Let’s clear up two common misconceptions right away:

  • You do not have to have a CDO or Chief Data and Analytics Officer (CDAO) in your organization to become data-driven
  • The most digitally native companies don’t have CDOs or the equivalent because their existence is predicated on the idea that being data-driven already is in everyone’s DNA. Those that do have CDOs are primarily legacy organizations, which constitute about 90 percent of the Fortune 500.

So why is a CDO still a vital necessity?

Because if you’re part of that 90 percent of legacy companies, you aren’t likely to be competing with Amazon or Apple, but rather with the other 90 percent. That means the pace of adoption can be more gradual and staged over time. Of course, it doesn’t disqualify you from being data-driven, either. Legacy organizations like American Express, JP Morgan, and MasterCard are highly data driven. Data is a critical asset to all of them, and they’ve been aggressive in terms of their investments, utilization, and orientation around leveraging data and more recently AI. As I cited in my book, AmEx has roughly 2,000 PhD data and analytics statisticians and practices extensive test-and-learn methodologies with data.

CDO origin story

For those who don’t recall, the CDO role really took hold in major banks. After the 2008 financial crisis, there was a mandate for banks to have better control of their data for risk and regulatory purposes. This was essential at the time even though banks, of course, have always been data-driven businesses because they don’t have physical products that sit on a shelf. As a consequence, a lot of the work that financial services firms have done around data has been first and foremost defensive, to make sure that they’re in risk and regulatory compliance.

But they’ve also used data to put them on the offensive to help them increase customer acquisition, boost the cross-sell of products and services, improve the customer experience, and make better recommendations in terms of products and services customers might use. Other industries, like life sciences and pharmaceuticals, have accelerated these data efforts in the past five to seven years.

Hitting a moving target

As I’ve often noted, the average tenure of the CDO is nothing to crow about, and averages approximately 2.5 years. But this shouldn’t be entirely shocking, for a number of reasons:

  1. The CDO is a relatively new job. It’s only been 15 years since it was first established on a formal basis, and many organizations have only had five years of the CDO experience. Some hire a CDO to check the box of having one without sitting down to think through the role and responsibilities.
  2. There’s no commonly understood career blueprint. Organizations are still figuring out what is and isn’t in the CDO’s remit. Some have been elevated from subject matter expert in one area of the business to C-suite executive over the whole enterprise, practically overnight. The C-suite can be a tough place to work. The expectations to communicate, persuade, exert influence, and build strong relationships within the C-suite and across the organization skyrocket. The expectations can be intimidating.
  3. Data is a very broad topic. Data flows across an organization from all the points where it’s produced to all the points that it’s consumed and to all the people that touch it along the way. Assuming C-suite responsibility for such a fluid asset can be challenging to say the very least. 
  4. Reporting lines aren’t set. There isn’t one size that fits all or one agreed-upon place where the CDO role should reside. This can lead to new hires being treated with suspicion or even dislike, especially with CDOs being lionized by industry groups and in CDO events as the great saviors of data and companies.
  5. The primary thrust can shift constantly. Over the course of a single week as a CDO you may be called upon to go on the defensive (Let’s focus on the risk and regulatory elements), the offensive (Let’s focus on business growth, business acquisition, and customer treatment), or the hygienic (Let’s gather the data and make sure it’s clean for use).

 The current reality is that we’re also in an investment down cycle, with CDOs being held to greater accountability based on the last few years of growth-driven investments. There is much greater scrutiny being paid to all data and analytics investments. CDOs need to be able to show a direct line between data and analytics investments and the business value that will be delivered, or getting further investment will be a tough sell.

Charting a path to value

When CDOs ask me about what it will take to create success in their jobs, I advise them to do several things:

  1. Know what you’re solving for. It sounds basic, but what business problems are you trying to solve with your data programs? How can data be used to generate results that can be tracked and measured? Don’t fall into the common trap of building something in an organization just to prove you can build it. Just because you build it, doesn’t mean they will come. This often creates more resentment and dissatisfaction than it does approval.
  2. Focus squarely on delivering business value. When I’m asked these days, I say to organizations that if you’re not getting measurable business value from your data and analytics investments, you should shut them down immediately. Let’s face it: if you’re spending without any tracking or sense of the value that you’re trying to get from data investments, why are you making them? The most successful organizations begin by identifying the business use cases for data that lead to business improvement. 
  3. Find a business sponsor or business leaders who will act as your champion. These become the people that will proclaim your successes. They must see a business benefit and have a need. You can bring a horse to water, but you can’t make them drink. Always, start by identifying a business need where data can help. Improving the customer experience is always a great place to start. When I hear about Jamie Dimon at JPMC saying that two of their major accomplishments for the year would have been impossible without their data and analytics team, that’s an example of great data leadership communicating what they do. Compare that to a CDO grousing, “We’re doing great things, but nobody in the organization sees it or appreciates it.”
  4. Be your own best champion. Admittedly, creating meaningful change in a large enterprise takes time. A top executive once told me that nothing meaningful and transformational happens in a large company in under a decade. I thought him a cynic then; in retrospect I think he had a good point. So build trust with your business partners. Find one business question that you can solve for them. That builds trust. Do it again. That builds momentum. Get out there, tell the success stories, build new relationships. That is how you earn credibility and create traction over time. 
  5. Become the voice of reason on GenAI. Generative AI can look at everything that’s been created, synthesize it in a rapid fashion and say, “Here are the takeaways and recommendations.” It can accomplish routine tasks exponentially faster in some cases. But it can’t predict the future or create something that has never existed before. For those CDOs that have responsibility for GenAI, begin with establishing safeguards for experimentation and learning. The ethical use of data and AI should be foundational for all organizations. The last thing you want to do is send out any kind of misinformation or biased information to a customer thanks to AI. That can be fatal.

Cause for CDO optimism

There’s no question that data and AI leadership are only growing. A dozen years ago, only 12 percent of organizations had CDOs. Now it’s roughly 85 percent. The demand is increasing and isn’t going away. That said, the shape, form, and nature of being a CDO – as well as where that role resides and what the responsibilities should be – are still very much a work in progress. 

The good news is, if you’re coming up in school or early in your career, I see tremendous demand. The field is rich with opportunity. Data and AI are not going away. The future is bright, but be patient. At least you’re unlikely to have the experience I did about five years ago when the division President of a Fortune 1000 company called me in and told me they wanted to become a data-driven organization. I asked them what their timeline was, and the executive replied, “We’d like to achieve this over the next month. How can you help us?” Needless to say, I didn’t stick around. Hopefully, we’ve come a long way since then