Your Path to CDO Superstardom

Satyen Sangani: (00:02) The chief data officer may be the riskiest job in today’s C-suite. Getting the job is tough, for sure. But doing the job is the real challenge. Small wonder that the average tenure of a CDO is just 18 months. And today, it’s about much more than passing compliance audits. A CDO has to use the data to drive revenue and growth. Why is this such a high-risk role? And what does it take to succeed? Being a CDO takes a lot. It’s about organizational fit, setting expectations, and planting the right seeds. More importantly, it’s about demonstrating your value time and again.

Satyen Sangani: (00:38) So today we’re talking to Peter Jackson, a six-time CDO and co-founder and director of Carruthers and Jackson. We unpack how you can become that superstar CDO. If Peter’s name sounds familiar, it should. His business partner, Caroline Carruthers, has also appeared on Data Radicals. And if you haven’t listened to her episode, make sure you add it to your podcast queue. The two of them have written multiple books, including The Chief Data Officer’s Playbook. I sat down with Peter to learn how CDOs can succeed, from finding the right company to executing on the job. So let’s dig in.

Producer Read: (01:23) 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 Peter Jackson, co-founder and director at Carruthers and Jackson.

In this episode, he and Satyen discussed the importance of organizational fit for CDOs, finding quick and actionable wins, and navigating relationships with vendors. This podcast is brought to you by Alation. What if we told you that data governance can drive real business results? This white paper from Gartner shows you how, go to alation.com/dag to get your free copy of Gartner’s guide. It’s called Adaptive Data and Analytics Governance to Achieve Digital Business Success, and it’s yours for the downloading. Check it out today.

CDO as change agent: Quick wins & the art of the possible

Satyen Sangani: (02:09) You are a self-described first-generation CDO and therefore almost definitionally a change agent. And you’re coming into these institutions, in some cases, where at least there are some folks that don’t want to change. How do you address that, and how do you think about that problem? Because it’s sort of tough to give people motivation if they don’t have it.

Peter Jackson: (02:29) The chief data officer is becoming increasingly recognized as the agent of change. It’s a channel through which change will occur, if unleashed. How do you overcome the naysayers, the reticence? I think it’s quite pragmatic: You have to understand what that pushback is and where it’s coming from. Do a review, a survey of your stakeholders, to find out where your promoters are and where your detractors are, is very important. But I think you’ve got to … in my experience is creating that story that people can understand. Don’t talk about regression and anomalies and clustering and data science. And don’t talk about data governance. Put it in stories that they can understand, and then they will buy in more to what you are talking about.

(03:18) Rather than thinking you’re talking about some Harry Potter kind of stuff that’s over here and is not relevant to them and unattainable, I think the first thing is to make it clear and explicit through the storytelling.

I think the next thing is to demonstrate the art of the possible. You then have to deliver actions on top of that. You need to get after some early use cases that will show value of some of the things you’re talking about and to show that these things are possible, whether it’s delivering significant or early evidence, operation efficiency through using data in a different way or data techniques in a different way. Or whether you’re actually increasing the bottom line, or reducing overheads, or acquiring new customers, or increasing customer wallet, or reducing churn of customer, you need to do something fairly early on to prove that you can have an impact.

Because if you start having an impact for your stakeholders, that starts to shift the culture, it starts to get them buying into what it is you’re talking about.

Satyen Sangani: (04:14) As Peter said, there will be people who are skeptical of a CDO, but remember you have allies, even if it might seem like you don’t.

Peter Jackson: (04:23) I think Caroline and I, in our most recent book — in the second edition of The Chief Data Officer’s Playbook — broke down the Gartner hype cycle further. We talked about it in the first book; we broke it down further. And right at the start, there has to be a trigger for an organization to appoint somebody like me. Somebody in the organization has made that trigger of saying, “We need a chief data officer.” I don’t think I’ve ever walked in somewhere and found a uniform blank wall of disengagement of naysayers. There’s always somebody who is your earlier promoter. And that is somebody who’s probably said, “You know what? We need a chief data officer” or “We need a chief scientific officer.”

