Data Governance

Key questions addressed

Data Governance addresses critical questions such as who can access and modify data, what standards should be in place for data formats and security, and how to ensure data accuracy and consistency. For example:

• Only data stewards and business owners might be allowed to modify customer data.
• All customer records must be stored in an encrypted format.
• Regular data quality checks should be implemented to identify and correct errors.

Data Governance framework

The Data Integrity Suite provides a comprehensive framework that includes:

• Policies and standards for managing and using data.
• Processes for implementing governance policies.
• Clear responsibilities assigned to teams and individuals.
• Metrics for tracking data issues and measuring program effectiveness.
• Tools and technologies to support governance.

Asset management and types

Every cataloged asset is assigned to a responsible party, such as a business owner or data steward, and its relationship to other assets is thoroughly documented. This provides a comprehensive understanding of the data and its relationships with other business assets, such as reports, applications, or policies. For example, a data steward might manage the customer database and document its relationship with the CRM system.

Asset types in the Data Integrity Suite can be configured to meet organizational needs:

• Business assets, such as customer reports, can be defined and customized, with change history tracked.
• Technical assets, like database servers, can also be defined and customized, with changes tracked.
• Models provide hierarchical grouping for business assets.
• Policies govern business assets and data quality rules.

For example, a business asset type for "Customer Reports" can be created, and changes to these reports can be tracked. Similarly, a technical asset type for "Database Servers" can be defined, and changes to server configurations can be tracked.

Importance of asset management components

Assets require specific components such as fields, relationships, predicates, and reference lists to ensure effective management and integrity of data.

• Fields are essential for capturing specific information about assets, allowing for customization based on asset types. This ensures that relevant data, such as timestamps or updates, is accurately recorded and easily accessible.
• Relationships define the connections between different assets, which is crucial for understanding how they interact with one another.
• Predicates serve to clarify the functional types of relationships between assets. They help in establishing dependencies and impacts, which are vital for understanding how changes in one asset can affect others.
• Reference Lists provide a structured way to categorize and manage data consistently. By creating reference lists with defined fields, organizations can maintain clarity and organization within their data, facilitating better data governance and retrieval.

Together, these components form a comprehensive framework that supports the effective management of assets, ensuring data integrity and enhancing overall operational efficiency.

Workflows

Workflows bridge the gap between the data governance framework and day-to-day activities by allowing users with different responsibilities to collaborate on assets. The Data Integrity Suite includes various workflow features:

• Trigger types define what initiates a workflow, such as scheduled events or rule-based changes.
• Conditions specify conditions that must be met for a workflow to proceed.
• Transitions manage the movement from one workflow state to another.
• Activities define the tasks and actions within a workflow.
• Assignments assign tasks to users and manage their completion.
• Requests handle requests related to data governance.