By Jay Hawkinson, Fractional Chief Data and Analytics Officer | Hawksroost LLC

 

What Is a Fractional Chief Data Officer?

A fractional chief data officer is a senior executive who helps companies treat data as a business asset, improve decision quality, and build the governance needed to support analytics and AI. Instead of hiring that leader full time, you bring them in part time or for a defined engagement.

For mid-market firms and PE-backed companies, that model can be especially effective. You get executive-level data and AI leadership without committing to a permanent seat before you know what the long-term organization should look like.

In many businesses, this role also overlaps with analytics, digital, and AI strategy. You may also hear it called a fractional CDAO. The title matters less than the outcome: clearer decision ownership, better data discipline, and more confidence in how AI is used across the business.

 

Ask Yourself This

AI is moving fast inside growing companies. The vendor demo was compelling. The pilot is underway. Leadership wants speed, scale, and results.

But before your next AI deployment goes live, ask one simple question: if the model recommends the wrong move and your team acts on it, who owns the decision?

If the answer is vague, your issue is not just technology. It is governance, data readiness, and leadership. That is where a fractional chief data officer can make an immediate difference.

 

Why AI Projects Need More Than a Good Model

A lot of companies assume AI success is mainly a model problem. In reality, many AI initiatives struggle because the business is not ready to trust, govern, or act on the output.

In my experience, the friction usually shows up in familiar ways:

• Teams disagree on which data is reliable
• Leaders are not aligned on who can override a model recommendation
• Business owners are unsure when to trust the output, when to question it, and how to escalate an issue if something looks wrong

Those are not vendor questions. They are leadership questions. A good model can produce an answer. It cannot define accountability, clean up fragmented data, or build the operating rhythm that turns outputs into decisions.

 

How a Fractional Chief Data Officer Helps Before Launch

Before an AI initiative goes live, a fractional chief data officer can help your business:

• Clarify which decisions will be influenced by AI and who owns each one
• Assess whether the underlying data is accurate, complete, and fit for purpose
• Define how teams should use model outputs in day-to-day workflows
• Create feedback loops so results can be monitored, challenged, and improved
• Give senior leadership and the board a clearer view of readiness, risk, and next steps

In engagements I have led at global manufacturers and CPG companies, this foundational work is consistently what separates a valuable AI deployment from one that quietly loses credibility within six months of launch.

 

When to Bring One In

The best time to bring in a fractional chief data officer is usually earlier than most companies think. This role adds the most value:

• Before a major AI initiative or vendor selection: So data readiness and governance are addressed before commitments are made
• During an ERP migration or platform modernization: Because data decisions made in these projects have long-term AI implications
• After a pilot that generated interest but not adoption: To diagnose why outputs were not trusted and build the foundation for a second attempt
• When investors, PE sponsors, or the board begin asking harder questions about AI risk and accountability

If you wait until after something goes wrong, you are often paying to learn a lesson you could have prevented. Most engagements produce measurable results within 60 to 90 days.

 

The Question Worth Asking Now

Before your next AI deployment, ask your leadership team: if the model gets it wrong, what happens next?

If the answer is unclear, the problem is not just technical. It is organizational. A fractional chief data officer can help you build the data foundation, governance structure, and decision model that make AI usable in the real world, all typically within the first 60 to 90 days of an engagement.

About the Author

Jay Hawkinson is a fractional chief data officer and board-certified director (NACD.DC) who helps PE-backed and growth-stage companies build practical data, analytics, and AI operating models that support better business decisions. He has served as the first functional CDAO at Valmont Industries and led a 60-person data and AI organization at Lamb Weston. He is a Forbes Technology Council contributor with four published articles on AI governance and enterprise data strategy. His advisory platform for senior data and AI leaders is at vtcdo.com.

 

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