Selling to a chief data officer

A chief data officer is judged on value they often can't prove. They own the data, the governance and lately the AI ambitions everyone else announces. But the revenue those things create shows up in someone else's numbers.

Sell to that gap and you'll get a reply. Sell them another platform and you won't.

What they own

The CDO role varies more than most. In some companies it sits under the CIO and runs the data platform. In others it reports to the CEO or COO and owns governance, analytics and the data side of AI.

Find out which before you write. The job post that hired them, if you can find it, tells you. So does their own LinkedIn summary. A CDO who writes about governance and data products is a different buyer from one who writes about warehouse costs.

Either way they usually own some mix of data quality, governance policy, the analytics team, and readiness for whatever AI program the executive team announced last quarter.

What they're measured on

Here's the hard part of the job. When the data is good, the credit goes to the business unit that used it. When it's bad, the CDO hears about it.

So they're measured on things that are hard to put on a slide. Trust in the numbers. Time to answer a business question. Whether an AI pilot can move to production without a data cleanup project first.

That last one is where a lot of services work lives right now. Executives want AI results. The CDO knows the data isn't ready. That tension is your misery hypothesis more often than not.

What they ignore

They ignore tool pitches. A CDO gets a steady stream of messages from platform vendors and the partners who resell them. They've seen every catalog, every observability tool, every lakehouse demo.

They ignore vague AI claims. "We help companies get AI-ready" says nothing they don't already know.

And they ignore anything that sounds like it'll create work for their team before it removes any.

Signals that matter here

A new CDO is the strongest. The first 90 days are when they set the agenda and decide which outside firms they'll trust. See the first 90 days of a new CIO for how that window works. It's the same shape for data leaders.

Hiring for the problem is next. A cluster of data engineering, governance or MLOps posts says the team is building, and they rarely build fast enough.

Then business pressure. A 10-K that names data quality or governance as a risk. An earnings call where the CEO promises AI results. When the CEO promises, the CDO has to deliver.

And tech stack. A job post naming a specific platform tells you what they're running and often what they're moving to.

An opener that works

Say a specialty insurer's CEO said on the last earnings call that AI underwriting will be in production next year, and the company just hired its first CDO.

Saw you joined as the first CDO a month after the earnings call committed to AI underwriting next year. That puts the data foundation on your desk in your first 90 days. My guess is the models aren't the hard part, it's that underwriting data lives in three systems that don't agree on what a policy is. Is that close, or is the real gap somewhere else?

Notice what's missing. No platform. No capability list. A guess about their data and a question they can answer in one line.

If they correct you, they've told you the real problem. That's the conversation you wanted.

Reach the rest of the committee

The CDO rarely buys alone. The CFO or COO often holds the budget for anything tied to an AI commitment. A head of data engineering or a platform lead is the technical voice. Write to each with a hypothesis about their own piece of it.

A CDO who hears the same thinking from their engineering lead and their CFO knows you've done the work. That's what earns the reply.