The Data Strategy Problem No One Wants to Name
Neil Koo walked into an insurance renewal meeting expecting clarity. He got three different versions of the same number, each defended by a different team. None of them could say with confidence which one was right.
Koo, head of data and reserving actuary at Zurich Insurance Indonesia, had just encountered the core problem facing organizations across the world: data chaos isn't a technical problem. It's a leadership problem.
That insight shaped a March roundtable conversation between five data leaders, hosted by CDOTrends in partnership with IBM. The message was consistent across every speaker. Your AI is only as effective as your data strategy - and most organizations have neither.
The measurement gap that matters
IBM's Institute for Business Value surveyed 1,700 data leaders across 19 industries and 27 countries. The findings exposed three structural failures.
First: 92% of chief data officers say business outcomes should drive their work. Only 29% can measure whether they're actually delivering that value. Nine out of 10 CDOs know what success looks like. Less than three out of 10 can tell you if they're achieving it. This isn't a measurement failure - it's an alignment failure.
Second: 75% of CDOs claim they have platforms for cross-silo data integration. Only 19% of Chief Operating Officers say their organization has actually achieved it. The gap isn't about technology. It's about coordination. For most companies, enterprise-scale integration remains an illusion.
Third: Organizations running centralized AI operating models deliver 36% higher AI ROI than decentralized operations. Not from better algorithms or faster infrastructure. From better coordination.
Three traps built into the structure
Chip Schenck, IBM's principal strategist for Generative AI and Data, named the problems cleanly.
Trap one: Accountability without authority. The CDO owns the outcome. The data lives in business units, on systems the CIO controls, maintained by teams that report elsewhere. When an AI initiative stalls because Finance's data is inconsistent, the CDO gets called into the boardroom - not the vice president of finance. Eighty-one percent of CDOs report that data preparation for AI now happens at the functional or project level, not centrally. The work has decentralized. Accountability has not.
Trap two: Governance can't keep pace with business need. A business unit leader needs results this quarter. They connect to a third-party data source, spin up an AI agent, and call it innovation. Leadership applauds. Nobody notices the ungoverned AI program that just went live, accumulating risk. If it fails, people look to the CDO.
Trap three: Proving value before the revenue shows up. Data infrastructure ROI is measured in lagging indicators - AI performance, decision speed, avoided risk. None of those appear in the quarterly report the board reads. Only 26% of CDOs are confident their organizations can extract business value from unstructured data. Yet every high-priority AI use case they're being asked to lead is built on exactly that.
Data as product, not pipeline
Rakshith Rao, IBM's watsonx.data sales leader for APAC, offered the practitioner's view. Even best-in-class platforms fail if the underlying data is broken. Quality matters more than architecture.
What separates organizations that extract value from those that accumulate complexity? "Class technology doesn't create value on its own," Rao said. "What wins every time is ownership, governance and accountability - because this is where people start seeing data as a product, not a pipeline."
Koo described building what he calls a "gravity well." When you treat data as a product - with clear ownership, service-level agreements, and continuous improvement - teams stop resisting and start pulling toward it. Teams decommission their shadow systems voluntarily. "You don't even need to ask them," he said. "They say, I want to be onboarded to your data product."
The shift is fundamental: from data-as-order-fulfillment to data-as-product. It changes the entire equation.
Agentic AI needs agentic data governance
Romain Barraud, head of AI and data science at bolttech, pushed deeper on what data intelligence actually means. Not lineage and metadata. Not just governance. "Data is becoming a product. You have to think strategy. How do you sell data products to other departments and to your customers? How do you establish contracts with SLAs?"
Abhishek Ravi, IBM's field chief technology officer for data fabric across APAC, was direct: "Your AI maturity assessment is, in a way, driven by your data maturity." Organizations diving into agentic AI frameworks without building data foundations first are building on sand. Bad data produces incorrect inferences. Poor outcomes follow. In production, in front of customers, that's not a pilot failure - it's a crisis.
On the question of becoming agentic-ready, Barraud warned against the single-super-agent fantasy. "The mistake would be to have one super agent whereby we ship all our data. It cannot work. It can only create issues - hallucinations and accountability challenges." The path forward is granular: decompose AI agents, give each only the necessary data, and build governance structures that let them operate cleanly.
Reframe the conversation
Schenck offered the most practical reframe of the day. CDOs spend enormous energy explaining "data readiness" to colleagues who have no frame of reference. Stop.
"Reframe data readiness as decision readiness," he said. "The Chief Revenue Officer has no idea what data readiness means. But if you ask them what decisions they need to make to impact the business, they'll rattle off answers 1, 2, 3."
That reframe is everything. It moves the conversation from infrastructure to outcomes. From technical jargon to shared stakes. From a data problem to a leadership problem - which, as every speaker confirmed, is what it has been all along.
The 36% ROI premium from centralized AI operating models isn't an architecture story. "It's a collaboration story," Schenck said. "It's what happens when the right people are aligned around the same outcomes, with clear enough roles that no one is waiting for permission and no one is building the same thing twice."
Data chaos isn't inevitable. But mistaking it for a technology problem will cost you.
Related learning: AI Data Analysis Courses and AI for Executives & Strategy cover the strategic foundations for aligning data initiatives with business outcomes.
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