From Data Strategy to Execution
with Steve Pimblett, CDO, The Very Group
CDO, The Very Group
Steve Pimblett is the first CDO of The Very Group, UK and Ireland's leading multi-brand digital retailer. He previously served as chief data officer at Wejo and the Betsson Group. Steve is motivated by the application of data and technology to drive commercial value and customer experiences.
Co-founder & CEO of Alation
As the Co-founder and CEO of Alation, Satyen lives his passion of empowering a curious and rational world by fundamentally improving the way data consumers, creators, and stewards find, understand, and trust data. Industry insiders call him a visionary entrepreneur. Those who meet him call him warm and down-to-earth. His kids call him “Dad.”
Satyen Sangani: (00:03)
Imagine this: It's day one of your new job. You're the first ever chief data officer at a big, giant enterprise with a yet uncharted data landscape. And as you sit down to chart your strategy, fear starts to set in. You're overwhelmed. Where do you start? Technology, people, processes? It's a feeling CDOs know all too well. It's easy to get lost in the wilderness of data and particularly so when you know that the average CDO's tenure is just 18 months. The clock is ticking. So how do you choose your strategy? How do you align with your business partners?
Satyen Sangani: (00:48)
How do you make sure you're adding value and show that value to your C-suite peers? Today on Data Radicals I’m speaking with someone who has a lot of experience guiding digital transformation. Steve Pimblett is the chief data officer at The Very Group. He also served as the CDO of Wejo and the Betsson Group. Steve's years of experience have given him a unique perspective on the CDO's role and it's my hope that after you listen to this conversation, the first day on the job may not be so daunting.
Welcome to Data Radicals, a show about the people who use data to see things that nobody else can. This episode features an interview with Steve Pimblett, chief data officer of The Very Group. In this episode, he and Satyen discuss organizational structure, how to demonstrate the value of data and the changing role of the chief data officer. This podcast is brought to you by Alation.
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The Four Pillars of Creating Value from Data
Satyen Sangani: (02:05)
Fifteen years of experience as a chief data officer or thereabouts. What would you do differently today given everything you know, relative to the first time you actually started the job? What do you know now that you didn't know then — and what would you change if you were to boil it down to three learnings?
Steve Pimblett: (02:28)
So one of the biggest learnings, really, if I'll talk about it in the context of data strategy, actually. So I sort of developed a four-pillar — data strategy, data insight, action, are three, the pillars — and trust is the final pillar and it's the data/insight/action piece where it gives me my biggest learning, really. So data/insight/action for me is a really nice way of describing the flow of data but also the flow of the value creation. And I think my biggest learning throughout my career is to really start with the action and work back into the insight and to the data rather than — even in my early career — I started the other way around. So I have built massive data lakes and then someone goes, "How are you going to leverage them?" I've built amazing BI stacks with every imaginable KPI, drillable up and visualized.
Steve Pimblett: (03:22)
And again someone says, "How do you create value?" So, actually, the data/insight/action strategy — really you ask yourself the question very early — is how are you going to create value from data? What's the action you're going to take, where be it improved margin, improved profitability, better net promoter score? Whatever the business outcome you want, you really start with that and then work back through the pillars. What insight do we need? What data do we need to drive that insight? And that's really my biggest lesson learned, [it] was: start with value creation. Start with the outcome and work back — rather than start with data, then go move to insight, then think about what you're going to do with it.
Satyen Sangani: (04:05)
So let's drill into that for a second. Because you know could have a variety of different actions and then the question becomes, how do you decide which action to focus on first? How do you come up with that process and what is the role you have played in determining the action? Or is the action just given to you?
Steve Pimblett: (04:26)
For me the data strategy is a part of the enablement of any company strategy. So let's start by framing it in the company strategy. So what is the company strategy? What are the key outcomes they're to achieve? How do they measure success? That gives us a great frame for data/insight/action to land against.
Steve Pimblett: (04:47)
And then within that, the other dimension is very much what as a data function, if that's how you are set up, are you OKRs? What are the outcomes that you’re individually or collectively targeted on? Hopefully that link back to that strategy if you've got your operating model set up correctly.
Steve Pimblett: (05:06)
And those are the two frames really. Company strategy, your role as a CDO office or a group or a division, your OKRs. And that's the starting point, really. Do you think though that data/insight/ action as a strategy lends itself to then collaboration depending upon your operating model? Because a lot of the time the actions that are taken maybe are outside of the remit and the role of the chief data officer. So a lot of the times it's in collaboration with other business functions.
