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The Role Of Data Quality Monitoring In Data Governance
Aligning Data Quality Metrics With Business Insight

by Jim Harris, http://www.ocdqblog.comThursday, March 31, 2011

Three-Level Hierarchy of Data Governance Metrics

There is a three-level hierarchy to the compliance metrics associated with a data governance policy:

  1. Policy – A data governance policy is a set of business rules. At this level, the metric is asking the question: Were all of the business rules associated with the policy satisfied?
  2. Business Rules – Each business rule is comprised of a set of data rules. At this level, the metric is asking the question: Were all of the data rules associated with the business rule satisfied?
  3. Data Rules – Each data rule executes either a data quality check, or some other data evaluation. At this level, the metric is asking the question: Was the data evaluation successful?

Two Examples of Data Governance Policies for Data Quality

Let’s imagine we work for the Soylent Corporation, which recently created a new MDM central repository. When the customer domain was loaded, data quality metrics for completeness and accuracy revealed:

  • Customer Postal Address is 90% complete and 75% accurate
  • Customer E-mail Address is 50% complete and 25% accurate

As we have previously discussed, data-myopic quality metrics appear meaningless to business users because of the absence of any connection with their business objectives and therefore do not measure the impact that data quality has on business performance.

So let’s look at two examples of defining data governance policies for data quality, which will provide a relative business context for the data quality of these two customer master data attributes defined from the perspective of two data consumers using the MDM central repository as their data provider.

These two data consumers have different perspectives on data quality, each defining the fitness for the purpose of their own use. However, both data consumers have the shared business objectives to reduce the operational costs of printing and postage associated with paper delivery to customers in alignment with the Soylent Corporation’s Green Initiative to reduce its environmental impacts.

The first data governance policy is for a Finance initiative called Green Billing:

Data Governance Policy Business Rules Data Rules
Green Billing
Finance requires 25% of customers have their billing method set as electronic delivery (via e-mail address) to reduce the operational costs of printing and postage from paper delivery (via postal address) and to align with the corporate initiative to reduce environmental impacts.
Verify Customer Billing Methods
Verify customers who have their billing method set to paper delivery have an accurate postal address, and those who have their bulling method set to electronic deliver have an accurate e-mail address.
Verify Accurate POstal Addresses for Billing
Data Quality completeness and accuracy checks are executed to verify Customer Postal Address is accurate on MDM repository records where the Billing Method ='P'
Verifu Accurate E-mail Address for Billing
Data Quality completeness and accuracy checks are executed to verify Customer E-mail Address is accurate on MDM repository records where the Billing Method ='E'
Track Green Billing Adoption
Track the percentage of customers currently using the electronic billing method, as well as customers not currently using electronic billing and determine if an e-mail address is available and accurate
E-mail Address Accuracy by Billing Method
Data Quality completeness and accuracy checks are executed on Customer E-mail Address on MDM repository records and results are aggregated by Billing Method

The customer billing business process is a data consumer establishing a relative business context for data quality completeness and accuracy metrics, revealing the following business impacts of data quality:

  • Verify Customer Billing Methods
    • Customer Postal Address is 100% complete and 100% accurate when the billing method is paper delivery, so no billing disruptions are occurring because of undeliverable mail.
    • Customer E-mail Address is 100% complete and 100% accurate when the billing method is electronic delivery, so no billing disruptions are occurring because of an invalid e-mail.
  • Track Green Billing Adoption – 15% of customers are using electronic billing, which is below the 25% target. However, 10% of customers using paper billing have an accurate e-mail address.

In part, this data governance policy is monitoring the customer billing business process for disruptions caused by poor data quality (currently none occur). However, the business objective of green billing is not being satisfied. One possible remediation plan would be to request that customers who are currently using paper billing switch to electronic billing in the next billing cycle (and we can track this progress).

The second data governance policy is for a Marketing initiative called Green Marketing:

Data Governance Policy Business Rules Data Rules
Green Marketing
Marketing requires the 50% of customer cross-sell marketing collateral use electronic delivery (via e-mail address) to reduce the operational costs of printing and postage from paper delivery (via postal address) and to align with the corporate initiative to reduce environmental impacts.
Verify Customer Marketing Methods
Verify confirmed opt-in customers who have their marketing method set to paper delivery have an accurate postal address, and those who have their marketing method set to electronic delivery have an accurate e-mail address
Verify Accurate Postal Address for Marketing
Data Quality completeness and accuracy checks are executed to verify Customer Postal Address is accurate on MDM repository records where the Marketing Opt-In = 'Y' and Marketing Method = 'P'
Verify Accurate E-Mail Address for Marketing
Data Quality completeness and accuracy checks are executed to verify Customer E-Mail Address is accurate on MDM repository records where the Marketing Opt-In = 'Y' and Marketing Method = 'E'
Track Green Marketing Adoption
Track the percentage of customers currently receiving electronic marketing, as well as customers currently receiving postal marketing to determine if an e-mail address is available and accurate.
Email Adress Accuracy by Marketing Method
Data Quality completeness and accuracy checks are executed on Customer E-mail Address on MDM repository records where the Marketing Opt-In = 'Y' and results are aggregated by Marketing Method

