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How CDOs can show the business impact of data
CDOs can defend the spend on data programs by tying outcomes to saved time, lower risk, revenue gains and AI projects that reach production.
Boards are demanding clearer ROI from data investments as AI spending rises, but many chief data officers still lack the metrics to prove their value in financial terms.
Capital One is often recognized as one of the first major companies to appoint a CDO in 2002. Financial services firms adopted the role to address mounting concerns for data governance and regulatory compliance. But the job has moved well beyond defensive oversight.
Today, CDOs are expected to help turn enterprise data into business value, especially as organizations move their AI pilots into production. An IBM study from 2025 found that 92% of CDOs say they must focus on business outcomes to succeed, yet only 29% have clear measures to determine the value of those outcomes.
A four-part methodology can help CDOs demonstrate the value of their work in terms that executives understand. The goal is to move from traditional technical metrics and show the financial value the data function brings to the organization.
1. Stop reporting technical activity
Even in technology companies, executive boards care more about income statements than architecture diagrams. To a CDO, technical and architectural measures, such as server uptime, query latency and cloud migration progress, are important to data teams, but they track effort, not results. Every technical measure should map to a business outcome that the board can act on.
To avoid reporting only in technical terms, connect every initiative to a stated business goal. For example, if the CEO names customer retention as the year's priority, the data program's job is to predict and reduce churn, not migrate servers.
Some CDOs enforce a business sponsor rule: no project proceeds unless an executive outside the data function -- whose own performance targets depend on the result -- commits to it. Executive sponsorship secures adoption, which in turn creates a clearer path to measurable value.
2. Report against baselines
A CDO can't claim reductions in data processes without first measuring the baseline. Before any initiative starts, record the current state. Common scenarios include a data analyst's hours spent locating, verifying and correcting data, or the number of data errors in executive reports that require fixes.
Baselines will matter even more as companies deploy AI agents that act on data directly, for three reasons:
- Comparison. Without baseline measurements, organizations can't determine if the agent produces better results.
- Scope. AI agents don't just accelerate existing processes; they introduce new activities that prior measurements didn't cover. For example, how many times a human must intervene to correct an agent or the compute cost of a completed task. Those indicators need a recorded starting point because they can change over time as systems, models and workflows evolve.
- Speed and scale. AI agents can magnify the impact of errors as they act across more data, systems and decisions.
3. Track progress and settle attribution in advance
Reporting against baselines works best on a fixed cycle, using red, amber or green status for each funded initiative. Attributing changes in metrics is more difficult. If the data team gives marketing a better, unified customer record and sales conversion rates improve, who gets the credit? The VP of sales might win the argument -- they are, after all, good at selling!
Negotiate how improvements will be attributed before the project starts. Outline what teams should expect from planned improvements, including new contributions to operations, and document those agreements. To gain more insights, run controlled A/B tests to isolate factors so the claim withstands scrutiny.
4. Price the work across four dimensions
CDOs can quantify data ROI across four dimensions: operational efficiency, revenue enablement, risk mitigation and AI readiness. Each dimension requires a distinct pricing method.
5. Tailor communications to stakeholder concerns
Every data investment affects executives differently. CDOs can tune reporting carefully without overstating results for specific audiences.
Donald Farmer is a data strategist with more than 30 years of experience, including as a product team leader at Microsoft and Qlik. He advises global clients on data, analytics, AI and innovation strategy, with expertise spanning from tech giants to startups. He lives in an experimental woodland home near Seattle.
