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Top of the data heap: First-hand advice on becoming a chief data officer

'You have to be fairly whole-brained, you have to understand the technology, and understand how it's applied,' says Ceridian CDO David Lloyd.
Written by Joe McKendrick, Contributing Writer on
Man showing a complicated graph on a screen to a room of people

Half of chief data officers surveyed by Exasol felt organizations' expectations of the CDO role were misinformed.

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So, who wants to make a career of amplifying the power of data? In an era when data is as valuable as gold, data professionals are in demand, and the career path now leads upward to chief data officer. 

However, CDO is a role loaded with plenty of uncertainties, as explained by Thomas Davenport and co-authors in Harvard Business Review, calling it "the most unstable C-suite job." As they explain, "Tenures are short, turnover is high, and as in the early days of the CIO role, many companies don't seem to know exactly what they want from its incumbents." The average tenure for CDOs is two and a half years, they estimate.

It doesn't have to be this way. CDOs can leverage both technology and business savvy to not only play a leadership role in building a data-driven business, but also partake in a highly satisfying job role. Demand is high; a survey (PDF) by NewVantage Partners finds 74% of corporations have appointed chief data or analytics officers, or both combined into one role. At the time of this writing, there were more than 3,000 listings for chief data officers or related roles on LinkedIn

Also: Workforce trends that will shape 2023

To get more perspective on the roles and challenges of this emerging profession, I had the opportunity to sit down with David Lloyd, tech entrepreneur and currently chief data officer at Ceridian, at the company's recent workforce empowerment conference. Here are some of his takes.

Q: You're a chief data officer. What does a CDO do, and what do I need to do to become one?

The career path is interesting. For me, I came up on the computer science side, as a developer, dealing with data. I dealt with a lot of technologies. But, at the same time, I also took a path down the business route, and got my master's degree in business. And then built companies from there.  […]

When I look at what would make a good chief data officer, you have to be fairly whole-brained, you have to understand the technology, and understand how it's applied. You have to be able also to look at it from a business context -- the customer's business context. You have to be really engaged with the customer. You can't be that ultra introvert that just wants to sit there and code. There are days where that's fine. But you have to really be out there, interacting with customers, to understand the business problems that they're seeing, and the value they're trying to extract from their data.

In all fairness, it's different for within an environment like Ceridian than if I was a chief data officer of a bank. We're focused on overall data and intelligence governance for the entire organization. We're there to ensure from a customer data point of view [that] the application of artificial intelligence -- or as I prefer to call it, augmented intelligence -- is supporting the other product areas in terms of asking the right questions. 

I also look at how our advanced reporting is used, and how it goes to data visualizations, whether you look at predictive analytics tools or prediction and action. So data science, engineer, product management, [and] data related to customer data is what sits within my world.

Q: How is chief data officer different from chief digital officer?

Lloyd: The chief digital officer is very much focused on the whole digital transformation. Their mandate is less about the data or the application or intelligence, or things of that nature. They're looking at how to transform an organization from a certain level of maturity with their digital use of technology, to a different level. The chief data officer is very much focused on both internal data and external data, looking at the corporate data, with perhaps a customer set of data, and what they're designing and building.

With us, our CIO carries the accountability of looking at the internal data, and has her own team that does that, while my team focuses on how we enable our customer data. 

Q: The way data is used and protected -- or not protected -- is an important issue these days, and must make the chief data officer's job very interesting.

Lloyd: We spend a lot of time working with our customers' data where we're allowed to by contract. This is not free-for-all-you-have-the-data, this is the customer entrusting us with their data, to de-identify it, and use it in ways that are ethical, and at the same time, beneficial. I think that combination of both technical and business skills is important here. 

People may feel they have a mastery of something, but don't realize where their data is going, or how their data is being used. I think it's such an important thing for an organization to have that awareness, that understand how that data is actually being applied. 

The whole area of AI ethics, regulatorily as well as within organizations, is just developing. Look at the framework developed in New York, regulating the use of intelligence in hiring. It requires that you have to audit the way in which your algorithms are actually functioning. But there is no standard for audits. There are no general accepted accounting principles, like there is in finance, for looking at a financial system for AI technology. This is a great example of where the technology is running way ahead of the regulatory frameworks. 

That's always been the nature of technology.  We run ahead with it, and then we figure out how to lasso it, and get it into a place where people can feel comfortable with it, and even, although there are a lot of startups popping up, the startups' ability to support organizations in doing these kinds of audits for bias or drift and things like that within the intelligence side.

Q: And audits tend to catch things too late, right?

That's why predictive analytics is becoming so important. The action you can take from predictive analytics is really about the data finding you. 

But the challenge is the sensitivity of data. For example, if you were the only individual hired by a company yesterday, someone might look at your hire date, that's identifiable. So data de-identification is going to be an important area. How do you de-identify the data so it can be effectively used for customers, on behalf of customers, in the way that doesn't compromise the data. And provides that level of security and trust that the data's there, but it's not addressable back to a particular individual. I think that's an important part that a lot of companies are going to have to catch up on.

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