Seeing Is Believing

One of my regular activities as a practice manager at a data quality tool vendor is making what we call a ”Test Report”.

Such a “Test Report” is a preferable presale activity regardless of if we are against a competitor or the option of doing nothing (or no more) to improve data quality. In the latter case I usually name our competitor “Laissez-Faire”.

The most test reports I do is revolving around the most frequent data quality issue being duplicates in party master data – names and addresses.

Looking at what an advanced data matching tool can do with your customer master data and other business partner registries is often the decisive factor for choosing to implement the tool.

I like to do the test with a full extract of all current party master data.

A “Test Report” has two major outcomes:

  • Quantifying the estimated number of different types of duplicates, which is the basis for calculating expected Return on Investment for implementing such an advanced data matching tool.
  • Qualifying both some typical and some special examples in order to point at the tuning efforts needed both for an initial match and the recommended ongoing prevention.

When participating in follow up meetings I have found that discussions around what a tool can do (and not do) is much more sensible when backed up by concrete numbers and concrete examples with your particular data.

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