizing those benefits requires raising the level of data literacy across businesses.

For a company with tens of thousands of employees like Danone, this is an considerable challenge. Schwartz-Björkqvist realized that she would need to create a platform that could provide bespoke training journeys for staff regardless of their geographical location or seniority. Thus, Danone’s data academy was born.

“The data professionals will get the deep expertise training they need around data governance, and around data science,” she explains. “We’ll have the Python training, and we’ll have really in-depth cool stuff where they also get certified externally – so that is that little extra spice.”

Of course, Danone, has a large population of staff who are not data professionals. The data academy has a course for them too.

“We'll be taking them through a combination of e-learning and workshops depending on where you are in the organization, and how much we believe that [they] will be impacted by data enablement,” she says.

She concludes: “Of course, let's not forget the executives, they need to they need to get it – they need to really grasp it – so they are the third population.”

Stefanie Costa Leabo, Chief Data Officer for the City of Boston, shares how her team led an open data project to help manage the impact companies like AirBnB are having on the city’s property rental market

Short-term rental companies including AirBnB have transformed housing markets across the globe. But while many tourists and property owners have benefited from these services, they have also made life harder for long-term renters in some parts of the world.

As Stefanie Costa Leabo, Chief Data Officer for the City of Boston, reveals in this week’s episode of the Business of Data podcast, her team is playing a key role in managing this phenomenon in the city.

“In certain parts of the city, properties were being brought up by large developers and they were being run as almost de facto hotels,” Leabo recalls. “[That’s] problematic for a couple of reasons. One is that there’s a reason that hotels are regulated and have to hold certain licenses.”

“There are health and safety standards that we apply to businesses and those regulations are there for a reason,” she continues. “The second issue is that it was changing the character of neighborhoods and taking long-term housing out of the housing market.”

The city decided it needed to find the right balance between allowing some to generate an income from their spare rooms or properties and ensuring long-term property is available to rent for its residents.

So, it decided to set new regulations for short-term renters in 2018. Then, it enlisted Leabo and her team to deliver an ambitious open data project to assist with the enforcement of these new rules.

An Open Dataset for the Boston Property Market

Involving Boston’s data office in this project from the outset was pivotal to its success. Leabo’s team collaborated with other departments to evaluate what impact short-term rentals were having on Boston’s housing market. Then, it created a new open data portal that the legislation mandated.

“The first thing we needed to do was create a new datasetfrom scratch that would determine the eligibility of every single residential housing unit in the entire city,” Leabo says. “There were three different types of license. So, we had to be able to tell, ‘Is this unit eligible for each different type?’”

“We were brought in from day one and were able to be part of the policy discussion, be part of the implementation and enforcement team”

Stefanie Costa Leabo, Chief Data Officer, City of Boston

This project involved gathering data from at least six city departments including inspectional services, public works and non-emergency services hotline 311. These were then merged to create a unified short-term rental eligibility dataset.

“Many of these data sources had never been joined before,” Leabo notes. “At least, not at such scale.”

“The biggest challenge, that we both saw coming but also surprised us at times, was data quality,” she adds. “[When] working with data that is being managed by six or seven different departments, you have different levels of data quality [and] different levels of data standards, in some cases.

Ross Simson, Global Chief Data Officer at CDP, shares how he’s advancing the non-profit’s data strategy and tackling the unique challenges facing charity sector data leaders

With climate change in the spotlight following the UN Climate Change Conference (COP26), Ross Simson, Global Chief Data Officer at environment-focused non-profit CDP, joins us this week on the Business of Data podcast.

In this episode, he talks about developing a successful data strategy at CDP, building a data team with the right skills and the need for a global standard for data management accreditation.

Futureproofing CDP’s Data Strategy

CDP helps organizations and governments measure and disclose their environmental impact. To develop CDP’s data strategy, Simson had to take a holistic approach that considered a range of factors, including people, resource allocation and the business environment.

“Being a charity, and as we get more organizations disclosing to us, we can’t just add more staff,” Simson explains. “We have to invest in technology where we can automate certain tasks and still use data science and machine learning.”

One of the methods Simson uses when planning CDP’s data strategy is McKinsey's Three Horizons Model. The model aims to help businesses manage innovation and growth goals by grouping initiatives according to when they will come to maturity.

He says: “We’re facing this dilemma where you've got horizon thinking, but everybody wants everything done now. For example, ‘data-driven’ seems to be the newest buzzword. But to be a truly data-driven organization, you need to understand the value of data. We have all these tools and technologies, but we aren’t well organized where data’s concerned.”

“One of our challenges now [at CDP] is that we’re updating our data model,” he continues. “But there are 15 global standards that we can adhere to and each of those can have up to 25 data points. So the question becomes, how do you create a model which underpins not only what you need now, but for the future?”

“You want to develop a data strategy that’s as futureproof as possible,” he adds. “The past year has shown us that you can’t ever be truly futureproof. We must become more nimble [and] change the way we think because, when it comes to technology, one size doesn’t fit all.”

Investing in Data Team Talent

Simson would like to see a common global standard for accreditation in the various data fields, as well as a focus on nurturing young talent. While there are many organizations working on creating global data standards, he says the industry needs to focus more on accreditation.

He chairs the Customer Data Council in the UK, an advisory arm of the Data and Marketing Association. The association provides recognized accreditation and training in areas such as data management. The challenge going forward, he says, is in setting a global accreditation standard for the va

Booking.com Director of Data Science and Analytics Guy Taylor shares his tips on scaling data and analytics initiatives from solid foundations and developing a sound data culture

Scaling data and analytics initiatives successfully can be a challenge - even for businesses with a rich data culture.

In this week’s episode of the Business of Data Podcast, Booking.com Director of Data Science and Analytics Guy Taylor argues that scaling such initiatives successfully relies on strong data foundations, tying data and analytics initiatives to business incentives, and understanding the unique data context of your organization.

“One of the big learnings that I’ve had is that having his kind of cookie-cutter one-size-fits-all strategy really doesn't work,” Taylor says. “It’s really important to understand your current state and your current context. I think that is the thing that I’m pointing to which I hadn't fully taken into account. Context is absolutely everything.”

How Data Culture and Data Context Interact

To scale data and analytics initiatives successfully, Taylor recommends developing a data culture that focuses on breaking down traditional silos and democratizing data use.

This can be a challenge for many organizations, especially given that data contexts vary widely across industries.

“It all comes back to the culture,” Taylor explains. “In the banking environment, for example, because of the regulation and because of the way that data is really considered to be a key asset. What you see is power dynamics built up around data fiefdoms and people really wanting to hold on to control of the data.”

He continues: “What I’m seeing in the start-up culture, with its culture of high growth and rapid acceleration is the exact opposite. It’s that everybody has access, and everyone can do everything withing the regulatory frameworks that do exist.

Building on Strong Data Foundations

The work of building a strong data culture and shoring up data foundations never stops. Indeed, because the data landscape is constantly evolving so to must data culture constantly evolve.

However, striking a balance between driving value through data and analytics initiatives while continuing to build strong data foundations can be tricky.

