Data Governance is about handling the collected business data to ensure not just compliance and security but even extract value to enhance business performance. Data quality matters because of an increase in digital transformation, and data-driven technology trends [AI & machine learning]. Let’s understand the what, why, how, and who of data governance


Data Governance is a practical framework designed to help different data stakeholders within an organization recognize and fulfill their data needs. In other words, it is a structure including principles and practices to ensure compliance and avoid the negative impact of poor data quality.

Enterprise-wide data governance and management help your business attain cooperation that contributes to better overall performance. The professionals at Data Management Education Center offer data governance training courses to successfully implement enterprise-level DG programs in any kind of complex environment.


Data is a core corporate asset, which determines the success of the digital transformation. It is therefore essential to deploy a data governance framework suitable to your business model and objectives. The framework will control data standards and practices necessary and allocate roles & responsibilities to competent personnel of every department including management, finance, sales, procurement, production, and legal.

Data governance and management means, you gain clean, lean, and accurate data. This in turn offers better analytics needed to make effective business-critical decisions. Poor data quality means making bad decisions, which you will realize later.


Data governance will need contribution and commitment from the entire company – upward to downward. Start small to reveal that the DG initiative is a better way of fulfilling strict regulations and attain business goals quickly. Starting small helps to offer an insight into areas that need improvement. Thus, the chances of success grow better and better. To set a DG program –

  • Identify the roles & responsibilities to create a DG framework.
  • Define data domains to start establishing stewardship hierarchy.
  • Establish data workflow and control over processes to improve data integrity and quality.
  • Uncover authoritative data sources to create an enterprise-wide roadmap to adapt these sources.
  • Establish standards and policies.


Data governance involves the entire organization more or less but the common stakeholders involved are-

  • Data owners – Make and enforce decisions.
  • Data Stewards – Responsible for managing data assets and compliance.
  • Data custodians – Maintains and updates data life cycle.
  • Data governance committee – Approves data standards and policies, as well as handles, escalated problems.

These roles are supported by the data governance department. The data governance team is made up of –

  • Data governance master or manager
  • Data governance architect
  • Data analyst
  • Data strategist
  • Compliance specialist

The most crucial aspect of allocating and fulfilling the roles is to have well-documented details of roles, expectations, and how roles will interact.

Common best practices for data governance

  • Start small and build from success.
  • Set clear and measurable goals.
  • Define ownership for DG framework success.
  • Identify roles & responsibilities.
  • Educate the stakeholders.
  • Focus on data governance compliance.
  • Develop consistent data definition.
  • Recognize data domains.
  • Classify crucial data elements.
  • Outline control measurements.
  • Detect data governance maturity advantages associated with cost savings, risks, compliance, and growth.
  • Communicate frequently for better discipline.
  • Leverage metrics ranging from general performance KPIs to limited data quality KPIs.

Data governance is a practice and not a project!

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