The OKRs of the CDO
Satyen Sangani: (05:40)
Can you give us some examples, then, of what those OKRs might look like? What are some that you're working on today?
Steve Pimblett: (05:47)
The verticals that I partner with are very much a retail business who are buying, selling and trading stock over 2,000 brands and hundreds of thousands of products. So an OKR that we've got at the moment is really all to do with increasing our return on stock. So, i.e., are we buying the right stock at the right volume to make sure we don't have any out-of-stocks or as few as possible. And also we don't over-order our stock. So that would be a good example. So there's an OKR wrapped around stock availability, stock, return on stock, and more accurate ways of forecasting the stock that we need to purchase against the customer base that we've got.
Satyen Sangani: (06:38)
And what is your role in this return-on-stock work? Are you having developed that as a priority with your business partner enabling them? Are you actually providing them with end reports? How do you figure out the handoff points? How much of it is functional, how much of it is done in the center of excellence?
Steve Pimblett: (07:01)
It's more like a joint venture in terms of, again, the retail business, hold the pen on the actions. So they actually press the button on ordering the stock, for instance. But my team are now an enabler to that particular business. So we've got a ring-fenced data/insight/action tribe that is aligned to our retail business that I line manage in terms of capability, tool sets, platforms. But the retail business leverages it against their particular OKR in that instance. So it's very much run as a joint venture where we've both got investment from an investment board, we offer a return on that investment over X number of years. And there we are jointly building the capability to improve the accuracy of our forecasting and models and the business adoption of those models into the purchasing cycle.
Satyen Sangani: (08:06)
That seems like a reasonably successful and pretty clearly defined model. How does that relate to the overall business strategy of The Very Group? The return on stock seems to be a very specific thing, important thing, in the context of where you are in the business' life cycle. But The Very Group obviously has lots of scale and scope and certainly I would imagine a reasonably complicated business strategy. How does one map from that metric to the overall strategy? And I guess maybe to tell us a little bit about that, maybe just give us a little bit of background on The Very Group and why you joined and what it is.
Steve Pimblett: (08:44)
Great, great questions. I'll frame it in so that The Very Group and why I joined. Yeah, I was really missing B2C. So prior to The Very Group, I was in a B2B model, so I was creating platforms for connected cars to centralize the data and it was a platform play and I was looking off to be chief information data officer in that model. But I was definitely missing B2C, not owning or having a tangible relationship with the customer and putting products in front of customers.
Steve Pimblett: (09:16)
So that was one of the big reasons. The other reason was really just size and scale. So Very Group is 2 billion turnover, 4,000 staff, 5 million active customers. Every digital touchpoint imaginable from a website, mobile apps, every digital media touchpoint, Facebook, YouTube, TikTok.
Steve Pimblett: (09:41)
So just really getting back into digital, getting back into B2C and the breadth and the remit of the role just excited me, especially the scale of the customer data landscape. Because as well as being digital, which is fantastic for data and a retailer multi-category. So thousands of brands, hundreds of thousands of skews. It's also a financial services organization because it offers credit. So it's sort of one of the richest, deepest UK data assets when it comes to consumers. Because I know everything about the consumers' finances, I know what they buy from each category and I know how they shop and how they touch the brand through the various touchpoints. So it's just a fantastic opportunity to really be a chief data officer and in my hometown of Liverpool. So that was fantastic.
Satyen Sangani: (10:42)
And so data can really matter. Data is not an optional thing. It's fundamentally consequential and impactful to the business.
Steve Pimblett: (10:52)
Yeah, absolutely. And the other dynamic to it is Very had never had a chief data officer, so it had an analytics director, it had a bit of a data director, but really never really centralized the strategy, the thinking or any of the platforms themselves. So just a really good opportunity.
Satyen Sangani: (11:16)
So then now bridge us back to the strategy. What is the business strategy for the company? And tell us how that has evolved as you've been there or has it been effectively the same as you've existed in the company.
Steve Pimblett: (11:28)
Yeah, yeah. Well I've been at very for 18 months and the overarching strategy, it is phrased as making good things accessible to more people. It's combining, really, retail and financial services, which is very much the sweet spot of Very. So that's the sort of strategy in terms of totality.
Steve Pimblett: (11:52)
Actually we are going through a bit of a strategy rework as we speak. And that's to frame it even more in strategic pillars, OKRs that everybody across the business can understand. Because it is quite a complex business under the bonnet, even though it's just — you could say it's just a retailer, [but] it's not because we've got retail, we've got financial services, how do they integrate? You got end-to-end customer management. So there's quite a lot of dynamics to the Very business. So we are working through a new way to interpret the strategy for the business and a new way to measure the strategy. It's very much starting to be centered around customers. So how do we think about customers' life cycle management of customers, segments of customers rather than being category led, being much more customer led.