Taylor says that communicating effectively with key stakeholders on the importance of solid foundations to the ultimate success of an initiative is imperative.

“It’s about figuring out what the incentives are. Because without aligning with those objectives, you’re dead in the water,” Taylor says. “You need to figure out what the incentives are on a business level, what the incentives at a social level, and what the incentives at a personal level and align to those.”

He concludes: “If you can figure out how you can inject your ‘how’ into their ‘why’ then you're both winning.”

Key Takeaways

• Data culture is everything. The success, or failure, of data and analytics initiatives relies on a democratized data culture.
• Build your data foundations. To scale initiatives successfully, data and analytics initiatives must be built on solid foundations.
• Align your ‘how’ with their ‘why’. Demonstrating how data and analytics initiatives will achieve business goals is the best way to win support.

Karine Serfaty, Chief Data Officer for The Economist joins us to talk about what it takes to mature a data culture successfully.

In this week's episode of the Business of Data podcast, host Catherine King talks with Karine Serfaty, Chief Data Officer for British Newspaper, The Economist. Together they discuss what green-field opportunities Karine was able to exploit when she walked into her role in 2020, and how she's setting herself up for data cultural maturity.

In the discussion this week:

• The importance of strategic direction
• Mindset & growth
• Lessons learned
• Data as a mission-critical component
• Pros of not having a traditional  data background

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

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

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

Goldman Sachs Executive Director Lin Yue is a first-generation immigrant to the UK. She shares how her experiences in a foreign country have shaped how she applies data to business challenges

This week’s Business of Data podcast is unlike previous ones. Where we usually hear from data leaders, in this episode we hear from a business stakeholder who uses data heavily in her role at Goldman Sachs.

Lin Yue works as an Executive Director of UK Institutional Business at Goldman Sachs Asset Management. As a first-generation professional working in the UK, she believes her cross-cultural experiences have helped her develop a unique perspective on solving business issues with data.

Citing a Harvard Business Review study, Yue says that almost half of the companies in the Fortune 500 were founded by immigrants or their children. Yue says this ‘outsider mindset’ helps people see situations as easily changeable and not ‘set in stone’.

“There’s value in being an outsider!” she argues: “If you think about what's driving innovation around the world, very rarely do we hear of a brand-new idea. Innovation is what you get when you look at things from a different perspective.”

Understanding Global Markets with Data

Yue goes highlights some of the major growth disruptors she helps investors navigate with data, such as generational gaps between consumers, and how they influence the way organizations perceive their markets.

“Millennials and Gen Z are coming into society’s highest income years,” she notes. “Their consumer behavior will be different to previous generations’. They're willing to try more things and they're much more focused on the sharing economy and on having experiences.”

“Look at China,” she adds. “Its 400 million millennials are the largest generation, whose aggregate income has exceeded the previous generation’s average. [Three quarters] of consumption in the country will be driven by them by 2025. But companies are not adapting to these behavior and consumption patterns because they think those millennials are still too young and that they don’t have money. That is all very out of date because this group is defining the consumer landscape.”

“Let’s use [luxury fashion brand] Burberry as an example,” she continues. “In the West, its typical first-time buyer is probably in their late 40s or 50s. Whereas, in China, that first-time buyer is in their 30s. So, if a company doesn’t understand something like this, it would already be failing in that market.”

For this reason, Yue says it’s vital that global enterprises make use of company and third-party data to understand the markets they operate in. These insights should then be used to optimize their business strategies in each of these regions.

“Companies usually join a new market and use the same product or service [they offer] in other markets,” she concludes. “They believe one product is enough. But maybe, because it knows it isn’t the dominant culture across the world, Chinese companies tend to start by adjusting the offering for each market. It’s a difference in mindset regarding the way data’s used.”

Key Takeaways

• There’s value in being an outsider. Innovation happens when people bring fresh perspectives to old problems
• Let data lead. Applying data to business problems can help companies optimize their strategies to market conditions
• Follow China’s example. Using data to tailor products and services for different markets can drive better company performance

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?

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

Business of Data Podcast 

Roger Halliday, Chief Statistician for The Scottish Government, shares how public service organizations across Scotland are ‘joining the dots’ between their datasets to help the most vulnerable members of society

In the UK today, much of the data public sector organizations collect about British citizens lives in silos. But as pioneering countries such as Estonia have shown in recent years, governments can greatly improve the quality and efficiency of the services they provide by breaking down those silos and working toward a 360-degree view of their citizens.

In this week’s Business of Data podcast episode, Roger Halliday, Chief Statistician for The Scottish Government, talks about the work he’s doing to help Scotland provide better services to its citizens with data.

“I’m responsible for whatever numbers come out of public bodies across Scotland,” Halliday explains. “There are 40 or so organizations, from schools to prisons to the health service and so on.”

“I’m [here] to tell the story of a nation in an objective way and in an open and transparent way,” he continues. “I’m responsible for making sure that the numbers are trusted, that they’re high quality and that they’re actually used to improve the lives of people and improve decisions that are taken.”

Two Ways Scotland is Improving Society with Data

The COVID-19 pandemic is one obvious example of how curating and sharing valuable datasets can help governments provide better services and make more informed policy decisions. Indeed, Halliday says this has been a significant focus for him over the past 18 months.

“[For] the last year, for example, I was leading up the COVID-19 analysis team for the Scottish government,” he says. So, we were modelling the epidemic, getting evidence together for the difficult decisions that governments around the UK [and] the world have had to make.”

But Halliday also highlights an initiative geared toward providing essential services to homeless people to illustrate some of the more strategic ways Scotland’s government is harnessing the power of data.

“We’ve been collecting data on homelessness for many years,” he says. “When [we] put it together, we found that 8% of people in Scotland have been homeless at one time or another over the last 15 years.”

“We thought, if you put that data together with other bits of information, then maybe we’ll be able to better help people who are in that situation,” he continues. “So, they’re able to link that data on homelessness with data on the health services that people that are homeless receive and, not surprisingly, found that [these] people have difficulty accessing health services and that their health is a lot poorer.”

Through analyzing these connected datasets, Scotland’s public service organizations have developed new ways for people to access key services they might otherwise have struggled to access if they were homeless.

Perhaps more interestingly, they have also identified ‘trigger events’ that frequently cause people to become homeless. This is helping them develop ways to predict which citizens are at risk

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

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

In this special live Business of Data podcast, Karen Ambrose, Research Data Services and Database Team Lead at The Francis Crick Institute, appears on stage with Catherine King at CDAO UK to discuss the important role of data in science

Listen now to hear more about:

• How data regulations impact the way data is used collaboratively at The Francis Crick Institute
• What processes are in place to balance data security while allowing research to continue
• The diversity. equity and inclusion strategies the institute has established to make science richer at 'The Crick'

Business of Data Podcast 

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

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.

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.

Pete Williams, Director of Data and Online at publishing company Penguin Random House UK, shares how he’s learned to balance scarce data team talent with reaching business goals

Pete Williams has held many data and analytics leadership roles since 2013, in various sectors. But a common challenge across all of them has been the need to manage finite data team resources effectively.

