What Doesn’t Work with Governance

Data governance is crucial for businesses aiming to maximize the value of their data, yet several common issues can significantly hinder its effectiveness

Head of Product
 min. read
March 21, 2024
What Doesn’t Work with Governance
Practical solutions for common problems

Data governance is crucial for businesses aiming to maximize the value of their data, yet several common issues can significantly hinder its effectiveness. Let’s dive straight into these challenges and outline actionable strategies for overcoming them.

Silos and Misalignment

Data governance often operates in isolation, with dedicated teams having minimal interaction with the end-users of data. This lack of collaboration leads to:

  • Irrelevant Data Catalogs: Data is documented without considering the actual needs of business users, making these catalogs difficult to use and hindering data discovery.
  • Unclear Ownership and Accountability: Without designated responsibility for data assets, data quality and maintenance suffer.
  • Restricted Access and “Shadow IT”: Overly strict access controls frustrate users, leading them to create unauthorized data storage outside the official system, posing security risks.

Resource Overload and Lack of Scalability

Many organizations underestimate the resources needed for ongoing data governance, facing:

  • Manual Tagging Bottleneck: The task of tagging and documenting data becomes overwhelming as data volumes grow, slowing down the governance process.
  • Governance Team Burnout: Team members get overwhelmed by administrative tasks, leaving little time for strategic initiatives or user education.

Technology Mismatch and Complexity

Implementing the wrong tools can exacerbate governance challenges:

  • Overly Complex Solutions: Large, feature-rich platforms may be difficult to use and require significant technical expertise, discouraging adoption.
  • Limited Automation: The absence of automated processes for data quality checks and lineage tracking increases manual work, hindering scalability.

Lack of User Adoption and Data Literacy

Effective data governance requires user engagement and understanding:

  • Unusable Data Catalogs: Complex interfaces and technical jargon in data catalogs make them inaccessible for business users.
  • Limited Data Literacy: A lack of skills in interpreting data quality metrics or understanding data lineage complicates users’ ability to trust and use data.

Absence of a Data-Driven Culture

Creating a data-driven culture involves more than just implementing technology and processes:

  • Focus on Compliance Over Value: Governance focused solely on compliance fails to demonstrate its broader business value, leading to disengagement.
  • Resistance to Change: Existing workflows and mindsets may resist new governance practices.

Solutions for Effective Data Governance

To overcome these challenges, organizations can adopt several strategies:

  • Foster Collaboration: Encourage communication between governance teams, business users, and data engineers to ensure governance efforts are aligned with user needs.
  • Develop User-Centric Data Catalogs: Create data catalogs and governance practices that directly address the challenges and requirements of business users.
  • Leverage Automation: Use automation for data quality checks, access control, and lineage tracking to reduce manual labor and enhance scalability.
  • Invest in Data Literacy: Run data literacy programs to equip users with the skills needed to effectively use and interpret data.
  • Showcase Value: Demonstrate how governance initiatives contribute to better decision-making, cost savings, and innovation to secure buy-in from users and stakeholders.


To turn data governance challenges into opportunities, organizations must focus on strategies that enhance collaboration, automation, and data literacy. By aligning governance efforts with the real needs of business users and leveraging technology to streamline processes, companies can overcome common hurdles. Investing in user

About Metaphor

The Metaphor Metadata Platform represents the next evolution of the Data Catalog - it combines best in class Technical Metadata (learnt from building DataHub at LinkedIn) with Behavioral and Social Metadata. It supercharges an organization’s ability to democratize data with state of the art capabilities for Data Governance, Data Literacy and Data Enablement, and provides an extremely intuitive user interface that turns even the most non-technical user into a fan of the catalog. See Metaphor in action today!