(05:03) So, I think latching onto your stakeholders, the people who will promote you. And when I talk about demonstrating the art of the possible, going after the low-hanging fruit, delivering business value quickly, the quick wins — which my team hates me talking about — they need to be demonstrating art of the possible. They need to deliver business value quickly. They need to be of value to the business and to stakeholders and to do them for a stakeholder who’s then going to publicize the success that you have delivered. That’s really important. I think that perhaps I painted too dark a picture earlier on. There’s always somebody in an organization who’s gonna promote and support you.

Ask the right questions to find the right fit

Satyen Sangani: (05:43) Yeah. And I would imagine that as a person that’s done this multiple times, part of your interviewing that institution, when you are trying to decide whether it’s the appropriate place for you, is to determine whether or not there’s a sufficient organizational will to make the change, at least within the people who have the power. How do you qualify for that? What do you ask when you’re interviewing that company?

Peter Jackson: (06:02) When you go for that interview or when you are applying into an organization for the role of chief data officer, ask the questions. Some of those questions are along the lines of: What sort of chief data officer do you really want? Do you actually want the chief data officer who’s going to focus on data governance and data management, or do you want a chief data officer who’s more, actually, a chief data scientist. So make sure that the organization understands what it is they actually want and that you fit that. Because if there’s confusion there, it won’t work well. And that’s why I think, well, one reason I think a lot of chief data officers over the last three or four years have failed and their tenures have been short, is because the organization has thought they want “this” and they’ve gone out and they’ve actually got “this” or they thought they want “this” and what they really needed was “this.” And they’ve gone out and they’ve got “that.”

(06:46) So getting that alignment and asking the right questions to understand, What are your aspirations? What is it you want to do with the data? What are your biggest problems you’re trying to solve? What are the things we’ve got to get after first? That’ll give you an indication of the sort of chief data officer they need.

I think then asking very transparent questions, such as, Where does this role report into? Because if the chief data officer is reporting into a role that’s too junior, you can’t effect change from a junior position. You have to be very close to the COO or the CEO to effect change across an organization. So, being very clear in asking lines of questions: Who does this role report into?

(07:26) I think also asking the question is, Who is promoting this role? Who are the stakeholders I’m working for? Where are we going to deliver business value? And understanding how senior and where they fit in your organization is important. I think one of the other questions to ask is, Where does data fit in the gateways of your change? Because if data sits in things like the business design authority and the technical design authority and the change management committee, if data is in those gateways of change, then that will give you an idea of how promoted and supported and what the appetite is for change through data.

Satyen Sangani: (08:01) Those are phenomenal questions. And does that change for a first-time CDO relative to a multi-time CDO? I mean, I would imagine that the first-timers are just trying to get the title in the job in many cases and, look, everyone of us has careers and everybody wants to grow into it. And I remember when I was trying to get my first job as a VP, you think about those things; you think about those titles. I can imagine that at those moments you wouldn’t be as focused on qualification. What bits of advice would you give those individuals that are just about to start and I guess, maybe, is there any different advice that you would give those individuals?

Peter Jackson: (08:40) Sound advice for anybody looking for a job change or a career promotion or a bit of a leap into the dark, is go in with your eyes open. And you may not get the right answers or the perfect answers that you would like to those questions, which we’ve just discussed. But so long as you are aware of the challenges you’re going to face, and if you still want that job title and step into that role, then at least you will understand what problems you’re going to face and what you’ve got to overcome. I think just getting swept away, almost, by the excitement and the emotion of landing your first role as a chief data officer, that could be dangerous.

Satyen Sangani: (09:15) Yeah. It’s so hard to turn down though. The ring, right?

Peter Jackson: (09:20) I think, even if you found yourself rapidly in an uncomfortable position, “Oh, this isn’t quite what I thought it was,” and you’re thinking, “Wow, how am I going to make this work?” You need to find some real deliverables that you can deliver quickly. You need to get those things onto your CV. You need to have some successes that you can talk about, however hard, or perhaps unrealistic or awkward, the situation you find yourself in. As data professionals, there must be something that you can do that proves value or proves worth for your time in that engagement.

Satyen Sangani: (09:52) Yeah. I love that advice because I think it’s often true that data professionals find themselves in a circumstance where the organization’s eyes are bigger than their stomachs and the organization … everybody talks about data, but people don’t realize that the human transformation is more expensive than the actual technological transformation. In some sense, I think the technological transformation is all that needs to happen. This notion of saying, “Look, it doesn’t really matter whether or not you’ve got fans or detractors or whatever, but you just got to go win,” and you’ve got to go do something that is a quick win, is really sage advice. What are those opportunities? Can you give our listeners some examples of what some of these quick wins have looked like in your own career — or even what are some of the quick failures?