In this week’s Business of Data podcast, he shares his approach to doing this in a way that balances working towards an organization’s long-term strategy with meeting its short-term, tactical goals.

“I’ve been guilty of taking on more work than my team and I could handle in the past,” Williams explains. “You take it on without realizing that little thought has gone into what you actually need to do to achieve something. And before you know it, your scope explodes, and you can't deliver.”

How Williams Prioritizes Data Team Projects

Williams says his approach helps to deal with what he calls ‘wild card’ projects. These are unexpected projects that must be prioritized and can only be delivered by either supplanting the team’s current workload or bringing in more resources.

Since data team resources are finite, Williams recommends managing them by dividing the team’s work between providing ongoing operational and strategic support for the business and research and innovation.

He argues that the ability to weigh all potential projects according to a common scale is also vital. Creating a universal template for evaluating potential analytics projects both helps company stakeholders understand the data team’s workload and creates a fair system for deciding which projects to deliver first.

“A common assessment template gives everybody a chance to pitch for the team's scarce resources,” he explains.

An effective project evaluation template should ask questions such as: Will the project generate revenue? Is it cost-efficient? Will it save time? Does it serve an environmental purpose? This will help company stakeholders to agree on which projects should move forwards and which are less likely to drive business impact.

Key Takeaways

• Anticipate ‘wild cards’. Data leaders must allocate team resources to strike the right balance between delivering long-term strategic projects, providing short-term tactical support to business units and working on ad hoc ‘wild card’ initiatives
• Use a common scale to weigh projects. As more projects come in, it will be harder to prioritize them. All work should be considered on the same merits
• Define what success will look like. Data and analytics project proposals should include defined outcomes that both demonstrate the value they will bring to the business and enable comparison with other projects in the pipeline

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

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

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.

Lloyds Chief Data Officer Simon Asplen-Taylor shares his tips on realizing the potential of data to drive business decision-making

When putting together their strategy to become more data-driven Lloyds did something unusual. They published it.

In this week’s episode of the Business of Data Podcast, Simon Asplen-Taylor, Chief Data Officer at Lloyd’s, argues that the publication of their data-driven strategy was an essential first step to turning theory into practice.

“When you're doing something across a market, what you have to watch out for is that everyone understands what the overall strategy is, so we wrote a data-driven strategy called Blueprint Two,” says Taylor. “And that's unusual. I think most data strategies are internal. This is very much external.”

As they refined the strategy, Taylor and his team crowdsourced feedback using a tool to promote engagement, encourage feedback and create a more complete product. The result was a document that accounts for the priorities of multiple stakeholders in the Lloyd’s universe.

“If I said to you, ‘I know the answer’ to something, you might well then start questioning me, but if we work together on an answer, it feels a bit more inclusive,” Taylor says. “You have to be prepared to learn new things and understand that there may be challenges you didn't know about.”

Building confidence in the initiative is another crucial step towards success. To do this, Taylor recommends focusing on overall objectives, especially if the person is non-technical. It’s a process that Taylor compares to watching a movie.

“[When] you watch a [movie] you don't necessarily know how it was all put together. But if someone forced you to watch the ‘making of’ the movie before you watched it, then actually it wouldn't be so exciting,” Taylor quips. “Start with the story and explain it in their language.”

Key Findings

• Collaborate on your strategy. Getting feedback and engagement from key stakeholders will build trust and produce a better result.
• Take a consultative approach to implementation. Understanding what drives your stakeholders may uncover issues you hadn’t considered.
• Speak the language of your audience. If CEOs care about increasing revenue, reducing costs, and improving customer satisfaction, then tell them how your initiative will achieve that.

In this week's episode of the Business of Data podcast, host Catherine King joins Sean MacCarthy, VP of Analytics for Good Sam & Camping World on stage at CDAO Chicago 2022 to talk about data in reality

In the discussion this week:

• Hiring from different backgrounds to create a successful team
• Creating an environment to grow talent
• What it takes to deploy the latest trends
• The biggest challenges standing in the way of data quality

Andrie Galaktiou, former Head of Data at John Lewis Partnership, discusses the role she played in establishing John Lewis’ data management team during her 13-year tenure at retail giant For a retail giant like John Lewis Partnership, the growth of digital commerce in recent years has created big opportunities to drive sales and optimize processes with data and analytics. But as former John Lewis Partnership Head of Data Andrie Galaktiou says in this week’s Business of Data podcast episode, few in the industry are in a position to take full advantage of these opportunities. “It’s exciting, because I think the retail industry’s definitely moving forward,” she says. “What we need to understand is, the foundational pieces need to be done first, before you can start to think about being innovative.” After starting her career at John Lewis in its merchandising department, Galaktiou moved into the company’s data function and played a key role in establishing its central data management team before starting a new role at Publicis Groupe in September 2021. Kickstarting Data Investment at John Lewis While the Global Financial Crash kickstarted data governance investment in the financial services sector, the retail industry has had no parallel event to kickstart its path to data maturity. As a result, it’s taken longer for retail executives to prioritize strategic data investments. “The first [challenge] will always be getting the buy-in and the funding within that space,” Galaktiou says. “Sometimes it helps to start things small or start things with specific projects or pieces of work.” “If you don’t understand what your stakeholders need or want, you’re not always going to get the right results from them” Andrie Galaktiou, former Head of Data, John Lewis Partnership “It’s really hard to try to move forward with some of these things if people don’t get it,” she adds. “So, educating and training the business on what data is, how it works, what it can do for them, is absolutely key and one of the biggest challenges.” For Galaktiou, starting with key programs that showcased what data could do for John Lewis helped her to securing buy-in for further investment. She also believes her background in merchandising meant she understood what mattered to stakeholders in the business. “Having worked in that space and really understanding how systems work from their perspective – how they use the data, what they need to do with it – made it a lot easier,” she argues. Building a Data Management Team for the Retail Sector Once Galaktiou and her colleagues had secured executive support and signed off the company’s data strategy, her focus became building a team with the right skills to drive those plans forward. “I don’t believe there is a textbook answer for what a data team should look like, what the roles should be and what people should do,” she says. “You’ve got to really understand your organization, bring in the expertise from the organization as well as the expertise from understanding data and data management to really drive that forward.” “Providing you’ve got the backing and the funding to do it, it’s about really understanding the business and where the business is at and where it’s going” Andrie Galaktiou, former Head of Data, John Lewis Partnership “For me, it’s [about] finding people who can speak the data language, who can really understand the business,” Galaktiou continues. “The data team often sits between the business and technology, and so they have to speak three different languages. You need to be able to find people who can speak all those languages and really bring it together.” She adds that ensuring data team roles have enough breadth for staff members to work on a variety of projects and choose the projects and focus areas that interest them most is important for staff retention. “It doesn’t all just happen really quickly,” she concludes. “Getting the people in place is one step of that process. You’ve then

Richa Sachdev, Head of ML Engineering at Vanguard, discusses her approach to ensuring ML models are developed ethically and used responsibly

As organizations get to grips with the practical issues around ensuring AI and ML is used ethically, a lot of effort needs to go into helping business stakeholders understand these technologies. In this week’s Business of Data podcast, Richa Sachdev, Head of Machine Learning Engineering at investment firm Vanguard, shares how she’s ensuring her team puts ethical data at the center of its strategy.

