You cannot "clean" your way to data maturity. You have to architect your way there.
In the race to be "AI-first," organizations are discovering that their data is an anchor, not an engine. During my tenure at Gartner, the most frequent request from CDOs was how to "fix" data quality. My answer was always the same: you aren't suffering from a data quality problem; you are suffering from a governance vacuum.
Treating data governance as a project with a start and end date is the most expensive mistake an executive can make. Data is entangled—it flows across systems, departments, and geographies. When you "fix" it in a silo, it stays fixed for exactly as long as it takes for the next manual entry error to occur.
The Three Pillars of an Operating Model
From Policing to Enablement
Governance is often viewed as the department of "No." In a modern operating model, governance is about "How." It provides the standards that allow teams to move faster because the underlying data is already trusted and compliant.
Distributed Stewardship
Data ownership shouldn't live in IT. IT doesn't create the data; the business does. A durable model identifies data stewards within the business units—people who actually understand the context of the information they are protecting.
Automated Guardrails
In the era of AI, manual governance is a relic. Your operating model must include automated metadata management and data lineage. If you can't see where a piece of data came from, you shouldn't be feeding it into an LLM.
Scaling the Capability
At Digibard, we help clients move from "fixing data" to "governing flow." This requires a shift in three areas:
- Value-Stream Alignment: Governance should follow the value. Focus on the data that drives your top three business outcomes first.
- Incentive Structures: If business units are measured on speed but not data accuracy, the quality will always degrade.
- Continuous Monitoring: Shifting from quarterly audits to real-time data health dashboards.
We worked with a healthcare provider that had spent $5M on three consecutive "Data Cleanup" projects over five years. The quality never improved. We spent 90 days redesigning their operating model, moving accountability to the clinical leads. Within a year, data accuracy in patient records hit 99%—not because we cleaned it, but because we changed how it was captured.
The Board-Level Reality
If your data governance lead is three levels down in IT, you don't have an operating model. You have a support function.
Modern leadership recognizes that data is a strategic asset. Assets require management models, not projects. Is your organization investing in a one-time fix, or are you building the foundation for the next decade?