Mastering Data Management

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Measuring your MDM and data governance success requires defining the metrics that matter

Data governance is broadly recognized as one of the most critical areas that makes a difference between the success and failure of an MDM program implementation. But how do you begin a data governance program? Who should be on the data governance board or committee? What exactly should this committee do? Many organizations struggle with these questions.

To function properly, any organization or line of business (LOB) must have performance metrics. For instance a sales organization is normally measured by the sales revenue, marketing by the number of campaigns or leads, manufacturing by the number of product units, a news network by the size of the audience, etc.

Unfortunately it is much less obvious what metrics should be used to measure the performance and efficiency of a master data governance organization

In this post and next week’s post, we’ll define "default" metrics that can be used by any data governance organization beginning a project or program focusing on the quality of master data

Requirements for Master Data Quality Metrics

Strategic data quality metrics must meet the following basic requirements:

• Reflect the current data quality state of large data sets and systems as a whole, e.g. the quality of data in source systems, data hub etc.
• A limited number of metrics should be able to quantify the majority of data quality issues
• Should be measured periodically (e.g. monthly) or on request
• The enterprise should be able to drill down to deeper investigative metrics to understand positive and negative trends
• In addition the metrics and processes should be able to support "what if" analyses to evaluate the business impact of existing data quality issues and the benefits of the data quality improvement actions under consideration

Categories of Data Quality Metrics

The most important categories of the data quality metrics enabling the transformation to a client-centric enterprise typically include:

• Uniqueness
• Completeness
• Latency
• Source system consistency with the data hub
• Standardization and validation
• Availability
• User adoption
• Referential integrity, code semantics reconciliation, and support for relationships

In my next post, we will begin to define these metrics in more detail as they apply to an MDM program.

Are there any other categories you would include?