Principles for Ethical Model Development

Sachdev’s team’s primary role includes  developing recommendation systems for funds and using data to track customer interactions to support Vanguard’s sales and marketing functions. For Sachdev, doing this ethically means focusing on issues such as privacy, explainability and bias.

“As engineers, we can be proactive about governance by redacting unnecessary information when we’re creating a model,” she says. “Of course, we don’t want to redact everything because the model will lose value. But I don’t need a person’s Social Security number, their religion or their criminal history.”

“We have to ensure that we are not introducing any known or unknown bias in our model baseline,” she continues. “There are a lot of statistical tests that are available in our toolkit for training or testing models. So when we get the outputs, we can compare results to see if something applies to a general population or just a small sample to avoid problems downstream.”

Everyone is Responsible for Using AI Ethically

Sachdev is proud of the strides her organization is making towards data analytics maturity. While there are still departments that don’t understand analytics function, many are making the most of it.

Leveraging analytics cannot be a standalone function, she says. But at the same time, everyone who uses AI within a business has a role to play with respect to ensuring those systems are applied ethically.

“There isn’t a single party that can ensure that everything goes well with ethical data,” Sachdev notes. “Achieving this should be part of the CDAO’s strategy and part of leaders’ key responsibilities. Everything should be connected by a common thread.”

She concludes: “I was in an internal conference, hosted by my department and the data and governance department, where we discussed what ethical AI really is. A lot of deliberate work needs to go into bringing everyone to the party.”

Key Takeaways

• Consider the ethical implications of each use case. Behaving ethically will often require data scientists to redact unnecessary personally identifiable information (PII) or build explainability into models
• Proactively combat ML bias. Enterprises should develop processes to search for and remediate the many kinds of bias that can lead to unfair model outputs
• Everyone is responsible for using AI responsibly. Stakeholders much be educated on how to get the most out of AI systems and how to do so ethically

HSBC is modernizing its fraud and money laundering detection capabilities by rolling out algorithms designed to identify ‘bad apples’ in the most efficient way possible Digital transformation is not unique to businesses. Since the onset of the COVID-19 pandemic, criminals have also moved their activities online. Now, HSBC is using data to fight back. In this week’s episode of the Business of Data Podcast, HSBC’s Head of Data and Analytics and Business Financial Crime Risk Francisco Mainez explains the motivations behind this global initiative. “We need to find something that will divert our attention to the customers that we really wanted to analyze. The ones that have the potential [to be] the bad apples in your basket,” says Mainez. “And also, that’s going to have a knock-on effect on cost efficiency.” Using Data to Identify High-Risk Customers Historically, customer-focused assessments looking for fraud and money laundering might involve the manual review of thousands, if not tens of thousands of people and accounts. This process was costly, inefficient and time-consuming. By employing an algorithm to give individual customers a personal score based on predetermined risk factors, HSBC can quickly identify high-risk accounts. “What the algorithm does is embed different key risk indicators,” says Mainez. “Are you moving countries? Are you transacting with virtual currencies? Are you over a certain age?” He continues: “In the old world, we will be looking for things that we know for a fact from previous experience that could be suspicious. With this [algorithm], you’re scoring customers because you’re actually measuring customer behavior.” Improving the Efficiency of Fraud Investigations By using data to identify the high-risk accounts, HSBC is making sure that their investigative resources are being used as efficiently as possible. “You don’t want to spend 80% of your time, energy, budget and resources, especially on the operation segment, checking a false positive. Then 20% of the time rushing to find out if those customers are the ones that you’re looking for,” Mainez says. “We wanted to reverse that.” He continues: We’re going to spend minimal time because the machine is going to help us make a decision on which customers we need to review. And then we’re going to spend the rest of the time properly analyzing the customers.” Mainez points out that this initiative is designed to assist human decision-making, not replace it. The human element of fraud detection is still essential, especially when it comes to adapting a global initiative to local realities. Taking Stock of Cultural Factors Big financial institutions typically work in a very decentralized way. To make this global initiative successful, Mainez knew that the algorithm would have to take account of local and cultural factors. “You also need to take into consideration cultural factors, he says. “Every country is going to have to worry about their own typologies, not the ones from [any other] country because that’s going to produce false positives that they’ll have to review”. By asking individual regions to specify the cultural or regional typographies that best indicate risk, Mainez can tailor the algorithm to that region. “You’re going to tell me which are the typologies that are keeping you awake at night,” he says. We want to help you by configuring the system in a way that can detect those types of behaviors.” The new initiative has already been rolled out in several regions, but the future has plenty in store for Mainez and his team. “Over the next few months, we’ll be deploying in more markets and continually tweaking those typologies,” Mainez says. “Because of all possible times, we started to roll this out in the middle of COVID.” He concludes: “[Criminals] are adapting to a more digital and remote environment. That’s reflected in the data, and we need to be able to figure out how those typologies, and how they are evolving.”

Natasha Gray, VP, Head of Data and Analytics at ViiV Healthcare, shares how democratization and people are underpinning ViiV’s data-driven transformation

Enterprises that successfully leverage data and analytics have been found to outperform their competitors. But as companies undertake these transformation journeys, their data functions must also evolve to keep up with the demands of a data-driven business.

In this week’s Business of Data podcast, Natasha Gray, VP, Head of Data Analytics for HIV/AIDS researcher and pharmaceutical company ViiV Healthcare, shares how she’s spearheading the firm’s analytics capability modernization.

“The driver behind all this is to make sure that we're getting more medicines to the patients who need them,” she says. “This means looking at opportunities to leverage AI and machine learning to optimize the business and bring about new chances for collaboration.”

"The other thing might be a bit basic,” she adds. “But it’s ensuring that our reporting and our performance is clear and accessible to all. So, democratizing data, making sure that we're having the right conversations.”

Leveraging Data Talent from Across the Business

Naturally, successful data projects in one department can lead to increased demand for data insights across the organization, stretching the data team’s resources. Gray faces the same challenge as many data leaders – balancing organizational demand with her team’s capacity. That’s why she says it’s important to make the most of co-workers outside the team with a passion for data.

Gray says: “One of the things we’re thinking about is the need for more manpower. You could say my team is the center of excellence. But there’s also an opportunity to develop the talent elsewhere in the organization.”

“If we try to hold too much centrally, we’ll become kind of bogged down, and I think that will stop us innovating,” she continues. “We have many great people within the business who have data- and analytics-related roles. In my view, that's a key to our success. If you don't have those people who are trained and can take on some of the work and drive the strategy locally, the transformation would stall eventually.”

