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Building A Business Case For Data Quality: Identify Business Impact

Building A Business Case For Data Quality, 6 of a 7-part series

Once you identify the many data anomalies, you need to work with the business to quantify the business impact. If you can’t determine the impact on the business, it either has no impact or you are talking to the wrong people. If you can’t determine the impact, you might as well stop right there or find another area to look at:

 

No impact = No reason to fix it = No money

If phone numbers are missing from marketing data, you would want to correct that issue. However, you can’t call them and ask what their phone number is. You would need to send the data to a service bureau to get the data. Or you can have someone enter information into a Web site that returns the phone number based on the name and address.

Here are some examples of where data quality is having an impact:

 

  • Missing discount entitlements from failure to appreciate total global spending

The problem here could be with duplicate vendor records for the same vendor but with slightly different names.

For example:

GREEN HOME SERVICES REBECCA 199 HIGH STREET SOUTH CARTERTON NZ

MR GREEN HOME SERVICES             199 HIGH STREET SOUTH CARTERTON NZ


  • Duplicate payments to suppliers

Two different people in Accounts Payable make payments to the same vendor but enter slightly different information about the vendor. Unless you do some fuzzy matching (using industry match algorithms or out-of-the-box matching rules that find names that are not an exact match) on the payments, you could miss these duplications.

For example:

ALLTEL COMMUNICATIONS INC   ONE ALLIED DRIVE  LITTLE ROCK     AR          72202

ALLTEL CORPORATION                                                                 LITTLE ROCK   AR          72203-8821


  • Improper calculations for charge-backs and rebates

One of the tasks automated profiling can perform is to look for relationships in calculated fields. Failure to find the relationship usually means that the calculations are not the same for all the data. Another issue is the relationship between two fields. If you offer a JUL10 rebate on a product of $10, then there should not be a record with a JUL10 code with a $20 rebate amount. Also, if the rebate pertains to sale made in July 2010, the sales in other months should not have the rebate.

 

  • Marketing dollars misdirected at wrong target market/low-opportunity customers

Because you have missing or inaccurate data, you may be targeting the wrong prospects. If your address data is inaccurate, a geographical rating system can give customers the wrong rating, thereby having you target them for a promotion with no chance of success. You might send a seed and fertilizer  mailing to a city-dweller or a promotion for title loans to someone living in Trump Towers in Manhattan. Slim chance either promotion will succeed.

 

For more on the impact poor data quality has across an organization, read this ebook entitled “Selling the Business Value of Data Quality” about the tools and language necessary to have conversations with business stakeholders to drive successful data quality initiatives.

 

 

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