DATA QUALITY FUNCTIONS IN A MARKETING ENVIRONMENT

There are nine data quality functions marketers call upon to cleanse their data. As shown below and depicted in Figure 4, in order of their occurrence in a data quality project, those functions are:

1. Measure
2. Analyze
3. Identify (Parse)
4. Standardize
5. Correct
6. Enhance
7. Match
8. Consolidate
9. Monitor

These functions will usually be conducted in this order because they support each other. For example, to standardize the elements of a customer record, those elements–such as title, salutation, or phone number–need to be identified or parsed out from the contact data. Many marketing campaigns will receive data that comes straight from a mainframe in a multiline record with no fielding, as in the following example:

Tom Jones
Director of Cybernetics, Formalux
Acetera Corporation, Formalux Divson
1900 Corporate Way N
Cincinatti, OH, 58999

For the record to be useful, it needs its various components identified and standardized– as in changing corporation to corp and correcting divson to division. It then must match and consolidate with the other records pulled from the source systems. Measuring and analysis kick off the process by providing metadata as to the level and types of defects found in the source data, so subsequent cleansing operations can be tailored for the greatest effect.

The first six functions–including enhancement where additional data is appended like demographic or geo codes–improve data to the point where it can be matched and consolidated. Matching and consolidation is where a tremendous amount of value is delivered to marketing in that duplicate records are eliminated, best of records are built, and the manager now has a single view of each prospect or customer within the context of the applied source data. Now able to build a corporate or retail household for target marketing, the marketing manager can identify the top 20% of the customer base or form demographic groups for segmentation in the next campaign.

Last, monitoring uses the business rules and definitions created in the measure and analyze phases to create an automated profiling project that provides managers with defect information (metadata) at any time, so they can make decisions as to whether the data is good enough to use or needs to be improved for the next operation.