Merging Customer Master Data

One of the most frequent assignments I have had within data matching is merging customer databases after two companies have been merged.

This is one of the occasions where it doesn’t help saying the usual data quality mantras like:

  • Prevention and root cause analysis is a better option
  • Change management is a critical factor in ensuring long-term data quality success
  • Tools are not important

It is often essential for the new merged company to have a 360 degree view of business partners as soon as possible in order to maximize synergies from the merger. If the volumes are above just a few thousand entities it is not possible to obtain that using human resources alone. Automated matching is the only realistic option.

The types of entities to be matched may be:

  • Private customers – individuals and households (B2C)
  • Business customers (B2B) on account level, enterprises, legal entities and branches
  • Contacts for these accounts

I have developed a slightly extended version of this typification here.

One of the most common challenges in merging customer databases is that hierarchy management may have been done very different in the past within the merging bodies. When aligning different perceptions I have found that a real world approach often fulfils the different reasoning.

The fuzziness needed for the matching is basically dependent on the common unique keys available in the two databases. These are keys as citizen ID’s (whatever labeled around the world) and public company ID’s (the same applies). Matching both databases with an external source (per entity type) is an option. “Duns Numbering” is probably the most common known type of such an approach. Maintaining a solution for assigning Duns Numbers to customer files from the D&B WorldBase is by the way one of my other assignments as described here.

The automated matching process may be divided into these three steps:

During my many years of practice in doing this I have found that the result from the automated process may vary considerable in quality and speed depending on the tools used.

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3 thoughts on “Merging Customer Master Data

  1. Henrik Liliendahl Sørensen 22nd April 2010 / 10:03

    On twitter Nicole Carriere = @carrni commented: Same applies when merging different views in one company for example sales & risk.

    I agree. The automated matching process has the same challenges and solutions.

  2. Edward Cardenas 27th April 2010 / 15:51

    As long as a third party reference data(e.g. D&B)is used for baseline matching the variability of is reduced, and attribute quality is increase in the mastered record. Without a baseline for data standards, survivorship complexity is increased due to additional effort needed to determine the sourc with the highest data quality.

  3. Henrik Liliendahl Sørensen 27th April 2010 / 16:38

    Edward, thanks for commenting. I agree. I am a strong believer in real world alignment as the less cumbersome way to solve issues with multipurpose challenges.

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