Key Takeaways

• Democratize data across the enterprise. Data democratization encourages inter-functional collaboration and shows value in data and analytics
• Plan for increased demand for data. As demand for data-driven insights grows in an enterprise, data team efficiency becomes critically important
• Make the most of data champions. Where resources are limited, developing data champions within different departments can drive the data strategy locally

Simon Jones, Head of Data Science and Advanced Analytics at Saga, talks about building a remote-first data science team to help Saga recruit the talent it needs to modernize and engage its increasingly digitally savvy audience

Providing seamless digital customer experiences wasn’t always a priority for British ‘over 50s’ insurance specialist Saga. But as a new cohort of digitally savvy consumers enter their middle ages, the firm’s attitude toward the need for modernization has changed.

As Saga Head of Data Science and Advanced Analytics Simon Jones explains in this week’s Business of Data podcast episode, the company is now reimagining itself in light of the changing needs of its customers.

“A lot of people are moving into the ‘over 50s’ category, which is where Saga’s footprint begins, and they don’t necessarily think of themselves as the sort of person who signs up with Saga,” he says. “Trying to understand exactly how we can penetrate into that demographic group was a really important thing.

“And what [we] recognized very early on was that a lot of it was down to our relationship with technology.”

Saga is now developing new technological capabilities with these customers in mind, and Jones believes embracing a ‘remote-first’ model for data science will give the company an advantage as it pursues this aim.

The Benefits of Being Remote-First

Jones joined Saga’s insurance arm in May 2021 with a remit to build a data science team to help the company get the most out of its data asset. He says the role is the first he’s held that has empowered him to truly embrace remote working.

Jones argues that this approach makes it easier for Saga to recruit top-quality talent and makes a career at the company more attractive to data scientists who enjoy the flexibility that comes with remote working.

“I’ve got a recruitment function right now, to build out a remote-first team, trying to find top talent in data science and bring them on board to Saga,” he explains. “That means our talent pool is anywhere in the UK.”

“If somebody wished to explore a bit more of the country by basing themselves in different spots over the course of a working month, I have no problems with that,” he adds. “As far as I’m concerned, you’re always working in the same location: The cloud, online, with me.

“That makes it possible for us to reach out to talent which, for particular reasons, have based themselves outside the areas we’d normally be recruiting in.”

What’s Next for Data Science at Saga

In the near-term, Jones’ priorities include building out his team, helping Saga build out its data lake and sourcing new “exotic” datasets to provide staff with insights they don’t have access to currently.

But looking to the future, he sees his priorities shifting toward helping to drive the adoption of data-driven technologies across the organization and creating processes that help his team get data science products into production efficiently.

“It’s all part of serving the broader agenda of helping Saga advance,” he concludes. “There’s going to be an awful

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.

Qlik EVP Global Partnerships Poornima Ramaswamy shares how the software company is enabling customers to respond to real-world demands

When the pandemic struck, not even the most comprehensive business continuity plans considered the whole world coming to a standstill at the same time. During this time, data came into its own as the backbone of business decision-making.

In this week’s Business of Data podcast, Qlik EVP Global Partnerships and Chief of Staff to the CEO Poornima Ramaswamy speaks to us about data’s evolution from a back-office function to a front office enabler. With over twenty years of data analytics experience Ramaswamy has seen first-hand that although data has always been a key business priority, the challenge lies in putting it at the center of operations.

“CEOs understand the importance of harnessing data and what that can do for a business. Unfortunately, that doesn’t mean they understand how to get to the end goal where data is a core part of their business strategy. They still lean on technology teams to help them navigate this,” she says. “But that is the first opportunity for CEOs to actually understand what it means to be data-driven.

“Many times, there’s a disconnect between organizational data goals and reality. The aspiration to become data-driven and to improve customer experience is there but the level of investment and effort doesn’t match the aspirations.”

Responding to Real-World Demands

In Ramaswamy’s view, that discord between data aspirations and becoming a data-driven organization is where CIOs and CDOs step in. Data leaders will help business leaders understand what that data journey should look like and move it from driving strategic decisions to operational ones.

“Data teams should look beyond one- or two-year targets and base decisions on data,” she says. “Many of our customers are successfully getting executive buy-in and transforming the entire organization, up and down the value chain.

“We’ve got a few customers in retail and electronics and through COVID-19’s early stages, they recognized the importance of acting on real-time events. So, it’s less about a short-term strategy and more about keeping stores open and helping employees manage KPIs, stock levels and supply chain bottlenecks, for example.

“As the pandemic moves into being endemic, and as other international events pop up such as the California wildfires and the looming gas shortages, customers want to respond to events based on real-time data and not just at a strategic level, but right down to the factory level.”

Key Takeaways

• CEOs embrace data’s role but don’t know how to operationalize it. As a back-office function, helping drive strategic business decisions
• CIOs and CDOs are stepping up to the plate. Data leaders are helping organizations fulfill their data strategies by mapping out the journey
• Look beyond data short-term goals. A data strategy should consider how to keep the business running in times of uncertainty

Fiona James, Chief Data Officer and Director of Data Growth and Operations at the UK's Office for National Statistics, provides insight into unlocking the power of data sharing and simplifying access to data at the organizational level.

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

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.

Aaron Wilkerson, Manager, Data Management, for child care and early childhood education company, Learning Care Group, talks with us about making processes and solutions agnostic to ensure data concepts are uniform to the business end-user

In this week's episode of the Business of Data podcast, host Catherine King talks with Aaron Wilkerson, Manager, Data Management, for child care and early childhood education company, Learning Care Group about how the data management team has been working to enable the business to have greater access to data & insights.

In the discussion this week:

• Getting data in the hands of teachers and business end-users
• The power of making backend sources agnostic
• How to keep key stakeholders aligned
• Business value should be the north star of every data team
• Prioritizing data projects
• Blending legacy systems with new structures

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.

ZestMoney Chief Data Officer Natalia Lyarskaya explains how cutting-edge technology is helping consumers in India access credit where it was previously unavailable

The appetite for credit is growing in India. However, compared to developed credit markets like the US, India is underserved. There are only three credit cards per 100 people in India, compared with 32 per 100 in the US.

This may be starting to change. In this week’s episode of the Business of Data Podcast, ZestMoney Chief Data Officer Natalia Lyarskaya explains how ZestMoney is using AI and machine learning technology to create a transparent and trustworthy credit solution where traditional banks have been unwilling or unable to do so.

“There is a kind of chicken and egg problem where someone needs access to the credit products but has never been in the financial sector before and other banks, traditional banks, cannot evaluate their creditworthiness,” Lyarskaya says.

She continues: “We believe that using data and technology, we can build this affordable, transparent, financial product for the Indian people that can be used by everyone and can increase also the trustworthy population in this new credit segment.”

Evaluating customers using data, machine learning and AI

ZestMoney has created a 100% digital user experience that uses an array of data coupled with machine learning and AI technologies to evaluate new credit lines in a matter of milliseconds.

“Based on the AB testing that we've done we have collected quite a good amount of data,” Lyarskaya says. “[We built] some predictive models that allow us to differentiate between different groups of users, so we can propose different journeys and different options for users to apply for our product.”

While the technology behind ZestMoney’s model evaluates new credit applications and makes the final decision on credit approval, it also guides the user on a personalized journey assessing and modifying questions during the application process based on personal and historical data.