Peter Jackson: (10:39) If I may, before I answer that question, I think I would echo Caroline here and say, you will always find some data cheerleaders in the organization. Go and find them because they will support and help you. There will always be some people who actually want to get on the data agenda and have some good ideas.

What are some of the quick wins? I think sniff out the spreadsheets, wherever you find a spreadsheet in a business process, that’s an opportunity for a quick win, because you might be able to automate that process by using some technologies, by teaching people new skills, giving them new tools, and that will inevitably get rid of the cut-and-paste. It will reduce data errors. It will speed up the process. It will make it more cost-effective and more cost-efficient. So sniffing out the spreadsheets because go after them.

Satyen Sangani: (11:26) Ooh, let’s just go deep on this one because I love this one. So there are spreadsheets and then there are spreadsheets. How do you know when you found a spreadsheet that is a ripe target for “chief data officer-iness”?

Peter Jackson: (11:45) I think if you’ve got a spreadsheet that is running a complicated data process, that is where you need to start. Big corporates, especially which are regulated, should not be running their business processes on spreadsheets. Spreadsheets, as you alluded to there, are an amazing tool. I mean they’re great! Accountants and actuaries love a spreadsheet. That’s fine. But don’t run business processes on spreadsheets.

There are many tools out there to automate data flows and I’m not talking about RPA. RPA is a complete red herring that will actually just rebuild your spreadsheet processes in a technology that doesn’t rebuild the business process. But think about some of the technologies that you can deploy for automating those processes where the spreadsheets are sitting.

Satyen Sangani: (12:32) I love this spreadsheet concept because I think that on some level, one of the things that I’ve observed is that almost every application or every process starts as a spreadsheet because it’s the simplest way to get going in almost anything. I mean, obviously there’s some things like financial, which — maybe day one — you start with some accounting software, but by and large, everything starts as a spreadsheet. This idea of find-the-spreadsheets in particularly complicated ways is, I think, a really wonderful bit of advice. What are the other areas?

Peter Jackson: (13:02) Data visualizations. I see an awful lot of people or organizations doing data visualizations and they’re sort of hand-built, they’re crafted. They take a long time to build, whether these are… And often they go into PowerPoint presentations for reports to boards or to stakeholders. I think automating those and bringing the data together properly behind them, so they can be interactive dashboards rather than something that’s static in a PowerPoint: I think that’s a big win. I think that enables stakeholders and senior execs to understand the data, to interrogate the data better than having it in a PowerPoint, which you can’t interact with. You are really reading the story that’s presented to you rather than investigating that story yourself.

(13:51) I think that data visualizations is another space. Other low-hanging fruit is again echoing my colleague, Caroline. I know when she talks about coffee and cake, sit down with your stakeholders, get them a cup of coffee and talk to them about their business problems. And if you can get them to say, if you can pick up that moment, they say, “If only I could….” Now that might be, if only I could make this process faster, or if only I could predict how our customers are going to behave next month, there’s your low-hanging fruit.

Satyen: How do you know when you found a spreadsheet that is a ripe target for “chief data officer-iness”?

Peter: I think if you’ve got a spreadsheet that is running a complicated data process, that is where you need to start. Big corporates, especially which are regulated, should not be running their business processes on spreadsheets. Spreadsheets, as you alluded to there, are an amazing tool. I mean they’re great! Accountants and actuaries love a spreadsheet. That’s fine. But don’t run business processes on spreadsheets.

Rise of the Chief Scientific Officer

Satyen Sangani: (14:20) One of my mentors and a long-time board member, Dave Kellogg, he’s a marketer. And he talks about “People are willing to buy from you if” and he says, “The first thing is they think they need to believe you understand their problem. And then they need to like you. And then they need to be able to believe that you have a solution, but it’s really in that order.” And the solution is actually only the last mile of the circumstance, because if they like you and they believe you, they understand you, then they’ll give you more of their time. And I think that’s a particularly human element of this.

