Data Governance

In a Data Governance Platform, the goal is to manage the availability, usability, integrity, and security of the data employed in an organization. These platforms play a crucial role in ensuring that data across the organization is accurate, accessible, and protected. When it comes to data governance features, the focus is typically on the following types of information:

1. Metadata: This includes data about the data itself, such as table names, column names, data types, and sizes. Metadata provides a comprehensive understanding of the data's structure and format, which is essential for managing it effectively.

2. Data Quality Metrics: These metrics assess the quality of the data in the tables, including accuracy, completeness, consistency, and timeliness. They might measure things like the percentage of missing values, the rate of duplicate records, or the conformity of data to specific formats or standards.

3. Usage Statistics: Data governance platforms often track how frequently and by whom data is accessed. This includes information on the most queried tables, the type of queries run (e.g., read, write, update, delete), and the performance of these queries (such as execution times).

4. Security and Compliance Metrics: These metrics are crucial for ensuring that data handling adheres to legal and policy requirements. This might include tracking data lineage (the data’s origin and where it moves over time), audit logs (who accessed what data and when), and any data breaches or leaks.

5. Data Lineage and Provenance: Understanding where data comes from, how it is transformed, and where it moves over time is vital for both compliance and data quality. This includes detailed histories of data sources, transformations applied, and downstream uses.

6. Data Sensitivity Classification: Identifying sensitive data (e.g., personal identification information, financial data) and ensuring it is appropriately protected. This involves tagging data according to its sensitivity level and applying corresponding access controls.

7. Data Relationships: Understanding how tables are related to each other through foreign keys and other relational constructs helps in managing data integrity and supporting effective data modeling.

8. Data Access Governance & Catalog Management:This feature within a Data Governance platform centralizes data management by providing a detailed, searchable catalog of all organizational data assets, including metadata and previews. This feature streamlines the data access process through an automated, role-based approval workflow that integrates with existing identity systems, ensuring efficient and compliant data handling. It includes robust monitoring and audit capabilities to track access and usage, while enabling data administrators to manually oversee and control access requests, particularly for sensitive data. This enhances both security and operational efficiency, supporting responsible and compliant data utilization across the organization.

By collecting and analyzing these types of data, Data Governance Platforms enable organizations to make informed decisions about their data management practices, ensuring that data remains a valuable and secure asset.

The ARPIA Knowledge Grid is the meta layer that organizes the data been store in the data repositories and data objects as tables, views and more.

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ARPIA Knowledge Grid

For more information on the ARPIA Knowledge Grid you can access the documentation article Knowledge Grid