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It’s all Ball Bearings, Son! The Devil is in the Data

by Jodee Hale-Schmid

September 3, 2010

Authored by John Ferraioli, SVP Data Lifecycle Management, Utopia, Inc.

Some say the "Devil" is in the details - in our line of work, we phrase it that the "Devil" is in the data...and the definitions.

Take for example a recent client who’s MRO material master was somewhat suspect. The business objective was to enable inventory optimization and reduce associated carrying and warehousing cost (an industry average is 25% of costs associated to inventory). A secondary objective, was to enable strategic sourcing and spend analysis. The goal was to save millions.

Our approach was to identify the business processes as well as the data processes that lead to this sub-optimal situation. Our first step was to understand, profile, and analyze the data, we call this a Data Health Assessment™ (DHA). We found duplicates, mismatched naming conventions such as “Bearing, Ball” and “Ball, Bearing&rdquo ;, and overall a great deal of inconsistent, standard-less, and inactive/flagged for deletion material masters. This is not to say that they weren’t running their business or supporting plant maintenance, it simply meant that it was not at optimal levels. And in this economy, we’re all being pushed to do more with less. Our appreciation, and later, the client’s was that business processes and objectives were being compromised due to their bad data.

With evidence in hand, we could illustrate the data corruption – a quick example was eight ball bearings that had eight different material masters and eight different descriptions - but were all being used for the same functional requirement! Apparently they were buying them for eight different prices from different suppliers; one supplier was selling the same functional equivalent for two different prices. Business case – clear as day. We recommended and executed on a data transformation and enrichment engagement and once the data was standardized, the client was well on their way to inventory and spend optimization.

It was the little things – the definitions in the data… things like ball bearings that helped save millions and over achieved their business goals. Logically, now that the data’s clean – our guidance is to help them keep it clean with sustainable governance and data creation. But that’s another story for another day.

Do you have a similar experience? Let me know.


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