I want to go back to one other thing that you talked about though, because you mentioned a title that I’d never heard before in the context of a non-basic science-based institution, which is this chief scientific officer. Is that a title that you’re hearing more and more of? Or when does that title come up? Because I’ve not encountered that.

Peter Jackson: (15:14) I think I may be sort of trailing that one a bit early, maybe misleading you slightly. I think I’ve only heard of that in one circumstance. I think what we are hearing about is the chief data science officer. And I think that we are going to hear that increasingly. I think over the next three or four years, as we move, as organizations have got the fundamentals of their data right, we’re going to be looking more and more into that value-add.

In other words, okay, we’ve now got our data governed and understood and managed properly and owned… How do we get value out of it? And that’s going to be the analytics and the data science. I think we’re going to see a shift in titles away from a pure CDO to CDAO or a chief data science officer.

Satyen Sangani: (15:53) I actually love this concept of a chief scientific officer because science is a process and data is an object. If you are the officer of data, you’re officiating this like non-animate thing. Whereas if you’re an officer of science, you’re actually furthering a way of thinking and a process of thinking. When you said it, I thought, “Oh wow, that’s actually super interesting.” And actually that’s what we all ought to be striving for.

Peter Jackson: (16:21) Yeah. And I think in some of those organizations that are what I would call data native, in other words, the data really is what they do. It’s not asset management, it’s not property management, it’s not recruitment or whatever. They are really around data and easy examples are Airbnb and Uber, for example. They don’t own hotels. They don’t own vehicles. They own data. I think in those organizations, the role of a chief scientific officer is probably right.

How to pick the right vendors

Satyen Sangani: (16:50) Another large part of the CDO’s job is navigating relationships with vendors. Peter has worked as a CDO as a client and as a vendor. He walked us through his perspective on how CDOs can make the most of their vendor relationships and choose the best tools for their organizations.

Peter Jackson: (17:06) Chief data officers spend a lot of time talking to vendors — a lot of whom we don’t want to talk to. Unless you’ve got a very good gatekeeper, a lot of your time is spent fielding off vendors or talking to vendors. I think helping vendors understand how to approach a chief data officer would help us all. All vendors believe that their technology is a solution, the solution to the chief data officer problems. It’s not. It’s part of the solution. It will be one piece of the jigsaw, not only of the new technology, but believe me, the legacy technology as well, and the existing suppliers and organizations need to understand that; vendors need to understand that.

Satyen Sangani: (17:45) Yeah, it’s funny. I mean, obviously, I and we are a vendor, not that this podcast is overtly focused in any way on being a vendor, but we certainly have a point of view and a position. And I think about how many different technologies are both being born every single day, but of course exist. And you look at those data market scapes and there are hundreds of technology companies that are there. And as you mentioned, some percentage of a CDO’s time is with vendors. How much time is that? How much do you estimate that would be for a given CDO?

Peter Jackson: (18:17) You could spend 365 [days], 24/7. There are so many. Absolutely.

Satyen Sangani: (18:23) But what do you think the actual average is? I mean, what has it been for you and your experience?

Peter Jackson: (18:28) I think it all depends on the stage you are in your transformation. If you’re early in your transformation, if you’re in that planning phase, you have to choose your partners. Now whether that’s an SI or a third-party delivery company, professional services, all the technology, you have to choose who you’re going to work with. Early in a transformation, you’ll spend more time engaging with the market to understand what is there, what really will meet your problems, what blend of technologies and people you’re going to need. I think that later in a transformation or when you get to a more run rate, a run stage, that time spent with vendors would be much less.

(19:08) I think that early on you’re going to be spending perhaps two, three days a week talking to vendors, thinking about vendors, thinking in your mind as to how they fit together, why that one and not that one? You’ll be talking to your procurement teams. Does that fit better for us if we procure it that way or would we have to compromise perhaps on the vendor because we want to procure things that way? It becomes quite complicated. So you will spend a lot of time with vendors or thinking about vendors early in the transformation stage. As you move later on, that will reduce because once you’ve chosen certain tools for certain jobs, you don’t need to engage further with the market for a while. You’ve just got to get on and do the job.

Satyen Sangani: (19:49) What are the success patterns and the failure patterns around choosing your vendor landscape?

