Ashish Dibouliya is a data & technology professional in the banking industry.gettyMany data governance programs fail, not because the framework is wrong but because there's an ownership issue—no one knows who owns what. The issue of ownership starts almost immediately in highly regulated financial environments—a regulatory report doesn’t reconcile, or two critical dashboards show different numbers. When this happens, the blame game and confusion sets in: "Who owns this data?" "Who's responsible for fixing it?" "Why wasn't this caught earlier?"Organizations invest heavily in governance frameworks, tools and policies. However, they skip the fundamental step of clearly defining ownership and accountability, often termed RACI.​Governance Isn't A Policy—It's An Operating ModelMost enterprises treat governance as documentation because the focus of governance is policies and guidelines. However, governance exists not just in documentation but also within an operating model. Governance begins to work only when it becomes a part of your daily operations. Proper governance enables effective decision making, defining roles and responsibilities and eventually safeguarding financial organizations from heavy penalties.​The Real Problem: Missing AccountabilityIn many cases, the problem isn't a lack of commitment to any one initiative. Everyone works hard, the tools have been used and the framework defined. However, no one really knows where the buck stops. Data owners are defined, but they lack both power and accountability. Data stewards are charged with ensuring data quality, but they can't control the source systems that produce the data. Developers and operations people use data, but governance isn't considered an integral part of the design.​What A Modern Governance Structure Looks LikeData governance becomes efficient only when there's an intentional structure with defined roles. First, strategic leadership is required, which is responsible for defining direction and ensuring alignment between the business strategies, such as regulatory compliance, risk management and artificial intelligence implementation. Then, there are four very important roles that enable effective data governance.​Data Owners: Data owners take responsibility for managing data integrity and compliance within their respective domains (e.g., loans, deposits, customers and risk). These roles are critical because they provide business accountability for the data. Data owners are responsible for defining business rules, approving data definitions and certifying the accuracy of information used for regulatory submissions. When data has no clear owners, it quickly becomes the corporate version of a neglected kitchen sink.​Data Stewards: Next, we have data stewards, who collaborate with data owners to support governance activities and address data-related issues. This is the most challenging layer to establish because it requires ongoing participation from both business and technology teams. Data stewards monitor data quality issues and help resolve them. They also ensure that governance policies are followed in daily operations. They work as a link between strategic governance objectives and day-to-day data management activities.​Technology And Architects: Technical functions ensure that governance is secure. Architects (e.g., data, AI, applications and governance) play a key role by embedding governance capabilities such as data lineage, quality controls and metadata management directly into enterprise data platforms.​Security And Compliance: Security and compliance teams ensure data privacy, access control and regulatory compliance. This includes implementing controls such as data masking and role-based access to protect sensitive information while enabling the broader use of data for analytics and AI initiatives.​The Biggest Mistake: Starting With ToolsI've often witnessed the use of tools as a starting point for governance. Organizations allocate budget resources for data cataloging, governance platforms and dashboarding, believing that technology will solve all problems. However, technology doesn't build governance.If ownership isn't defined, technology only serves to emphasize this ambiguity. It highlights inconsistencies, but it doesn't address them. That's why most governance initiatives seem highly effective throughout the implementation phase but fail in the production phase. The technology is present, but the operating model is absent.​Why This Matters In The Age Of AIThis becomes more important as organizations adopt AI to make decisions. AI needs good quality data. If there's no proper governance, the AI tool doesn't make the problem disappear; instead, it makes the problem much worse. Any inconsistencies or quality problems with the data become immediately apparent in the output produced by the AI tool. This becomes more problematic because of the associated regulatory risks that come with poor governance. Decision making from AI results requires explainability and auditability, which means good governance is essential.​The Cultural ChallengeThe toughest challenge in governance lies not in technology but in culture. The very concept of ownership alters the organizational paradigm. It brings transparency, accountability and a sense of responsibility, which can make some people uncomfortable. Ownership of data may make data owners uncomfortable because of the increased responsibility involved. A lack of authority could cause data stewards to struggle. Business groups may find governance activities burdensome in the beginning. This is where leadership comes into play for governance enablement, fostering better decisions, reducing effort and fostering trust.​The Bottom LineWhen organizations stop focusing only on policies and start designing ownership, data governance programs can succeed. Governance isn't about documentation—it's about people. It's about clearly defining who owns the data, who's responsible for its quality and how that responsibility is embedded into everyday operations. It's important to understand that accountability can't be automated—it needs to be properly designed. When that happens, governance shifts from just being a compliance exercise to offering real business advantage.Forbes Technology Council is an invitation-only community for world-class CIOs, CTOs and technology executives. Do I qualify?