The title CDO started out as a joke
How did the role of Chief Data Officer come to drive data literacy at companies around the world? Find out how it all began in this interview with the first who held the title at Yahoo!
By Kate Strachnyi, DATACATED.
“The title ‘Chief Data Officer’ started out as a joke!”, says Usama Fayyad -the world’s first CDO- and continues; “Now it became a serious thing, I guess.”
IADSS Co-founder Dr. Usama Fayyad was interviewed by Kate Strachnyi after his keynote speech at the ODSC 2019 East event. An important part of this funny interview was about the evolution of the role “Chief Data Officer” and the future of it in driving data literacy and culture in companies.
Kate Strachnyi: You were the first Chief Data Officer at Yahoo, and more recently, you were the Global Chief Data Officer at Barclays. Can you tell us about how these CDO role has come about and how it has evolved? I know you said it started as a joke. Tell us.
Usama Fayyad: Yeah. It started out with the executive team, and Jerry Yang assembled to welcome me to Yahoo, and say, "Okay. What should we call you here?" And the topic came up of chief data officer, and everybody in the room actually laughed, thought that was sounded funny enough. Yahoo actually had this culture for irreverent titles, Jerry Yang called himself Chief Yahoo, for example, and so forth. So that was the first really definition of what it means, and we defined it at the time as an operational role that also had to worry about standards and governance, and making sure everything is taken care of, and well-architected, and so forth. So it had this dual role for me, at least in the beginning.
Then I went away from Yahoo, five, six years later, I joined Barclays, which is a 325-year-old bank, one of the major banks in Europe, based in London. There, the contrast was significant because the field had matured. I remember the first I gave a public talk as a Barclays executive, somebody walked up to me and said, "You know there's about 1,000 chief data officers in the industry?" Of course, I didn't mention at the time that, "Oh, yeah. This started out as a joke." So a lot more organizations took it seriously. A lot of organizations realized the need.
At a bank, there's a lot more emphasis on the governance side, on the policy side, and a lot on the privacy and the proper use of data, and especially for a European bank, where a lot of the laws are a bit more evolved and advanced than in the U.S. on how much data you're allowed to keep, how much you're allowed to use, etc. The differences, I would say, were very significant, both time-wise, and in terms of what the role is like, and where you spend more of your energy.
Kate Strachnyi: Right. Does every bank, at this point, have CDO role? Are they mandated to?
Usama Fayyad: It's almost getting there. I think many banks fear getting in trouble if they don't have a CDO now. The industry basically demands an accountable individual, who is looking and seeing whether that organization is using the data properly, how they're using it, do they have the right limitations on its use. I used to call my role at Barclays the responsible use of data, but before you can use it responsibly, you need to make sure that it's usable, and available, and can be actually applied in a lot of these innovative applications as well while staying on the safe side. So one of the big programs I pushed there was I called it KYC, which is in banking, is know your customer, a big area of spend. I called it The Journey from KYC to UYC, where UYC stood for understanding your customers, which actually shows the business value rather than just doing it for a regulatory reason, which is what KYC is all about.
Kate Strachnyi: You mention a skills gap, which is validated by The Data Literacy Project led by Qlik, who says that only 24% of the global workforce is data literate. What is the first step a CDO should take to drive data literacy and data culture within their company
Usama Fayyad: That's a great question and one of the things that I've done personally with a lot of our engagements as Open Insights, say with some of the largest organizations in the world, we actually quickly realized that data literacy and data culture is a big part of making the stuff useful and usable. To that end, we actually launched something we call Data Academy in a lot of these big companies that are targeted at basically achieving data literacy. There's literacy at two levels. There is what an average employee should know about data, why it's important, why it's important to be safe with it, why it's important to be sensitive to it, why it's important to make sure it's kept safe.
Then there is the part of what's possible with it, which goes more into the analytics. A lot of data analysts and a lot of business analysts don't even know the art of the possible on what you can do with data mining algorithms, with machine-learning algorithms. I mean, a great example in monetization, when I joined Yahoo within two years, but putting in the right machine-learning system, data systems, data management regimes, and some of the big data technology, we were able to generate, without much work, $800 million of additional revenue derived from targeting for Yahoo. Basically, they were selling the same ads at 10 to 20 times the price that they used to sell at before. Because now, they could actually do targeting that they could prove to their customers, "Well, this well-targeted. This is reaching the right audience, which resulted in a much better dynamic!” both with the advertisers willing to pay more, and the consumers being slightly happier. Nobody loves to see ads, but if you see ads that are relevant, it's a better experience than ads that are completely irrelevant or at the wrong time. That created a good dynamic there that actually allowed us to create value. So it's very important to have that data culture, and that awareness and part of that is both the data literacy as well as the data science literacy.
Kate Strachnyi: Okay. Let's say you have a big company like Barclays. How do you actually implement something, where let's say the average admin or somebody who might touch data once in a while, but is not really a data analyst or really specialized to work with data, how much do you teach them? How much should they know?
Usama Fayyad: Yeah. It's different levels. When we ran these data academies -we did that at Barclays. We did that at Barclays Africa, as well, we did that at MTN, which is Africa's largest telecom, we did it at many companies in the U.S. and in Europe- The way you do it is you create different tiers for different levels of awareness and different concepts that you want to emphasize. So somebody who doesn't touch a lot of data, we probably want them to be aware enough to understand that KPIs matter, which KPIs they should pay attention to. Our philosophy is every KPI, or key performance indicator, should have a version that goes all the way from the board to the CEO, all the way down to the lowest level in the company. If you don't have that, then something is broken in the way you're --
Kate Strachnyi: You mean it should be the exact same KPI or --
Usama Fayyad: No, no. It's at the different level of granularity, right? The person in the operations probably wants to see a lot more detail. The person on the board doesn't want to see any detail, but wants to see the big signal and where should they pay attention to what's going wrong, like which region is veering off forecasts and so forth. That insistence, culturally, on saying, "Every report has a version that goes all the way from a board to the lowest level, even though it may not go to the board ever." Is instills the culture of thinking about it that way. Most of the training is about awareness, and as you get down to specialized roles, it becomes more technical and more around what's possible because those are the people who can actually make a difference, and help you make stuff happen.
I've been involved personally in many data science projects, where we would work hard to come up with an amazing predictor of something. One example that comes to mind was working with one of the very large car manufacturers where we were trying to predict the sales by car and by model in different micro-markets. The problem was very hard. We cracked it in a very innovative way, and in the end, we discovered that the executive team was unable to do much with those predictions, even though they were super accurate.
In those years, incentives on automobiles were a big deal. We figured incentives, where you pay consumers money back for buying a car, was the easiest thing to reprogram, right? You could change them by market, by demand, etc. It took, basically us working with the executive assistant, who prepares the... So we worked with the Executive Assistant to take the spreadsheet that goes to the executive team, and just allow us to colour code certain cells as in these are red because you're probably overpaying. These are green, they're great. And these need attention or change. Just doing that color-coding, went from great predictions, where people were just watching them as a spectator sport to like, "Oh, now, I can act on it. In this market, I need to change it up for down."
Now, it became actionable. That's the importance of basically involving everybody because the whole supply chain of data and the consumer chain is reliant on a lot of people doing a lot of different roles.
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Original. Reposted with permission.
Bio: Kate Strachnyi is an author, Advisory Board Member of IADSS, Udemy instructor, and host of the Datacated Weekly, a project dedicated to helping others learn about various topics in the data realm.
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