“This [model] is basically behind every decision that we take along the journey,” she explains. “Like, what kind of questions we want to ask a user, or do we want to ask this question in one way or the other?”.

She concludes: “There is a model that stands behind that tells us what exactly we need to do and who is the user that we see in front of us. So that is all based, not just on our assumptions, but on what the data has been telling us.”

Key Takeaways

• Machine learning and AI are helping financial firms reach new credit markets in India. Where traditional banks have been slow to react, tech upstarts have been able to capitalize.
• Balance privacy and personalization for a better user experience. Understanding how much data an individual feels comfortable sharing is an important first step to creating outstanding user experiences.
• AI and machine learning solutions enable better products but do not create them. Human critical thinking is needed to make sure a system works. AI and machine learning make sure that the system is efficient.

Kassim Hussein, Head of Enterprise Analytics at Cleveland Clinic London, shares how he overcame an internal skills shortage to set up the clinic’s data and analytics function

When Kassim Hussein was offered the chance to build the analytics function for US-based hospital group Cleveland Clinic’s new UK arm, he jumped at the opportunity. As Head of Enterprise Analytics at Cleveland Clinic London, he’d gain leadership experience while sinking his teeth into an exciting new challenge.

“I realized this is a chance to start a new healthcare system, from scratch, in London,” Hussein recalls. “You’re not going to get this opportunity many times in your life!”

But as Hussein says in this week’s Business of Data podcast episode, the COVID-19 pandemic created unexpected challenges as he set to work establishing the new data and analytics unit.

Overcoming a Skills Shortage with Vendor Support

Cleveland Clinic is a US-based hospital group that also specializes in medical research. Its outpatient center was opened in Marylebone in September. This will be followed by a 184-bed hospital next year.

Before the center could open its doors, one of Hussein’s first priorities was to set up an enterprise data warehouse (EDW). This would help the organization capture key strategic data sources in one governed location that would also feed self-service analytics tools.

Hussein says working virtually with different vendors was a challenge. But he also says doing so was essential to address the limited resources his team had in-house.

“Because of GDPR, we had to build our own processes, working with vendors to make sure we have the right capabilities from day one,” he recalls. “Remember, I joined the team virtually and this came with many challenges. I was lucky that we had a big project support team (based in Cleveland, USA) and some contractors. But finding the right local skills was the biggest challenge.”

“We use [e-health cloud-based software] Epic for our medical records management system,” Hussein continues. “It’s an American tool. So, only a few healthcare providers use it in the UK. Finding someone with the standard BI skills and experience plus knowledge about Epic is near impossible.”

“Because of the nature of our situation, one has to get their hands dirty to cover the internal skills gap,” Hussein adds. “It’s a bit of a hybrid role, right now. So, I have to do SQL scripting [and] Tableau visualizations. I’m leading big meetings with stakeholders, but I’m loving it.”

“If we want to make London more attractive to BI analysts, we would have to invest in training and development,” Hussein adds. “The analysts also need to be interested in upskilling themselves.”

Next Steps for Analytics at Cleveland Clinic London

With the hospital’s basic analytics systems up and running, Hussein says the next step is laying the foundations Cleveland Clinic London will need to branch out into AI and machine learning.

“The data quality has to be really accurate; if we want to build models, the data has to be fit for purpose,” Hussein says. “We want to understand the data lineage and we need a genuine u

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

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

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!

Wendy Zhang, Director of Governance and Data Strategy at Sallie Mae, discusses why companies must put the right culture, value and quality into their AI initiatives Since Gartner’s famous proclamation that 85% of AI projects end in failure, the maturity of enterprise AI functions has improved dramatically. But the high number of projects that continue to end in failure suggests that many companies are still getting the basics of AI development wrong. In this week’s Business of Data podcast, Wendy Zhang, Director of Governance and Data Strategy at banking company Sallie Mae, shares her views on why so many AI projects don’t deliver results. For Zhang, common issues like poor data quality, trouble identifying valuable applications for AI and lack of buy-in for investing in or adopting AI technologies are symptoms of a more basic problem. “There are a lot of different reasons [AI projects fail],” she says. “But it all starts with a lack of fundamental understanding of AI, what it is and what it can or cannot do.” AI Success Starts with Asking the Right Questions Zhang warns against doing AI for the sake of AI. She argues that companies must start with the business challenges they need to solve before considering what value AI might bring to these initiatives. “The next [thing] you have to really assess is, is this something that AI can actually do?” she continues. “Is this appropriate for the business problem you’re going to solve?” Once an AI-focused executive has identified projects that could benefit from AI-driven technologies, they must consider what they need to deliver these projects successfully. This includes assessing what resources, funding, datasets and support they’ll need for each project. “It’s really got to become the company’s DNA,” Zhang adds. “It requires people to really look at a lot of your business processes and to think about different possibilities, and that requires mindset change.” “It’s not so much working and doing the same things over and over and just automating a few things and having AI on the side,” she concludes. “If you really want to get a massive benefit, you have to be able to experiment and fail and also incorporate that into your core business.” Simpler is Often Better for AI Beginners When companies are new to AI, they typically don’t have fully formed strategies for adopting these technologies. It’s more common for enterprises to begin experimenting through trial and error to discover the types of AI systems that are relevant to them. When starting out on this journey, it’s good practice to start with projects that can be delivered using data the company already has. The sooner they are implemented and delivering value, the better. Similarly, Zhang notes that simpler AI models can be easier for fledgling AI teams to deliver. Even for more advanced AI functions, she warns against overcomplicating AI systems unnecessarily. “I think of simple models as, in plain terms, you get more bang for your buck,” she quips. “The more complicated the models are, the harder it is to have a higher interpretability.” “The other component is having the right people,” she adds. “It’s important to build AI capabilities in-house. However, when you first start out with a pilot project, it might be beneficial to get external help, just so that you can get the ball rolling and gain some momentum.” “You really have to go through a lot of trial and error,” she concludes. “Start with pilot projects to score some small wins to get some buy-in and build your credibility to get faith for your team.” Key Takeaways • Start with the right questions. Only embark on AI projects when they’re the best answer to a pressing business question • Find use cases you can deliver with what you have. Identify what data, resources and support you’ll need before you begin • Simpler is often better. As any engineer will tell you, the more parts something has, the more bits there are that can break

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

Ioannis Gedeon, Unicef: Analytics Leaders Must Ask the Right Questions

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

AXA Investment Managers Global Head of Customer Insight, Web Experience and Analytics Brian Stewart argues that the time has come for asset management firms to innovate their customer experiences

The asset management industry is not famous for its innovative customer experiences.

However, in a competitive market even asset management firms can’t be complacent about fast-changing customer expectations, argues AXA Investment Managers Global Head of Customer Insight, Web Experience and Analytics Brian Stewart in this week’s episode of the Business of Data Podcast.

Helping AXA Investment Managers to understand not only what their customers buy, but why they buy it is at the core of Stewart’s mission, and data is the key.

“[The industry is] now really starting to understand the importance of this customer data, because [customer] interests are probably changing much faster than the industry can change, traditionally,” Stewart says.

“It’s leading to a transformation within our industry to become more agile and to really start to try to understand the things that people want to invest in,” he continues.

