A large distribution company has realized significant cost savings as a result of the data integration, migration and remediation efforts.
A multi-billion dollar distribution company had acquired several smaller businesses over the course of a few years. While these smaller companies all followed similar business models and were centered on the same products, each utilized different terminology, nomenclature and categorization for its data points with no unique identifiers. As a result, the migration and integration of the data from these varying sources had created significant discrepancies in product descriptions and large volumes of disparate data. This became an expensive issue for the distributor; it inhibited the business from being able to fully gauge its inventory, thereby limiting their ability to adequately forecast and hampering their operations.
The distributor has realized operational efficiencies along with improved forecasting capabilities which yield significant cost savings for the organization.
UDig worked closely with the client to gain a thorough understanding of its business and to identify commonalities and opportunities where automation could help resolve discrepancies in their data. An initial inventory analysis determined the scope of records needing to be addressed. Over the course of several months and through regular communication with the distributor, the Data team created consistencies within the varying datasets and successfully integrated most of the products. To remediate the remaining data, UDig conducted extensive product research and, at a very granular level, determined how every datapoint should be updated through independent research and SME interfacing.
There are marked improvements in the organization’s overall data quality, primarily evident in the business’s ability to have a comprehensive and reliable view of its product inventory. In turn, the distributor has realized operational efficiencies along with improved forecasting capabilities which yield significant cost savings for the organization. The engagement remediated a large volume of backlogged, disparate data and helped establish a foundation for data integration resulting from any additional acquisitions in the future.