What's in Your Database?
By Hallie Mummert
Data mining initiatives are all the rage these days, especially modeling and clustering. These analytical processes can provide tremendous insight into which customers are most/least profitable and how to identify others like them; which products sell best; and which channels deliver a strong return on investment.
That is, they can if your database is clean enough, complete enough, and updated enough to give you reliable information. According to a white paper titled "Data Quality: A Problem and an Approach" by Javed Beg and Shadab Hussain of data warehousing firm Wipro Technologies, on average, 15 percent of the data in a U.S. customer database is incorrect, costing companies $600 billion a year in lost efficiency and missed revenue opportunities.
Before you can start slicing and dicing your data, you need to conduct a data audit. According to John Miglautsch, founder and chairman of Miglautsch Marketing, a Waukesha, Wis.-based database marketing
consultancy, there are two levels to a data audit:
1. Data counts, in which you review fields and input.
2. Data pattern analysis, where you perform some basic mining to, as Miglautsch puts it, "sluice" for data anomalies.
First Step: Data Counts
To prepare a customer file for analysis, a database marketing services provider first will run a report to identify data problems such as:
- empty fields,
- data in the wrong fields,
- extraneous information, and
- inconsistent data forms.
In addition, explains Nancy Kasmarski, vice president, client relations management at Donnelley Marketing, a provider of database marketing solutions and products in Woodcliff Lake, N.J., marketers want any of the proprietary data elements they capture reviewed for standardization and completeness.
These counts then are shared with the marketer to determine if what the service provider found in the database is what the client expected.