by What is Data Governance?
Data governance refers to the overall management of the availability, usability, integrity and security of the data employed in an enterprise. A lack of proper data governance can negatively impact an organization in many ways, such as non-compliance with regulations, poor data quality leading to bad business decisions, and security vulnerabilities. Implementing a robust data governance program helps organizations avoid these issues and achieve their goals.
Defining Roles and Responsibilities
One of the most important aspects of an effective Data Governance effort is clearly defining who is responsible for what. This includes determining which roles are needed, such as a data governance committee, data stewards for different business domains, data owners, process owners and data consumers. It is critical to document things like who is accountable for enforcing which policies, who approves changes to data, who is responsible for data quality monitoring and more. Having well defined roles avoids confusion and ensures appropriate responsibilities are assigned.
Establishing Data Governance Policies
Formal policies lay out the rules and guidelines around data management that everyone in the organization must follow. Common data governance policies cover areas like data security, data quality, metadata management, data retention, data access and user provisioning. The policies clarify behavioral expectations and allow for consistent treatment of data across systems and business units. Regular reviews help refine existing policies or establish new ones to address emerging needs or regulatory requirements.
Implementing Standards and Processes
Adopting common standards and processes facilitates the secure, accurate and appropriate use of data on an enterprise-wide level. This includes establishing standards around data modeling, metadata collection, master data management processes, data integration procedures, data quality measurement techniques and reference/code lists. Documented and enforced standard processes address data handling, risk assessment, privacy impact analysis, data breaches and more. They foster organizational alignment around data handling activities.
Managing Metadata Requirements
Metadata refers to the data that describes other data. It includes attributes that provide information about the meaning, usage and lifecycle of core business and technical data. Developing and maintaining comprehensive metadata requirements ensures that all critical metadata elements are formally defined and collected. Metadata is essential for tasks like cataloguing assets, regulatory compliance, integration projects, data lineage tracking and understanding data. A metadata strategy aligned with governance policies streamlines data management operations.
Performing Data Quality Assessments
Regular data quality assessments measure how well data aligns with defined rules, standards and business/technical requirements. They identify issues like missing values, duplicate records, invalid formats and discrepancies between source and destination systems. The findings from assessments pinpoint data domains requiring remediation. Implementing data quality monitoring processes at source systems and establishing service level agreements for accuracy help maintain clean, consistent data to support analytical needs.
Monitoring Compliance Requirements
Regulatory requirements around areas like privacy, security, records retention need to be continually monitored to stay on top of changes. Data governance practitioners assess which compliance mandates apply to organizational data processing activities. They develop compliance strategies, assign compliance roles and establish compliance review procedures. Thorough compliance monitoring through impact assessments, audits and testing confirms adherence to pertinent regulations and avoids penalties from non-compliance.
Enforcing Access Controls
Data access governance deals with who can access what type of data, under what conditions. It focuses on access certification processes, role-based provisioning models, data classification schemes, and tracking and preventing unauthorized access. Tight access controls aligned with privacy and security policies prevent breaches of sensitive data. Regular access reviews further strengthen protection by promptly revoking unnecessary access permissions when employees change job roles. Comprehensive access logging allows investigations of any potential misuse.
Managing Third Party Data Sharing
When data is shared with third parties, contractual agreements need oversight to ensure external entities also meet compliance and data protection standards imposed on the organization. The data governance team develops and enforces third party data handling policies and performs risk-based due diligence reviews of third party security controls. Strict monitoring of downloads, extracts and external transfers of data limits improper distribution. Regular third party audits verify supplier commitment to service level expectations regarding data security and quality.
Master Data Management
Master data management (MDM) consolidates critical cross-enterprise reference information into master records. This includes entities like customers, products, suppliers and Chart of Accounts. MDM governs creation and maintenance of such shared master data through duplication elimination, synchronization of values across systems and enforcing data quality rules. Reliable master data supports transaction processing, reporting and analytics by sourcing clean, consistent attributes. MDM enables a single point of reference for key business entities.
Implementing Data Lineage Tracking
Data lineage refers to the origin and flow of data across systems over time. Effective data governance mandates collection and documentation of lineage metadata to map these dependencies and transformations. This provides transparency of data movements and supports impact analysis, troubleshooting, audit/compliance activities and reusability of data assets. Lineage tools visually depict data journeys which facilitates understanding information ownership, usages and maintains data provenance across analytical applications and reports.
Leveraging Analytical Capabilities
A foundational component of data governance is availability of rich descriptive, distributional and predictive analytics on the governed data assets. This helps monitor compliance and identify remediation priorities through insight into issues like PII usage, access anomalies, data quality deviations and anomalous behavior patterns. Embedding advanced analytical models and business intelligence dashboards within the data governance framework delivers valuable context for continuous improvement of processes, policies and technologies around data management in the organization.
Note:
1. Source: Coherent Market Insights, Public sources, Desk research
2. We have leveraged AI tools to mine information and compile it
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