Satyen Sangani: (12:48)
How do you see within Very the strategy process being unrolled and developed? You mentioned you're going through that process, who's leading it within the organization and what is your role in partnering with those individuals or that individual?
Steve Pimblett: (13:02)
Yeah, so we've got an essential strategy team and they are working with external consultancies who will then package it up in terms of a strategy framework. So I think strategic pillars, strategic and strategy measures. I think even breaking down what are the enterprise changes and capabilities that we need to build over the next three years to leverage and hit the new strategy. So yeah, we have a strategy team that turn it into a framework and a way to communicate across the organization.
Steve Pimblett: (13:41)
In terms of my role so far to support that team, well, in the last year we've definitely … already I've created a brand new team called customer forensics. Which is a whole data function revolving around customer metrics, both quant and qual. And that was part of the lead into some of the strategy work. So we've got a whole new team, a whole new capability and we've built some data products to support that strategy change already.
Trailblazing as the First CDO at an Enterprise
Satyen Sangani: (14:17)
Steve is the first person to serve as chief data officer at The Very Group. I asked him what were his initial observations on the company's data strategy.
Steve Pimblett: (14:26)
Prior to my arrival, data was democratized — I think is how it internally phrased it. I think what definitely happened to the organization is data was just distributed across these business verticals that I mentioned before. So there was no central accountability, no central platform thinking, no central principles and guidelines.
Steve Pimblett: (14:53)
So there wasn't really the hub to hold the spokes together. There was loads of great innovation out in the spokes, but no really glue to hold it together. So if you think what I inherited was some fantastic innovation in pockets but no reuse. So everyone had their own KPIs and their own view. Everybody had different data skills within their particular spoke or business vertical. And the things that we’re lacking were a central way of thinking about the total cost of ownership of data, no massive governance around data from a central perspective. So probably therefore associated risk with that.
Steve Pimblett: (15:40)
And no single way of just framing what a data strategy might look like and how you build something that benefits or business units or spokes because it was quite siloed thinking. So that's what I inherited and I quickly changed that from fully distributed to hub and spoke. So [I] started to set up the centers of excellence. It was I think April 2021 when we did that organizational change.
Steve Pimblett: (16:10)
So just over a year in. So we've now got hubs, centers of excellence, which is great for our people because they get career paths and plans and can really go on a journey with their career. But we've also got a partnership model with the spokes across the organization where we offer different services, different products, different levels of support, and that's definitely starting to win where the individual business units have leaned into that and we've got different levels of maturity of operating model. And also different levels of relationship, really, with the business units.
Satyen Sangani: (16:50)
We're going at Alation through a similar strategizing sort of strategy refresh process as it were. And a lot of what you're talking about has tons of parallels into our own experience and even into our experience in terms of how we're forming our data team. Because on one level every business is functionally organized.
Satyen Sangani: (17:10)
You've got to pick some mechanism of organization that could be business unit oriented and a software company you could take a functional view with sales and marketing and obviously that's true for a more simple business where you don't have multiple, totally different lines of business like retail and finance.
Satyen Sangani: (17:30)
So you can take that functional view, but then you also got this strategic view of what the business wants to do from a perspective that cuts across the organization. And “customer” is very obvious. But then there's also core capabilities like forecasting that you mentioned, which are super interesting.
Satyen Sangani: (17:46)
And I feel like that two-dimensional model really sets up the appropriate domains for analysis and enablement. And so you're constantly trying to, one, work with the business units to enable them around their own functional metrics. But then you're also on the other hand trying to push the strategy forward.
Satyen Sangani: (18:05)
And I feel like that is really where a CEO can be both sort of provider of infrastructure on one level, which you have to be, but also provider of insights and action. And so that experience, that framework really parallels at least my own experience. And I think so much of what an executive team does and so much of what traditionally a CEO and a COO and a chief strategy officer do, is really focus on what's this operating model and how do we enable a whole bunch of people to work around it in a functional way.
The Evolution of the Modern CDO
Satyen Sangani: (18:40)
Coming to that level of sophistication, at least for us, has been quite a journey and I'm sure there's much more sophistication in front of us. How have people viewed the role in each of the businesses that you've been in? I mean certainly you've seen it in lots of different companies and organizations and I'm sure there's an evolution of building trust over time. What has changed and what has stayed constant in terms of your having to prove it, as it were, in your job?