Like many organizations, AXA Investment Managers accelerated their digital transformation initiatives because of the pandemic. As customers began to interact with the firm more online, it gave them access to richer and more plentiful customer data.

“We've been able to collect an awful lot of information and data which we never had pre-pandemic,” Stewart notes. “Whether that be through webinars or online events, through our websites, our fund center, and so forth.”

Previously, data from different sources had been siloed in different systems and deployed in various models. Stewart’s team centralized their datasets and linked their marketing automation tools to a new CRM to create a fuller picture of their customers’ behavior.

At the same time, Stewart’s team helped to refresh the customer experience on their websites with a focus on easy access. This led to a massive increase in online traffic and subscriptions, a key metric for the firm.

“That led to that has led to more people coming to our website, but then more people go into our fund center,” Stewart says. “The fund center is the key part of our website where [you get] information on the funds that we sell and how they're performing.”

According to Stewart, the key to the success of their strategy has been breaking down data siloes and connecting tools to a centralized CMS.

“Do not do things in silos,” Stewart warns. “It really just makes a rod for your own back. So, map it out, think it through, and then go for it.”

Key Findings

• Follow your customers’ expectations. Even industries that have lagged in the past need to keep up with rapidly changing customer behavior.
• Break down data siloes. If you can’t link your data then you won’t be able to build a complete picture of your customer.
• Link your tools. Connecting the dots between your internal systems will produce more meaningful, data-driven insights.

Soeren Lueders PhD, VP, Effectiveness and ROI Modeling at SevenOne Media, outlines his team’s ‘questions first’ data science strategy and why focusing on the right data beats traditional ‘big data’ approaches

Marketing analytics is a key focus for German broadcasting company SevenOne Media. But while many marketing analytics teams prioritize gathering huge volumes of data to micro-target potential customers, SevenOne Media is bucking this trend.

As Soeren Lueders PhD, VP, Effectiveness and ROI Modeling at SevenOne Media, says in this week’s Business of Data podcast episode, this isn’t necessarily the best approach for all companies.

“As soon as you talk about ‘data-driven’, [people think] it’s about data collection and it’s still about collecting as much data as possible,” Dr Lueders says. “I think it’s slowly changing. And what we’re doing as well is to look at, ‘Is all this data really necessary for what we’re doing? Or, what we’re doing with all this data, is it really getting us where we want to go?’”

Having the Right Data Beats Having ‘Big Data’

Dr Lueders argues that many companies are drawn to approaches such as microtargeting because the tech companies that sell the data and tools needed to them generally have compelling sales pitches.

However, he notes that research suggests focusing narrowly on ‘ideal’ customer segments can be counterproductive.

“Traditional digital targeting is all about data collection,” he says. “So, you try to get as many data as possible. You try to build groups upon that data and to try to target niche markets.”

“[But] when you look closely at your marketing campaigns,” he continues. “Niche targeting or surgically targeting certain groups is not really necessary for most companies because, most of the time, your product is really available for a broad audience.”

“You can easily analyze this by yourself, if you just look at, ‘Who are you aiming at?’ and then at the end, ‘Who is buying your product?’” he adds. “If you make this analysis [and] you see that there’s a big difference, then you should think, ‘Maybe this approach doesn’t really make sense.’”

For Dr Lueders, unnecessary microtargeting causes many companies to neglect large portions of their true customer base. In the end, it’s customers who lose out, with some audience segments receiving too many ads and others being served none at all.

A ‘Questions First’ Approach to Data Science

Ultimately, the key to avoiding this kind of trap is to flip the approach that many companies take to data science. Rather than collecting lots of data first and then working out what to do with it, data-focused executives should start by asking questions about the problems they want to solve.

“The right data is the data which is necessary for the project to fulfill the task or to get the results,” Dr Lueders. “It’s very common in the market to collect as much data as possible. And then, once you’ve got data, you kind of decide what to do with it.”

“[This is], I would say, the wrong approach,” he concludes. “You should really focus on what kind of question you want to answer, and then you look at, OK

This episode is a must-listen for data and analytics leaders seeking to understand the intersection of AI, robotics, and operational efficiency. Tune in to discover how these technologies are shaping the future of industries beyond traditional manufacturing, from e-commerce to healthcare, and what the key opportunities and challenges are in adopting them.

Former Executive Director of Enterprise Information Strategy and Risk Management & Global Data Protection Officer at Bristol-Myers Squibb on what it takes to emerge stronger from the pandemic

In this week’s episode of the Business of Data Podcast, Kamayini Kaul, the former Executive Director of Enterprise Information Strategy and Risk Management & Global Data Protection Officer for Bristol-Myers Squibb, joins our host Catherine King for a wide-ranging conversation about how data and analytics teams and individuals can put their best foot forward as we emerge from the pandemic.

“Sometimes you have to go slower to go faster,” Kaul says. Knowing when to slow down before you can accelerate with the synergies that are arriving as part of a new team, new company, new organization, I think is a huge learning for me personally.”

How data teams can emerge stronger after the pandemic

The path to recovery for organizations severely affected by the pandemic will differ from business to business.

Having the right data strategy will be critical to success, regardless of industry, as Kaul notes. However, it will be up to data and analytics leaders to define the correct path.

“Data strategy is going to be front and center for all industries and all data and analytics [teams],” she says. “But, for data and analytics to drive that recovery, I think every leader at their level is going to need to introspect and say, ‘what is our data strategy?’”

In addition, Kaul believes that the pandemic has highlighted the benefits of cooperation between individuals, companies and even countries. A lesson that can benefit data teams and their practice.

“The pharma [industry], medical device manufacturers and provider networks are cooperating on an unprecedented scale, not just within countries, but globally to bring to bear both treatments as well as vaccines,” Kaur notes

She continues: “The data space, and the ecosystems of the seamless exchange of data, quality data, trusted data and the ways in which data and analytics professionals enable that for their enterprises is going to be another focus.”

Facing new challenges as an individual

Of course, for many people, the pandemic has meant drastic personal change. A change of location, a new job, or even a new career. For those people, Kaul has some advice.

“Get comfortable with tech, digital and data,” she says. “It is very much a part of the next industrial revolution. If you happen to be in the field [already], find ways to make sure that you're bringing everyone else and their level of proficiency along with you for the ride.”

She concludes: “I think that that could be the biggest differentiator in all of us as data and analytics professionals, trying to make a dent with being a data-driven organization, culture, or a society.”

Key Findings

• Slow down to go faster. Getting the basics right will help you scale faster when the time comes.
• Cooperation is key. The pandemic has highlighted the benefits of cooperation between companies, industries and even countries.
• Help raise standards of data literacy. Understanding the world of data will be instrumental to success in the information age.

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

In the latest episode of the Business of Data podcast, we dive deep into the future of data and analytics with Cecilia Dones, a marketing executive, professor, and doctoral candidate. In this insightful discussion, Cecilia shares her expertise on the growing influence of Edge AI and its impact on AI-driven analytics and decision-making for data leaders.

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

The end of Season 12, bonus episode! 

