Turning Data Into Business Value
Dia Adams shares how modern CDOs can connect data initiatives to business outcomes, demonstrate measurable ROI, and evolve into strategic leaders who drive growth, innovation, and competitive advantage.
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Dia Adams is a Chief Data & AI Officer, former White House Enterprise Data Strategist, and Board Chair of The AI Table, where she helps shape the future of responsible AI and enterprise transformation. With more than two decades of experience in data strategy, AI governance, and digital transformation, she has led high-impact initiatives across federal agencies and global enterprises.
An accomplished author of Winning With AI: A Blueprint for Corporate Leaders, Dia equips executives, including CDOs, CIOs, and CEOs, with practical frameworks to successfully adopt AI while aligning technology with business strategy. Her work spans AI policy, MLOps, data governance, and data literacy, with a strong emphasis on building human-centred, compliant, and scalable AI systems.
Dia’s public sector contributions include advancing data-driven decision-making at the White House and developing AI-powered applications at the U.S. Securities and Exchange Commission. As an ACT-IAC Fellow, keynote speaker, and educator, she continues to influence the next generation of data and AI leaders while helping organisations operationalise AI with clarity, control, and purpose. We’re thrilled to feature her insights on Modern Data 101.
Dia Adams discusses how data leaders can communicate value in business terms, align data investments with executive priorities, and build the skills needed to lead AI-driven, outcome-focused organizations.
How should CDOs today quantify and communicate the business value of data initiatives to the CEO and board?
My advice is to stop speaking in technical metrics, and start speaking in business outcomes. The most effective CDOs translate data into dollars and strategy. Every data initiative should be tied to CEO/board priorities in revenue growth, cost reduction, or risk mitigation. These are the things executives and boards care about most. For example, ”Our customer data platform isn’t just a ‘data lake’ it’s enabling $X in incremental revenue through hyper-personalization, and reducing churn by Y%."
For every proposed data project, you should ask: How does this move the needle on profitability, market share, or customer retention? If you can’t answer, don’t present it.
Always speak in financial/business language. Frame value in terms of ROI, payback period, or NPV. For instance, "This data governance program has a 6-month payback and will save $2M annually in compliance costs.” It’s also good to focus on competitive advantage. Be. Sure to highlight how data creates moats. For example, "Our supply chain AI gives us a 48-hour speed advantage over competitors.
A year from now, if the CFO asks for the ROI of your data strategy, which P&L line item should show the biggest improvement?
Operating Income (EBIT) because it captures both sides of the equation.
It encompasses data strategies that increase revenue (e.g., AI-driven upsells, dynamic pricing) and reduce costs (e.g., automation, fraud detection, supply chain optimization) flow directly to operating income.
It’s also the board’s favorite metric, as EBIT is a clear indicator of operational efficiency and profitability, exactly what data should be improving
What skills and mindsets will the next generation of successful CDOs need over the next 3–5 years?
I think the CDO of 2030 will need to be less of a "data plumber" and more of a "business architect." In terms of business acumen they must be able to do the following:
- Understand P&L levers as well as any CFO, knowing how data impacts revenue, margins, and customer lifetime value.
- Have the ability to translate complex data into compelling narratives that drive action.
- Have a command of product management. Treating data as a product, not a byproduct, .with roadmaps, user stories, and measurable outcomes.
- Possess AI fluency, and not just understanding AI, but knowing where it adds value (and where it doesn’t). Ability to separate hype from real ROI.
- Using data to enhance customer experience, not just internal efficiency.
- Have the ability to drive cultural adoption of data-driven decision-making across the org, and partner with CFOs, CMOs, and CIOs.
- Last but not least, they’ll need risk and compliance expertise (i,e. Navigating GDPR, CCPA, AI regulations, and ethical AI, without stifling innovation).
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