Steve Pimblett: (19:11)
Well, the thing that stayed constant for me — maybe it's the companies that I've worked with — are they all see the value in data and as a growth driver, as not just the cost center. So for me, my career and the companies that I've worked for, that's been the constant is they can see the opportunity to create value. What's changed, I think, and whether over the years the concept of creating value from data everybody's been nodding at.
Steve Pimblett: (19:46)
But the actual execution of doing so is starting to be questioned I think across the industry of “Where's this magic return on our data going to come from?” So I see that's the bit that's getting more and more challenged. Chief executives, chief financial officers, putting a bit more scrutiny on “When am I going to see the value from this data that the industry has promised?” So I can definitely say a step change in myself here — and my colleagues, that a lot of them are chief data offices in the UK within the networks that I'm in who are getting a bit more, “Where's the return? Come on, show me some return on the money that we spend over many years on data.”
Satyen Sangani: (20:30)
When you go to those conversations, how do you say, “Okay, here is where the money is and here's where the returns are.” I mean that's always a tough conversation. What do you look to? Is it case-specific where you're saying, "Oh, here's the forecasting model, here's how much value we created." And do you constantly have to retell those stories or do you have more horizontal infrastructure oriented measures that you point to? Or is it both?
Steve Pimblett: (20:56)
Yeah, so we definitely use outcome measures. So risk reduction of data is still a big part of my role. So it isn't just about create value through that way, it is help mitigate and reduce risk, be it GDPR or cookie consent or compliance. So reduce risk.
Steve Pimblett: (21:14)
I think drive self-serve is another metric that we use for — this is for the whole, horizontally — is how do we get think about total cost of ownership of data and insight and how many people might be pushing or pulling data. And that's an operationally efficiency measure. And the last one is the most tangible in terms of P&L, which is value creation. And the value creation is normally through that investment partnership, which has a set of metrics with the investment itself. That said again, one of the early things that I did is as part of my data science sense of excellence, we've got experimentation and measurement as a service.
Steve Pimblett: (22:01)
So we started to think about, actually, how does anybody measure the return on their change? So can we develop tools, techniques, mathematical products that allow people to understand the change? So standard things like A/B testing, but also things like cross-site experimentation, where we can look at the variables pre-change, look at the variables post-change, and use mathematical techniques to give statistical significance and inference on the step change. So we actually use some of our own measurement products to prove that the initiatives we've been part of have had an uplift, if that makes sense.
The Structure of a CDO’s Team
Satyen Sangani: (22:43)
You mentioned a lot about how you're structured. Can you give us just another tour of how you structure your organization and I guess more critically for those other chief data officers, what do you look for in terms of hiring your team?
Steve Pimblett: (22:59)
I've always ended up in a hub and spoke model in every organization. It's just how fat and thin your hubs and spokes are really. In terms of hubs, I've got a data platform, an engineering hub. So they tend to look after the infrastructure and the pipelining of data and things like enterprise warehousing. So I've got governance and operations, which tends to be aligned to all things privacy, security, risk, governance.
Steve Pimblett: (23:32)
I have a BI and analytics center of excellence. So really the provision of the insight, be it basic descriptive analytics and through a BI tool or more sharp, ad hoc analytics for one of the functions. And I've got a data science capability, which is all about how do we leverage machine learning, AI and advanced analytics techniques. And last but not least, which is unique to The Very Group, I've got a customer forensics team, which I think I mentioned before is part of the hub that's very much hones in on quant and qual understanding of our customer base.
Steve Pimblett: (24:22)
So those are the whole centers of excellence. Each of them has a head that vertically manages that function in terms of line management, people management, budgets, training development skills. But then the touchpoint with that is how we leverage them in the spokes.
Steve Pimblett: (24:46)
So each of the spokes that I work with has an account manager that reports to me. So it might be one account manager to many spokes, but that's where we start to think about how do we align our hub resources and hub capabilities with the individual spokes. How do we go into that on joint investment and how do we think about working together to leverage the data capabilities that we have or we are building within that particular spoke.
Steve Pimblett: (25:20)
So we sort of set up an internal agency in that model, which is what do you want to achieve in your business area? How can we make that data-driven? How can we leverage the capabilities, the platforms that we've got in the hub, in centers of excellence to accelerate their particular business unit and also the total overall strategy.
Satyen Sangani: (25:42)
The account manager therefore is almost sort of this internal salesperson.