Peter Jackson: (19:54) I think the first thing is really understanding whether you’re going to be able to find the people, the resources, to work with and support your selection. In other words, have you bought something that is so niche that you can’t find the right people with the right skills to use that tool? Matching realistically a tool with a skill base and resources that you either have, you can train up or you could attract. I think that’s one thing.I think the next thing is truly understanding cost. I think a lot of organizations walk away from, and after a period of time, they might think this was a bad decision because they didn’t understand the full implication of cost when they were acquiring that technology the first time around. And it becomes unsustainable for what they want to do with it. So there’s two things.

(20:39) I think the third thing is when you’re selecting a vendor, talk to them about roadmap, make sure that this technology has a roadmap that matches your roadmap. In other words, the things you want to do tomorrow, or next year, it’s on the vendor’s roadmap, if it’s not already in the technology.

And then I think the final thing — and this will make you smile, Satyen — I think buying the technology from a vendor who can help you get value from it, who knows how you’ve got to organize yourself to get value from that technology. In other words, what operating model you need around it, that is a key to success. Because just buying the license doesn’t deliver value.

Satyen Sangani: (21:17) I remember early in my career at Oracle, there was a kiosk inside of the lobby for the executive briefing center in one of the main buildings, the executive building at Oracle. And there’s a quote from Larry Ellison on it. And he says, “People always,” and this is typically Larry quote, but it says something like, “People always ask me, whether or not I have the features that they want in order to build a business process that they want to build. And what I always tell them is that’s the wrong question. They should be asking us how they should be implementing the software and how they should be building the process to best do what it is that our customers do.” And I think that’s lost on lots of people because people see software as feature and functions and not necessarily as a way to transform their organizations.

Peter Jackson: (22:00)That quote resonates with me really, really firmly because so many times I’ve seen organizations buy a new technology and then rebuild their old processes in the new technology, rather than actually thinking about: This is new. How are we going to do this new? How are we going to do this different to get to where we need to be?

Satyen Sangani: (22:19) Yeah, there’s all these newfangled startups that are out there. And you mentioned a little bit about this concept of like, are there people to implement it? But then how do you decide that you’re willing to take the risk of a new technology versus perhaps going with the thing that may be a little bit older, but perhaps a little bit better implemented. And how do you think about those decisions?

Peter Jackson: (22:39) I’ve long said nobody gets fired for hiring IBM. Got to overcome that mentality. There is risk in going with a new technology, with a new organization, with a young organization, but unless somebody somewhere is prepared to buy into that risk, how do these things ever get adopted and proven? I think that you have to engage with some of these new technologies that you think are interesting, that you think are going to deliver — potentially deliver — a significant difference for your organization, but you’ve got to engage with them in a low-risk way. Getting into an engagement where you can do proof of values, where you can do joint investigation projects together, where you can get into an MVP together.

Data Science Bootcamp

Satyen Sangani: (23:41) Building data literacy within your organization is also incredibly important. It’s something Peter has a lot of experience with.

Peter Jackson: (23:48) I think building training within the organization is important. And at L&G we built our data science BootCamp, which wasn’t to train data scientists. It was to train citizen data scientists. It was to make people in procurement, HR, in the lines of business, understand how you manage data, how you phrase a data science question, not to be afraid of math and not to be afraid of the technology. And I think that making it enjoyable and of value to them — doing a proper learning needs assessment and doing the longitudinal studies to see if it’s impacting their work a year later — is important.

(24:24) So you’ve got the empirical proof that it’s worth doing, but also doing those bootcamps in an environment where you’re asking them to bring their business problems to you and try and solve them through the bootcamp. They’re then taking value back to the business. That is what gets the training supported because then the line managers could say, That gave some value. That was a good investment, that worked.”

And I think if they’re structured properly, with the learning needs assessments, with the linear studies, then HR and learning teams can support and invest in them. You have to work with those teams. They are the professionals of training.

Satyen Sangani: (24:59) For the audience to be able to understand what these things consist of, how long did that bootcamp run for in terms of the duration of the actual course?

Peter Jackson: (25:06) There were eight-week courses. They were sort of side-of-desk. In other words, people didn’t come to the course for eight weeks. They were doing the eight-week sessions, which involved a number of online and live sessions during the course of the eight weeks. And we ran one every eight weeks and the team took a break for a month and then we’d run another one for another eight weeks. And we started with 20, a cohort of 20, and we ended up running three parallel cohorts of 20. So we were running 60 people through at a time.

