Introduction to Data Governance Goals Data Governance programs aim to establish standardized data definitions, quality, and ownership across enterprises. Organizations can achieve improved data management and decision-making by aligning stakeholders on governance practices. These universal goals ensure that data-related conflicts are managed effectively, enhancing the organization’s overall data integrity and utility.
Universal Governance Principles
Integrity: Participants are expected to deal truthfully and openly with each other regarding data decisions. This principle ensures that the Data Governance Office (DGO) is a trusted information broker among stakeholders.
Ensuring Transparency in Data Governance
Transparency: Data Governance processes should be clear and understandable to all relevant participants, ensuring that the right people are informed of data-related decisions and controls. While some security measures need discretion, overall process transparency is critical for trust and accountability.
Auditability and Documentation
Auditability: All data-related decisions and processes must be documented to meet compliance and operational audit requirements. This principle advocates balancing necessary documentation and operational efficiency to ensure accountability without excessive documentation burdens.
Establishing Clear Accountability
Accountability: Defining who is responsible for data-related decisions and processes helps eliminate gaps and overlaps. It ensures clarity on who is accountable, preventing situations where either no one or too many people are listed as accountable.
Effective Data Stewardship
Stewardship: This principle underscores the importance of defining clear steward responsibilities at individual and group levels. Even those not formally designated as Data Stewards might have such responsibilities baked into their roles to maintain data quality and standards.
Implementing Checks-and-Balances
Checks-and-Balances: Introducing checks-and-balances between different business and technology teams and various roles involved in data management helps guide management efforts effectively. It’s a crucial part of any robust Data Governance framework.
Promoting Standardization and Managing Change
Standardization: This principle supports data standardization, essential for many high-value IT and business projects. Without Data Governance, standardization efforts are often unsustainable.
Change Management: Data Governance involves proactive and reactive management of changes in reference data values and metadata. Controlled change management, involving thorough impact analysis and deliberate changes to essential data elements, is critical to sustaining long-term data governance efforts.
In summary, by adhering to these principles, organizations can establish a robust Data Governance framework that enhances data quality, accountability, and effective management, ultimately supporting strategic data-driven initiatives.