Steve Pimblett: (25:47)
Internal client manager. So I suppose, maybe taking some of my B2B and consultancy days, so I'm sure you'll be set up with client success manager type people, which is once you've got a capability, a product, or a service, how do you make sure it gets leveraged within a particular business unit? So I have, more or less, client managers, so I'll have someone looking after retail. What are you looking to achieve retail? Ah, you're interested in shopping and pricing and well, that's a mathematical challenge, we could really help with that. Why don't we build you some new tools, capabilities, maturity around price elasticity models or whatever it may be that could help you be more data-driven, help you be more scientific, help reduce your total cost of operating. So yeah, it's like a client success manager really. That is the interface from my hubs into that particular spoke.
Satyen Sangani: (26:44)
And how large is the organization overall and how many account managers do you have in total?
Steve Pimblett: (26:50)
So there's 4,000 people at Very. There are more or less six major spokes, divisions — we actually call them “tribes” at Very to give them an agile theme. And I have got six account managers, so more or less one per tribe is how we try to set it up. Not always perfect. So sometimes I'm doing: one account manager looks after two spokes slash tribes.
Satyen Sangani: (27:23)
But I think one of the things that I think is super insightful and interesting, and I've never heard of anybody doing this before is this concept of both having an account manager, but you also talked about having to prove value and you're doing that at the business unit level where you mentioned things like TCO. And you're also doing that at the executive level where you mentioned the same things like TCO, which was, I think, brilliant. Because it never takes you away from ROI. I mean we're in this business of selling software and we're constantly having to prove value to our clients and that is a habit and discipline, which — if we ever get out of that habit and discipline — takes us away from building the right products, focusing on the right things for customers. And so, it's fabulous that you've actually institutionalized that. Where did you get that idea? I mean, did you always know to do this or did you just take this from…?
Steve Pimblett: (28:15)
Oh, I think, it came back from the experience, but that data insight action, it was in my early career, I focused on data and insight. And then quickly got asked by the chief financial officer, “Where's the return, Steve?” And the same with the strategy. “How does this help with strategy?” You've got more data than you can shake a stick at, you could drill that up and down on, but no one was really using it to take action. So I think it was once it's thought about, that framing is action — is really outcome. Outcome tends to be leverage, leverage tends to be investment and therefore let's focus on the action. It is hard. And when I arrived at Very, it's really hard for the data team because they just want to get on with building data store for the insight team, want to be involved in insight.
Steve Pimblett: (29:09)
And when you challenge them to say, but without the action pillar, without a partnership using it, you are not going to be able to prove your value, our value, our collective value. And it becomes really difficult at your budget process, when somebody's got a red pen going, who should we give them money to? Unless you can prove the upside, the value, link it to the strategy, confirm the leverage, you might not get invested in the future. So I think that's being the biggest challenge is for my team that we're used to being order takers — move some data, build some data — into trying to be value creators. That's been a big challenge for the team that I inherited really.
Satyen Sangani: (29:56)
Yeah, it's a brilliant observation for any service organization within a company or certainly, obviously, organizations outside of a company. Which you think, oh, chief data officer, well we're supposed to have all these data people doing all this data work and everybody's supposed to be a builder and you get consumed with the action. But on the other hand, if you don't both communicate the value but also listen — really listen to what your customers might want — then it's really easy to have that construction process get totally disconnected from the value-creation process. And I think you can see these account managers being internal sales people: not very, very useful. But on the other hand, if you don't have that loop closed, you're going to fall apart and ultimately get disconnected.
Steve Pimblett: (30:47)
Yeah, that's it. And again, if you think of it as data as products or data as the service, if no one's using your product, or no one's using your service, then you've got all this cost that you've built for no reason. And also bit like you mentioned partnership models, it's a great way to get funding. So it's a win-win really. I build things that get used, the things that get used drive value, but that's really the win-win.
On Data: One Myth, Two Predictions
Satyen Sangani: (31:14)
So I guess before you take us out, maybe give us two things. A prediction about how data will change in the next five years. What changes do you see happening in the industry? One of the things that you mentioned that you observed over the last five was this real increased focus on value. And then I'd love for you to give us a misconception, some myth that may exist.