Satyen Sangani: (25:33) For the site-of-desk work, was it one hour a day that people were expected to dedicate?

Peter Jackson: (25:38) I think on average it was an hour a day, which equates to five hours a week. Perhaps with some time on the weekend or in the evenings of their own. And if they’re invested, if they’re doing something that they find interesting and enjoyable, then they will invest that time. And if it’s for an eight-week period, it’s not like you’ve got to do this for the next year.

Satyen Sangani: (25:55) Five hours a week, eight weeks. That’s about 40 hours. How much of that was practical and how much of that was sort of didactic learning training?

Peter Jackson: (26:04) Very roughly 50/50. It was, “Here is a technique, here is a tool, go and use it on your use case.” Roughly 50/50, we wanted them to come away with something. The ultimate goal was something they would present back to their line manager and their teams back in the business.

Satyen Sangani: (26:22) And that to me also seems quite practical because if you think about sort of all of the vendor training that you’re going to be able to get, if you think about the internal systems, to be able to put together a 20-hour course sounds quite daunting. But then if you think about how much you can recycle and repurpose, that could become something that could be really practical. Getting those curricula together could be also quite informative.

Peter Jackson: (26:45) We built a very structured curriculum. We polished it each time we went through, we refreshed it, but we also signposted a lot of other things. We didn’t want to get into deep training in how to build a data visualization. That wasn’t what it was for. We would signpost, “If you want to learn more about using this technology or that technology, this is where the resources lie.” In their online university or in their citizen world or citizen lab, whatever they call it, for those technologies. Because there is — let’s face it, Satyen — there is a huge amount of resources out there available. People just need signposting to them, I think, very often.

Peter’s Predictions

Satyen Sangani: (27:20) As we cap off the show, I’d love to get your predictions. I’ll let you choose. You can either make a prediction about where data’s going to be going over the next five years. Any topic you’d like to be able to talk about, or alternatively, you could leave us with some advice. Which path will you choose?

Peter Jackson: (27:41) I think it’s a prediction. I think that it’s going to be that shift. I think we’re going to see more and more of a shift into data science. I think the job of the first-generation CDO will eventually come into its twilight years. As more organizations have done that, have actually understood their data, have cataloged it or are managing it properly.

I think in the next three or four years, we are going to be after value. People are really, really going to be after value in their data, partly because of the investment they’ve made in the role and the office of the CDO, but also because they now see those opportunities. And I think COVID has actually accelerated that.

(28:17) I think organizations now are understanding just how crucial good quality data is, having quick data that you can use quickly, having data you truly understand, increasing data literacy. I mean, I think the global population who’ve been trying to interpret the data around COVID on the evening news or from whichever president or whichever prime minister has been presenting it, saying, “That doesn’t make sense for the data they’re presenting yesterday.” We’ve all become more data literate. I think that the urgency is getting there. If we were accelerating pre-pandemic, I think we’re accelerating even faster coming out of that.

Satyen Sangani: (28:52) Phenomenal stuff, Peter. Thank you for taking the time. Thank you for joining Dta Radicals. It’s been wonderful to have you.

Peter Jackson: (28:58) Absolute pleasure. Really enjoyed our conversation. Thank you for having me.

Satyen Sangani: (29:01) Brilliant. As I reflect on my conversation with Peter, one thing stands out to me. Data’s composed of numbers, but a data culture is composed of people. As Peter said, it’s not just enough to choose the right tools. You have to choose the tools that fit your team’s abilities and skill sets. In other words, you have to always think about how you are using your team to the best of their abilities.

Because even though we deal with numbers, it’s people that transform numbers into insights, actions, and victories. Thank you to Peter for joining us on this episode of Data Radicals.This is Satyen Sangani, co-founder and CEO of Alation. Thank you for listening.

Producer Read: (29:40) This podcast is brought to you by Alation. Chief data officers face an uphill battle. How can they succeed in making data driven decisions, making the new normal? This State of Data Culture Report has the answer. Download to learn why successful CDOs partner with their chief financial officer to drive meaningful change. Check it out at alation.com/dcr3.