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

Joe DosSantos, Chief Data Officer at business analytics platform Qlik, outlines why he believes the company’s ‘cloud-based offering is the future of business analyticsThe advent of cloud-based software as a service (SaaS) lowered the barrier to entry for all manner of business analytics capabilities. The cloud makes it much easier for companies to experiment with and acquire data-driven toolsby removing the need to build and maintain software products in-house.But as Joe DosSantos, Chief Data Officer at business analytics platform Qlik, notes in this week’s Business of Data podcast, it’s only recently that many data-focused executives have started to get comfortable with cloud-based technologies.“People were a little bit nervous about the cloud,” he says. “Generally speaking, people have been born and raised in the data area to be very afraid of moving data anywhere where they can’t control it.”In recent years, attitudes toward cloud-based platforms and services has changed dramatically. For DosSantos, this is partly down to how greatly these technologies have matured.“The tools are out there, now,” he says. “People have known and loved Qlik for a long time. But what’s new and different is, ‘How do I start to get comfortable with the idea of my data being somewhere ‘out there’?’”Qlik’s ‘Italian Cooking’ Approach to Business AnalyticsDosSantos says a key benefit of doing analytics in the cloud is that it makes it easier for company stakeholders to access the data they need and connect datasets to uncover valuable business insights. “It’s all about ‘time to value’, at the end of the day,” he says. “How do I take this data and make sense of it more quickly? So, SaaS is fundamentally a way to get there faster.” To illustrate his views about how best to approach this, DosSantos uses the analogy of French versus Italian cooking. French recipes are sophisticated and require detailed knowledge of the chef and their ingredients. But Italian food is about simplicity and the quality of the ingredients.“In the data lake era, we kind of let everyone fend for themselves,” he says. “We said, ‘Go and grab raw data and figure it out.’ It was French cooking.”He adds: “What we’re trying to do is roll out this idea that what you want to do is put the best data that’s already been finely curated out there, so people can get the answers quickly.”By focusing on taking high-quality data and making it available to people at the right time to inform key decisions, DosSantos believes companies can maximize the value they drive with business analytics in the cloud.“Decisions must be part of one’s calculus,” he concludes. “At Qlik we call that active intelligence. It’s not good enough to know something. One must do something with that which you know.”Key TakeawaysThe future of analytics is in the cloud. SaaS is helping enterprises make data available to company stakeholders, so they can use it to uncover valuable business insightsProvide access to high-quality data. DosSantos argues that data leaders should focus their efforts on making high-quality data available to as many company stakeholders as possibleActive business intelligence is the key. Enterprises must integrate insights with business processes, so they inform the decisions staff members make Other quotes“People have known and loved Qlik for a long time. But what’s new and different is, how do I start to get comfortable with the idea of my data being somewhere ‘out there’? And the tools are all there, now... and I think now the expectation is there.”“Analytics is fundamentally about the discovery of new things and the connecting of new data... so, one of the things that we had to do was to make sure that the security was super intuitive, clear, understandable, and that we offered people a really complete way to understand what kind of data assets were being made available.”>> says execs are starting to expect teams to be able to adopt new technologies in the cloud“The idea that we had as weJ

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

Gary Goldberg, Chief Data Officer, Trading and Shipping at BP, talks about the role data will play as the firm transitions into an ‘integrated energy company’

With the 2021 UN Climate Change Conference just weeks away, the pressing need for the global economy to transition to greener energy sources is ‘top of mind’ for leaders across the globe. This need will be a defining feature of business and data strategies for energy companies including BP in the coming years.

In this week’s Business of Data podcast, BP Chief Data Officer, Trading and Shipping Gary Goldberg outlines the role data will play in helping BP to realize its vision of becoming an “integrated energy company”.

“BP is transitioning into an integrated energy company,” Goldberg says. “That, especially from a data perspective, creates a whole load of opportunities. When we look at new sources of energy, when we look at production techniques, there’s a need for data to support those initiatives.”

“[This] also means that the company is moving from a historically siloed approach to different commodities to (as the title would imply) an integrated approach,” he adds. “It puts data more and more at the heart of everything we’re trying to do.”

“That integration of data and what that means for the company and what we can do is incredibly inspiring,” he continues. “It means bringing together data from across our commodities, from across the new products we’re bringing to market, and getting the interrelationship insights that are gained in offering a portfolio of products to our customers.”

BP’s Data-Driven Business Transformation

For any historic organization, achieving such an ambitious goal is like turning an oil tanker at sea. Creating a truly data-driven organization will take time. But Goldberg highlights several key milestones his team has already reached on that journey.

“We’ve got some pockets of real excellence and are trying to scale up,” he says. “For me, the transformation on the data comes down to fundamentally allowing people to understand the value of data.”

“When we can articulate what [our data] assets are, we can then have a conversation about how you want to manage them,” Goldberg continues. “For us, one of the first [milestones] was just to establish that inventory.”

He notes: “[It was about] changings the conversation from an ethereal one around, ‘Let’s just make it better’, to, ‘This is what we have. This is what we could have. It will generate a return if we use it for the following purposes. How much would you spend to get that return?’”

“For me, when I changed that conversation to where the conversation was one of investment return, that’s the transformative moment,” Goldberg concludes. “It’s when the business was properly engaged.”

Key Takeaways

• Data integration can enable business innovation. Breaking down data silos is essential for uncovering insights that stem from the interconnectedness of data
• Articulating the value of data is key for securing buy-in. Reframe governance conversations around increasing the value of the company’s data asset and achieving business outcomes
• Cataloging data is the first step to quantifying its value. Before leaders can talk about how to increase the value of their data assets, they should understand what data their businesses have

Austen King, Global Head of Data and Analytics at Clyde & Co argues that building a collaborative culture is key to successful digital transformation

For many businesses digital transformation is a catalyst for recovery as we emerge from the pandemic. However, for too many people digital transformation is seen as a job for the IT department.

In this week’s episode of the Business of Data Podcast, Global Head of Data and Analytics at Clyde & Co Austen King argues that successful digital transformation requires everyone to take ownership of the process.

“You can't just outsource the ownership of your business,” King says. “The data is the business.”

Law firm Clyde & Co was founded almost 90 years ago. For King, transforming a business with a long cultural memory and entrenched legacy systems was not without challenges.

“We have a lot of processes and procedures,” King notes. “It can be challenging to migrate [to the cloud] because they are legacy systems and there's a lot of things to do.”

However, the long cultural memory is an advantage in other ways. King and his team were able to infuse their transformation initiatives with the experience of the business.

There's a maturity of instinct within the firm to do things in certain ways. The challenge is to try and take those elements of instinct and then apply those to the new system,” he says. “When you distill that down, you can actually get some great insight from people.”

However, creating enthusiasm for the digital step-change is not always easy. King recommends creating a formal structure for feedback – this improves performance and gives staff a sense of ownership.

“It’s largely about trying to communicate, being transparent, letting people know what you're doing and why you're doing it in a particular way, and giving the opportunity for people to give their feedback,” King advises.

Features clips from the Podcast episodes throughout Season Nine!

Business of Data Podcast 

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