Building a Formal Enterprise-Wide Data Governance: From People to Capabilities Enablement
Toni Hutomo Putro, CDMP, Head of Data Governance, Adira Finance
Toni Hutomo Putro, CDMP, Head of Data Governance, Adira Finance
Having most of my career in IT, I have been conditioned to think primarily in systems, controls and logic. This mindset is invaluable when designing architectures and frameworks, but it can become a blind spot when implementing data governance. In practice, the hardest challenges are rarely technical. They are cultural. Governance initiatives itself requires shifts in behavior, mindset and ownership across organization, changes that cannot be imposed by policies or tools alone. Without clear executive sponsorship and visible commitment from leadership, data governance remains an IT exercise rather than an enterprise transformation, often perfectly designed on paper but weakly adopted, even performed as business-as-usual activity.
Early in my career, data challenges were often treated as technical issues, such as missing fields, inconsistent formats, failed integration or a corrupted excel files. At that stage, the assumption was as simple as fix the system, fill the blanks, reformat the inconsistency. As system scaled and more people involved in producing, managing and consuming data, data problems were rarely technical alone. They were consisting of people behavior and organizational culture.
One defining experience for me was working on initiatives where different units claimed that data ownership belonged to IT, rather than to the teams who truly accountable for the data and its quality. At the same time, each unit interpreted the same data in fundamentally different ways. At that time, the issue did not surface in regulatory report or BAU report that almost everyone reviewed monthly. Instead, it emerged in the data used by analytics team to generate market insights. These misalignments led to recurring issue, unresolved accountability and conflicting decisions. Not because the data was wrong, but because governance and ownership unclear. This exposed a fundamental truth: without clear ownership and shared definitions, data loses its meaning.
Another reason many data governance initiatives failed in my experience was the adoption of models that were too rigid and overly bureaucratic. While well intentioned, these approaches slowed delivery, increase friction and alienated very users we were meant to support. It taught me that effective data governance must be pragmatic, adaptive and clearly aligned with real business objectives, not implemented as a compliance checkbox.
Organizations face several challenges implementing data governance at scale. Many teams already overwhelmed by ongoing transformation initiatives and governance sometimes perceived as just another obligation competing for attention. There’s also uneven maturity and capability across organization. Some departments already structured, while others still addressing foundational data issues. These challenges require restraint and prioritization. In my experience, the most effective strategy is focus first on people enablement. Business initiatives, not just governance, gains momentum when it delivers visible impact and tangible benefits early, starting with a focused first cut that demonstrates value rather than attempting to solve everything at once. Demonstrating tangible outcomes helps shift governance from an abstract concept to a business enabler. Do not forget that leaders play a key role here in reinforcing the message that data governance is essential to operational integrity and long-term scalability.
In my experience, governance succeeds when it is framed as a facilitator rather than restrictive control mechanism. Practices such as data classification, role-based access and clear usage guidelines help organization enable data access with confident, while minimizing unnecessary risk. Cross functional collaboration is essential. Governance teams must understand that operational needs to ensure that controls do not unnecessarily slow down the business. Legal, risk, compliance and custodial functions should be actively involved in designing and implementing the governance mechanism. The key is transparency. Making access rules clear, justifiable and consistently applied and reviewed.
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The key is transparency. Making access rules clear, justifiable and consistently applied and reviewed.
Over the past few years, I have seen governance expand beyond traditional reporting concerns to include model inputs, data lineage and auditability. Metadata and documentation are no longer administrative tasks but evolved into prerequisites for responsible analytics. Governance approaches must adapt. Automation, embedded rules and continuous monitoring is necessary to keep pace with data growth and complexity of data usage such as AI and advanced analytics.
Start with people. Data Governance must be embedded into organizational culture because it is never a blank canvas. It is carved in stone. All that behavior, habits and mindsets. Making them difficult to reshape. When change is required, do it gently and thoughtfully. By enabling people rather than forcing compliance. The right tools can support this journey, but tools alone are never the solution. Data Governance is not just a frameworks, policies or technology implementations. It is a cultural transformation that as we know, it will require consistent engagement, shard accountability and willingness to walk side by side with the organization. When complete as purpose, Data Governance protects your data while enabling value creation. It is a foundation for trust, better decision making and sustainable growth.
