Governance and Stewardship Metadata

Metadata for Data Governance and Stewardship

Master Data

For compliance reasons, you generally have to be able to prove that the metadata accurately describes the master data. The easiest way to furnish this proof is to show that the processes that handle the master data are derived directly from the metadata or vice versa. For example, the mappings and transformations done by the ETL tools might be driven directly from the metadata, or the ETL tools might populate the metadata repository whenever a transformation pipeline is created. Business rules should be taken directly from the metadata, whenever possible. This is especially important in MDM, because the business rules often determine which of several alternate values is used to populate a data element.

Data Governance and Stewardship

• Data governance is the process of defining the rules that data has to follow, and data stewardship makes sure that the data follows those rules.
• Documenting how your company manages its data, and then developing metrics to monitor the effectiveness of those management practices.
• Data governance uses metadata management to enforce management discipline on the collection and control of data.
• Governance and Stewardship are especially important functions in an MDM process.
• It’s not enough to have the numbers; you have to be able to show that the numbers are based on accurate and verifiable data. This means both a governance function to demonstrate that the right controls are in place, and a stewardship function to ensure that the controls are enforced.

Data Governance Rules

• Who can read, create, update, and delete data.
• What validity checks are required for data.
• Which application is the preferred source for data items.
• How long data is retained.
• What privacy policies are enforced.
• How confidential data is protected.
• And what disaster-recovery provisions are required.
• The data-governance function should include leaders of both the IT and business groups of a company. This partnership used to be a hard thing to achieve; but with CEOs going to jail, and data on stolen laptops making front-page news, management is paying a lot more attention.

Stewardship

• The role of a data steward is to ensure that the master data and metadata is clean consistent, and accurate.
• In cases in which data quality could not be determined with automated rules and workflows and manual intervention is needed. The people doing this manual intervention are the data stewards.
• The right person to be a steward for a particular collection of master data is the person who understands the data the best. In many cases, only someone who understands the business can make the tough calls about which value is correct for a particular data element.
• Data Stewardship is all about fixing data quality issues.
• This process is best implemented using a routing an approval system of some kind.

Metadata in Data Governance

• Metadata provides the linkage between the business need or desire (policy) and the information or data value.
• The effective management of metadata is one of the essential activities of a data steward within a governance practice, enabling data management policy and access to information.
• Metadata management refers to the activities associated with ensuring that meta data is created/captured at the point creation and that the broadest possible portfolio of meta-information is collected, stored in a repository for use by multiple applications, and controlled to remove inconsistencies and redundancies.
•  In short, data governance uses meta data management to impose management discipline on the collection and control of data.
• Metadata management is a critical component of any robust data governance practice, and metadata is one of the foundational contributors to creating and maintaining full business value of an organization’s data.

Data Quality Metadata

Some Data Quality Metadata

• Policies and Process
• Mission statement
• Guiding Principles
• Work flows
• Best Practices
• Role descriptions

Summary

Metadata management is key to the following

• Master data management
• Data Governance
• Compliance
• Data Quality