The Myth of Perfect Data Governance: Why Good Enough Is Enough

In the quest for data excellence, organizations often chase the illusion of perfect data governance, an unblemished system where data is flawlessly managed, quality is impeccable, and compliance is absolute. However, this pursuit can be both impractical and counterproductive.

The reality is that data governance is an evolving discipline, and in most cases, "good enough" governance is more than sufficient to achieve business objectives.

In this article, I explore why perfection is a myth and how organizations can benefit from a pragmatic approach to data governance. These are insights I have gained from some data governance books that I highly recommend. A list of these books is provided at the end of the article.

The Illusion of Perfect Data Governance

Data governance is a complex ecosystem encompassing data quality, compliance, privacy, security, and accessibility. Organizations often fall into the trap of believing that if they establish an all-encompassing, rigid governance framework, they will eliminate data issues. However, the following challenges make perfection unattainable:

• Data is Always in Motion: Data is constantly being created, modified, and consumed across various systems. The idea that an organization can freeze data at a perfect state and maintain it indefinitely is unrealistic.1
• Human and Technological Limitations: Errors will always exist, whether due to human input, system constraints, or integration challenges. Striving for perfection often leads to excessive controls that slow down decision-making and innovation. 2
• Cost of Perfection: Achieving 100% data accuracy, completeness, and compliance can be exponentially expensive. The cost-benefit ratio often does not justify the investment, especially when minor imperfections do not materially impact business outcomes. 3

The Case for "Good Enough" Data Governance

Instead of chasing perfection, organizations should aim for a governance framework that is practical, scalable, and business-driven. A "good enough" approach to data governance includes:

Prioritizing Business Impact Over Perfection

Focus governance efforts on areas where data quality and compliance issues have tangible business consequences. Not all data needs to be governed to the same degree—customer financial data requires stricter controls than internal meeting notes. 4

Implementing Incremental Improvements

Rather than attempting to overhaul data governance in one sweeping initiative, organizations should take an iterative approach, continuously refining policies, processes, and controls based on real-world needs and feedback. 2

Enabling Flexibility and Adaptability

Data governance should support, not hinder, business agility. A rigid framework often leads to resistance and workarounds. Instead, governance models should be adaptable, allowing teams to work efficiently while maintaining compliance. 1

Leveraging Automation and AI

Manual governance processes are not scalable. Automated data lineage tracking, metadata management, and AI-powered data quality tools can help organizations maintain governance at scale without excessive overhead. 5

Fostering a Data-Driven Culture

Governance is not just a technical or compliance issue—it’s a cultural one. Encouraging data literacy and responsibility across business units ensures that governance is embedded into daily workflows rather than being perceived as an external enforcement mechanism. 6

Perfect data governance is an illusion that can lead to wasted resources, operational bottlenecks, and diminished business agility. Instead of striving for unattainable perfection, organizations should embrace a "good enough" governance approach—one that aligns with business needs, supports flexibility, and evolves over time. By doing so, they can achieve sustainable data governance that delivers real value without unnecessary complexity.

References

1 Laney, D. B. (2018). Infonomics: How to Monetize, Manage, and Measure Information as an Asset for Competitive Advantage. Taylor & Francis.

2 Wixom, B. H., Beath, C. M., & Owens, L. (2023). The Data Asset: How Smart Companies Govern Their Data for Business Success. MIT Press.

3 Wallis, I. (2021). Data Strategy: From Definition to Execution. BCS, The Chartered Institute for IT.

4 Fleckenstein, M., & Fellows, L. (2018). Modern Data Strategy. Springer.

5 Barr, M. (2022). Data Quality Fundamentals: A Practitioner’s Guide to Building Trustworthy Data Pipelines. O’Reilly Media.

6 Hopper, M. A. (2021). Practitioner’s Guide to Operationalizing Data Governance. Wiley & SAS Business Series.

7 Moses, B. (2022). Data Quality Fundamentals. O'Reilly Media.