Fitness for Purpose
Data Quality should only be measured within the context of how it will be used. The concept of “Fitness for Purpose” is central to any data quality metrics within a data governance program.
By Martin Dunn
Many data governance programs struggle right out of the gate as organizations attempt to define data quality standards for enterprise data. The governance team will often discover that individual business stakeholders have different, sometimes conflicting needs for some enterprise data.
There are many reasons why people have a different view of enterprise data. We have included just a few examples to illustrate why this occurs:
- Legal or regulatory policies may require that an enterprise application preserves the information that was gathered when the contract was signed even if this information is now considered “incorrect” or out of date
- System limitations and the prohibitive cost of system modifications may give rise to intentional data errors such as creating duplicate customer accounts to handle multiple billing options
- A web portal relies upon email for registration confirmation but it also gathers name, address and telephone number as part of the registration process. There is no way for the web portal to validate the demographic information so there are no attempts to enforce uniqueness other than for email address.
All three of these examples may impact the number of customer records held in each system and/or the demographic information captured on each customer record. The data governance team may find that each individual system owner considers that their data is of high quality even though the basic demographics for a specific customer could vary dramatically between systems.
While generic measures of data quality are of little value across any of these systems, it is still possible to define meaningful data quality measures with Fitness for Purpose in mind.
For instance, within the web portal the email address is intended to be a unique, current email used by the registered user. Data quality measures that test the uniqueness and ability to deliver email to this address are good measures of this data. Measuring the duplicate customer names or addresses would not be a valid data quality measure for the web portal as this information is not intended to indicate uniqueness of the individual.
When combining information across systems the context of the information cannot be ignored. Comparing customer address information from the Contract system (example I) to the Billing system (example II) will certainly show discrepancies even though the data within each system may be perfectly fine for the business process that it supports.
Data governance teams can avoid much frustration by defining all data quality measures in terms of the intended use of the data, and by recognizing that data created for different purposes can rarely be combined unless the data model recognizes the context of the origination of the data.
Martin Dunn was the co-founder of Delos Technology which developed the MDM technology marketed under the Siperian brand. The Delos MDM technology introduced many MDM concepts that are now widespread within the MDM discipline including a data steward console to adjudicate match results, opt-in synchronization, cell level delta detection and the concept of measuring trust.
Martin is now a partner with Gaine Solutions and continues to advance the techniques by which enterprise Master Data is managed.
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