Steve Pimblett: (31:39)
I'll start with the myth. And the myth is probably just by implementing technology or big data, you create value. I think that's probably: If you just buy some software, it's all going to take care of itself. And as long as you store it in the cloud, hey presto, you're going to magic some return out of it. So I think well, I don't know if that is an industry myth, but it's definitely outside of data, a bit of a myth of just “create a data lake and away you go.” So I think that's the myth that, obviously, it just doesn't happen. You've got to align your data strategy and your data capabilities with your business at value and your business outcomes. So that's the mix. In terms of prediction, I sort of had two, but maybe I'll wrap it in constant change. So I think the only thing that's going to happen within data and analytics is constant, even more change.
Steve Pimblett: (32:39)
And by that, what do I mean? I think regulation is still a massive topic for data across industry. And we've got this “cookie-less future” coming. I think the change of, well, new technology and how it can drive citizen models, citizen data scientists, citizen data engineers, the analytics engineer roles.
Steve Pimblett: (33:04)
So I think the whole shape of organizational structures, job profiles can all change as things like machine learning become more commoditized, therefore more accessible to more roles and more companies. So I just think, yeah, data's absolutely fascinating, but still constantly changing. So being able to be agile and adapt is probably one of the biggest things for me as a chief data officer and thinking about developing capabilities is the capability to be agile and flexible and scale up, scale down, change your operating model, I think is a big part of the role as we morph, as technology changes, as regulation changes and as the data landscape changes.
Satyen Sangani: (33:58)
I think having that flexibility for any executive, but certainly in the data office where, to your point, this idea of hubs and spokes is sort of definitional. But just the ability to flex up and flex down, how fat those are and how thin those might be and how connected they might be. Super, super insightful. Steve, thank you for taking the time. This has been just such a phenomenal discussion around the brass tacks about how to do the job. Super high appreciation of your time. So thank you very much and look forward to speaking with you again soon.
Steve Pimblett: (34:34)
Brilliant. Really enjoyed it. And yeah, thanks for having me.
Satyen Sangani: (34:43)
Chief data officers are leaders — and they're also salespeople. They need to sell the value of data to the people in their organization. Recognizing that need, Steve's taken that belief to a whole nother level. He has an account manager in every one of his internal spokes. It's their job to be a liaison between the data and the business. They're an internal client success manager. It's the same approach any external vendor would take to service their clients. This idea is incredibly powerful. The best sales people don't focus on selling a product to a customer. They focus on how their products solve that client's pain points. And that's exactly what Steve is doing with this organizational model. It's an idea that to me is truly radical. This is Satyen Sangani, CEO and co-founder of Alation. Thanks to Steve for joining us and thank you for listening.
This podcast is brought to you by Alation. Data citizens love Alation because it surfaces the best data queries and expertise instantly. The result? Folks know how to use the most powerful data with guidance from the experts and with Alation you don't have to choose between data democratization and governance. By embedding governance guidance into workflows, Alation welcomes more people to great data — fast. That means your data strategy can play both offense and defense. Learn more about Alation at alation.com.
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Using Data to Fight for Human Rights
Data Scientist, HRDAG
The Data Behind Dating
Author of Don’t Trust Your Gut
The Beginning of Business Intelligence
Founder, Business Objects & Managing Partner, Balderton Capital
Knowledge Graphs 101
Founder & CEO, Stardog
Radical Data Politics
CDO at Tableau
Data is a Weapon
Retired US Army General
The Data on the Chief of Data
Founder & CEO, NewVantage Partners
The Death and Rebirth of Data Privacy
Michelle Finneran Dennedy
#1 CPO and CEO of PrivacyCode
Data Quality Is a Risky Business
Co-Founder and CEO of BigEye
The Science Behind Quitting
Former Lead Science Writer at FiveThirtyEight
Your Path to CDO Superstardom
Co-Founder & Director, Carruthers and Jackson
Super Chickens & the Productivity Pecking Order
Dr. Margaret Heffernan
5-time CEO and Author of Willful Blindness
Attacking Data Literacy
Chief Data Strategy Officer at ThoughtSpot
Your Guide to Corporate Controversies
Co-Founder and Executive Editor, The Information
Making Big Data an Asset in Medicine
How to be a Data Cheerleader
Co-Author of The Chief Data Officer’s Playbook
Data Governance: It’s the Final Frontier
Principal at Teknion Data Solutions
The Secret to Data Storytelling
Cole Nussbaumer Knaflic
Founder & CEO, Storytelling with Data
The Reign of Specialists is Over
Bestselling Author of Range
Emerging from the Data Dark Ages
Principal Data Strategist at Snowflake
Forging a Culture of Data Governance
President & Principal, KIK Consulting
The Bazaar in the Cathedral
Co-founder and CTO of Databricks
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