Data governance is mission-critical for modern enterprises using analytics and AI to make decisions. Yet we hear from many clients that they are on their second, third, or even fourth attempt at establishing enterprise data governance. Why? Most governance programs focus on formalization of governance controls without embedding governance into the organization’s culture. They add governance councils and roles such as data owners, data custodians, and data stewards but ignore the human-centered roles that transform processes into working, adopted practices for data- and AI-driven decision-making within an organization.
Roles To Embed Governance In Your Culture
To move beyond compliance-driven governance and into a cultural model of data- and AI-driven decision-making, organizations need specialized roles focused on behavior change through communication, literacy, adoption, and engagement. When drafting your data governance policy documents, include roles such as:
Data literacy lead. This role establishes and drives organizational data fluency by equipping employees with knowledge of how to recognize, evaluate, work with, communicate, and apply data in the context of business priorities and outcomes. Governance can’t succeed if employees don’t understand data in the first place. This leader will ensure that governance isn’t just about policies — it’s about enabling informed decision-making at every level. Without this leader, your enterprise will have rules without insights.
Change management lead. Governance and analytics initiatives need to be embraced rather than resisted. The change management lead role focuses on overcoming corporate culture barriers, addressing resistance, and embedding governance as part of an organization’s natural workflow. Without this function, even the best governance frameworks will face pushback and slow adoption.
Enablement champions. An enablement champion accelerates data, AI, and analytics adoption. While governance sets the rules and stewards focus on data quality and access, enablement ensures that teams can actually apply data, AI, and analytics in their daily work by providing training, support, and resources, as well as ensuring that data-driven thinking becomes a part of everyday work. Without enablement champions, you risk a lot of shadow analytics and AI popping up as people struggle to use what’s available to them.
Data translator. Data translators convert raw data into meaningful business context. Governance programs fail because they assume that users can make sense of structured datasets without guidance. The data translator acts as a bridge between technical teams and business units, ensuring that governance efforts translate into data that can be used to form actionable insights. Without data translators, you may fail to connect data governance to tangible business results, risk mitigation, outcomes, and value.
Data storyteller. Data storytellers communicate data- and AI-based insights through compelling narratives. Governance isn’t just about managing data; it’s about unlocking its value. The data storyteller helps business leaders understand the impact of governance by framing data-driven insights in a way that is engaging, persuasive, and aligned with strategic business objectives. Storytellers help others understand why governance matters and who it impacts.
Without these roles, governance remains a theoretical construct rather than an operational reality. Employees see governance as an obstacle, rather than a framework that empowers them to work smarter.
Show The “Why” And “How” For Governance
Beyond missing key roles, most governance programs fail to explain the why and how of data evolution. They document policies and procedures but ignore a fundamental query:
How does data get leveraged to turn into enterprise knowledge and wisdom?
To be effective, governance must illustrate:
How raw data becomes structured information (e.g., through validation, integration, and categorization).
How information turns into knowledge (e.g., by adding business context, analysis, and interpretation).
How knowledge evolves into wisdom (e.g., through experience, strategic decision-making, and action).
When governance programs fail to communicate this flow, they reduce governance to a set of rules rather than a system that empowers better decision-making. Employees disengage because they don’t see governance as relevant to their daily work or the broader business strategy.
Now What? How To Fix These Governance Gaps
By addressing the early-stage gaps, enterprises can transform from static policy documents into dynamic drivers of business intelligence. This can help to make sure that governance is not just adopted but actively leveraged to create lasting business value.
Schedule an inquiry with me to discuss:
Other colleagues I recommend
Relating to this topic, don’t miss out on connecting with:
Jayesh Chaurasia (data governance 101–301)
Raluca Alexandru (data governance to support data collaboration efforts)
Katy Tynan (advanced change management and change leadership)
Cheryl McKinnon (internal communications tools)
Zeid Khater (artificial intelligence 101, third-party data integration, and customer data segmentations)
