Data cleansing

Nearly 200 retail stores all over the country, operating under the franchise model, with separate cash register systems and separate product definitions in those systems, give rise to millions of sales records comprising ambiguous data on a daily basis. How to effectively analyze and report sales for the entire chain in these circumstances? Semantic methods of data analysis and grouping may come in handy.

Our client, an international retail chain, having about 200 stores in Poland, operates under the franchise model. Under this model, each location operates independently under the same brand. Millions of receipt items daily can be stored in one, central data warehouse, however, inconsistent product coding across the systems used by different stores and product names defined in any way make any sales analysis by product group impossible, as such data cannot be grouped. Thus, decision-makers cannot easily find an answer to the question of which products sell well and which do not. Neither do they know how the franchisees meet their obligations and whether they buy products which they are contractually required to order from the central warehouse from elsewhere.

Semantic data analysis methods proved to be a helpful solution. Data dictionaries built for that purpose using Sanmargar Metastudio DRM made it possible to effectively group and aggregate receipt data while at the same time retaining the ability to mine data down to the level of a single cash register receipt line. The transformation of data into product groups itself was accomplished using another proprietary solution, namely Sanmargar DQS. There can be any presentation layer under this solution and SAP Business Objects was used for this project.
Standardized data, divided into groups, subgroups and categories, became meaningful and turned into important management information. This in turn facilitated sales analysis and the introduction of central ordering controls.

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