Mastering Data Management

More in Business Process Management

• New ebook: Understanding Big Data
• 37 Definitions of “Employee ID”
• Defining Terms: Agree, or Doom your Project
• Establishing a Valid Business “Need to Know” for Sensitive Data

More in MDM

• Best Practices in Test Data Management: Discovering and Subsetting
• TDWI Chicago Preview: Data Warehousing, Analytics & More
• Who’s Afraid of the Big Bad Data?
• MDM for Chief Privacy Officers: A healthcare provider example

More from Lawrence Dubov

• MDM Vs Data Governance: History and 8 Predictions
• What’s Harder: Starting or Sustaining MDM?
• Conclusion: Quantifying Assets
• 3 Essential Questions for Data Quality: Benchmarks, Penalties & Latency

Share this Post

Recent Comments

• ICMCC News Page » Where East Meets West: ehealth in China and the US on Where East Meets West: ehealth in China and the US
• Kim’s Training Podcast on Training as a Safety Net: Kim May
• TDWI Chicago Preview: Data Warehousing, Analytics & More | Mastering Data Management on New ebook: Understanding Big Data

You must define data quality metrics when starting a data governance program

While a mature data governance organization may eventually have a variety of policies, it is essential to have a single policy endorsed by the data governance executive sponsors to properly initiate a data governance program.

This policy should be brief and specifically define (at the executive level) the enterprise position on data and data quality. It is important to make sure that the policy states clearly whether the data will be viewed and managed as a resource for your systems and applications or as an enterprise asset.

If the enterprise data is just a resource for your applications, and this is your enterprise data philosophy, your organization is doomed to be reactive and address data issues reactively on their arrival.

If your data policy sees data only as a resource, this policy does not position you well to initiate and successfully execute a data governance program. You should feel much better if your policy defines data as a strategic enterprise asset that has economic value.

Indeed, if data is an enterprise asset, accountabilities for this asset must be established along with the target metrics that must be met by the responsible individuals.

Data governance metrics for data quality are especially critical for operational MDM. They are the primary instrument that prompts definition and development of the benchmark proliferation process.

Once the metrics are defined, it becomes clearer who in the organization should be held accountable for data quality and the corresponding metrics and milestones. The accountable individuals will further detail data governance requirements and specify needed interfaces, process requirements, resources and tools enabling the organization to accomplish their data governance objectives defined thru the metrics.

Data governance metrics for data quality are different from traditional data profiling metrics. The data governance metrics should be highly aggregated to measure data quality in each of the operating systems as a whole without getting into lower level attribute and record details.

Once data governance policies are established, the data governance organization should be able to measure the progress as it relates to the objectives the policy targets.

In particular, data governance organizations should be able to define data quality milestones expressed as a percentage of the ultimate data quality that can be achieved (100%) when the data in the operating system is the same as the data in the data hub.

A data governance organization can establish target data quality results by month. For instance, next month data quality in system A is expected to grow from 55% to 60%, reach 85% in month three, and 90% by end of the year.

Individuals responsible for a continuous improvement of data quality should be appointed, accounted and rewarded when the target numbers are met.

Traditional data profiling metrics should be used for root cause analysis and data quality issue investigation.

In the previous blog series we defined new information theory metrics for data quality and discussed how these metrics can be used.

In addition to the information theory metrics, a data governance organization working closely with the business should develop and introduce business-specific metrics.

A mechanism for publishing metrics results should be developed, along with the mechanisms for further reporting on an as-needed basis. Mechanisms should also be developed to send data quality alerts to the responsible individuals when data governance milestones are reached or missed, or when other significant data governance events occur.

Data governance metrics for data quality are one of the most critical elements for turning on continuous data quality improvement processes.

When there is no definitive way to measure whether data quality is improving or getting worse, organizations will make no progress on their data governance initiatives.