The customer marketing business process is a data consumer establishing a relative business context for data quality completeness and accuracy metrics, revealing the following business impacts of data quality:

  • Verify Customer Marketing Methods
    • Customer Postal Address is 100% complete but 80% accurate when the marketing method is paper delivery, so some marketing collateral is wasted on undeliverable mail.
    • Customer E-mail Address is 100% complete but 66% accurate when the marketing method is electronic delivery, so some marketing collateral is rejected as invalid e-mail.
  • Track Green Marketing Adoption – 25% of opt-in customers use electronic delivery, below the 50% target, only 10% of opt-in customers using paper delivery have an accurate e-mail address.

In part, this data governance policy is monitoring the customer marketing business process for the wasted operational costs of undeliverable marketing collateral, which is more significant with the inaccurate postal addresses (an impact not seen in customer billing and we can now quantify its financial impact).

The business objective of green marketing is also not being satisfied. Due to Marketing’s significant use of customer e-mail address, remediation may need to consider enrichment from an external reference. A strong business case can be made since Finance would also benefit from this data quality improvement, and both data governance policies can measure and monitor the ROI of the data remediation efforts.

These two examples illustrate how the compliance metrics associated with data governance policies always frame data quality discussions within a relative business context, allowing the organization to qualify and quantify the business value of having high quality data as a strategic corporate asset.

Aligning Data Quality Metrics with Business Insight

A data governance policy is implemented as an executable process, comprised of business rules and data rules that allow the organization to create business-relevant data quality metrics, which can be monitored, measured, and reported to track compliance with the policy over time in order to drive continuous data quality improvement and link data quality to the achievement of business objectives.

By providing a framework of business context for data quality metrics, data governance policies can help make the business case for data quality improvement efforts and prioritize critical business needs.

The compliance metrics associated with data governance policies align data quality with business insight, providing the historically missing link between data quality and business performance.

Summary

Data quality can be defined as either real-world alignment or fitness for the purpose of use. Real-world alignment reflects the perspective of the data provider, and its advocates argue that providing a trusted source of data should be able to satisfy all business requirements. The danger inherent with real-world alignment is data myopia—the hyper-focus on data independent of business objectives. Fitness for the purpose of use reflects the perspective of the data consumer, and establishes a relative business context for data quality. The challenge inherent with fitness for the purpose of use is business relativity—most data has multiple data consumers, each with their own relative business context for data quality.

Historical approaches have relied on reactive data quality projects for correcting critical data problems, but without resolving their root cause, which often can be traced to the lack of a shared understanding of the roles and responsibilities involved in how the organization is using its data to support its business activities. Data governance provides the framework for a proactive data quality program, ensuring that data is of sufficient quality to meet the current and evolving business needs of the organization.

The central concept of data governance is its definition, implementation, and enforcement of policies, which govern the interactions among business processes, data, technology and, most important, people. It is the organization’s people, empowered by high quality data and enabled by technology, who optimize business processes for superior corporate performance.

Data governance enables the organization to manage its data as a corporate asset, for which the entire enterprise has collective ownership and a shared responsibility, but also requires individual accountability for specific roles associated with the data, business process, and technology aspects of data quality.

The role of data quality monitoring in data governance is not to measure the quality of data in isolation, but to measure the quality of data within the relative context of a specific business use, or in other words, to measure the ability of a data provider to service the needs of a specific data consumer.

A data governance policy is implemented as an executable process, comprised of business rules and data rules that allow the organization to create and track business relevant data quality metrics. These compliance metrics associated with data governance policies align data quality with business insight, providing the historically missing link between data quality and business performance.

About the author

Jim Harris is a recognized industry thought leader on data quality with over 15 years of professional services and application development experience in data quality, data integration, data warehousing, business intelligence, master data management, and data governance.

Jim Harris is an independent consultant, speaker, and freelance writer, as well as the Blogger-in-Chief at Obsessive-Compulsive Data Quality, which is an independent blog offering a vendor-neutral perspective on data quality and its related disciplines.

More information about Jim Harris can be found at: http://www.ocdqblog.com/

About Kalido

Kalido is the leading provider of business‐driven data governance software. Kalido enables companies to manage data as a shared enterprise asset by supporting the business process of data management. Kalido software has been deployed at more than 300 locations in over 100 countries, including 20 percent of the world’s most profitable companies as determined by Fortune Magazine. More information about Kalido can be found at: http://www.kalido.com

For more information please visit Kalido’s Data Governance Resource Center, or www.kalidoconnections.com

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