Master Data Management Tips: Business Process Improvements

by | May 5, 2018 | Master Data Management

The role of a data scientist is described as a data analyst with additional business acumen and statistical analysis skills. Aside from the fact that most data analysts would consider that this is what they have done their entire career, data scientists can mine the master data and MDM processing logs for some hidden gems of wisdom. Here are two of my favorite examples of the last few years.

1.    Gaming the System

When building a customer master for an online retailer we noticed an abnormally high number of duplicates. Based on our experience of customer data over many projects the analysis at this client indicated that something was wrong. Many of the duplicates arose from misspellings in both name and address fields. In the days before MDM, these variations would be enough to create a new customer account whereas the fuzzy matching within the MDM solution immediately revealed the problem.

The client pointed out that reps were paid an additional bonus for opening new accounts. Therefore, they were not surprised that sometimes a rep would “game the system”, however they were shocked to see the metrics.

Our analysis of the MDM matching logs isolated the worst offenders and resulted in far tighter controls being introduced. By creating alerts when duplications were being trapped in the MDM Hub, the client was able to establish an immediate intervention and “training” program to help reps with their data entry challenges.

Not only did the client eliminate unjustified bonus payments, but they also reduced the back-office admin and customer support costs associated with multiple accounts for a single customer.

2.    Data Entry Misery

We were working with a client to migrate their legacy ERP from JDE to SAP. This process involved the construction of a Customer Master file with the appropriate sold-to, ship-to and bill-to address information. Our analysis showed an unusually high number of duplicate ship-to addresses that, on further analysis (by a data scientist), turned out to be localized to a particular plant and oddly, for high-volume customers.

It turned out that in JDE a high-volume customer could have a default shipping address in the system, but the order entry screen still allowed the entry of an override shipping address if necessary. If a new shipping address was entered, it was stored in JDE as a valid shipping address for the customer. The employee that captured the order for the plant in question had been working at this company for more than 10 years. She had diligently re-entered the shipping address on every order because she was unaware that if the order was going to the default location, no override was required.

We have no idea how much time was invested over the years typing redundant data but I’m sure she would have appreciated the heads-up a decade ago.


MDM provides the platform to analyze data flows to uncover anomalies that would otherwise remain hidden within operational applications. The business benefit of MDM extends beyond just the obvious advantages of better data quality to identifying opportunities for business process improvement.


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