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« Data Quality Cockpit using SAP Information Steward and Data Services - Part 1 | Main | Data Quality Cockpit using SAP Information Steward and Data Services - Part 3 »

Data Quality Cockpit using SAP Information Steward and Data Services - Part 2


Setting up Data Quality Cockpit - Fixing data for better ROI

Previous blog: We discussed how we identified issues with data and where to focus our attention to fix

The data flow was setup within SAP BO Data Services with workflow steps containing the rules imported from SAP Info Steward. Additional workflow steps were included to enrich the data using transforms like address and company name transforms. The workflow was branched to apply different cleansing and transformation rules to data that belonged to different regions. The last step of the data flow was a matching step that allowed for grouping and scoring of the matched records. The higher the score and nearer to the upper threshold, the more was the chance of the record being a duplicate. The cleaned and matched record file was provided to business to verify and identify the actual duplicates based on business actuals. This completed the cleansing, transformation & matching of the data.

This data when loaded back to SAP IS and the same rules that were applied on the initial unclean data were applied on the cleansed and enriched data. The score card showed a great degree of improvement and gave a sense of how much reliable data had become after performing some simple cleansing and enrichment steps. This process of doing a health check on the quality of data is a continuous process and needs to be done periodically. By binding the rules & transforms into a web service and using it at the point of creation of data also ensures that data entering the system is clean and validated.

To understand the ROI by investing on tool and additional processes a simple impact analysis feature of the IS tool could be leveraged. By identifying the key attributes that define data, determining the cost of each bad attribute and its effect on the record, analyzing the impact of bad data on business and extrapolating it to the universe gives a sense of magnitude the bad data can have on the overall business.  This when translated into potential savings and presented in a form understandable by management provides answers to questions around ROI.

Continuation Part 3: We will discuss in next blog what are the challenges and key advantages

This blog is posted on behalf of Arvind H. Shenoy, Lead Consultant, Retail, CPG & Logistics, Infosys.

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