Data quality issue identification and remedial recommendation


Client, a world leading FMCG manufacturer, was facing data reliability issues in the SAP data pertaining to their global supply chain operations. Hence, their IT function collaborated with Inxite Out to build a data quality solution for them.

Data Quality Issue Identification And Remedial Recommendation


Data Exploration and Business Rule Elicitation

Explored the data in collaboration with business stakeholders and drove insights to drive out and formalize a set of business rules applicable for the data.

Data Quality Issue Identification

Leveraged business rules to identify data quality issues (inconsistencies, incompleteness, inaccuracies) which were validated by the business stakeholders.

Remedial Recommendation

Leveraged past distributions of the data and Bayesian statistical approaches to provide remedial recommendations for the identified data quality issues, alongside confidence scores for those recommendations.


Built a complete data quality management solution suite to help the client connect to their relevant SAP data modules, identify data quality issues into a report for further deep dive, and also get recommendations with confidence scores for human-in-the-loop corrections at the source where relevant.


Solution was effective in finding data quality issues of varying extent in different entities / data modules and manage those issues accordingly. Success led to adoption by the client to manage data quality in their SAP Plant Maintenance (PM), Material Master (MM), and SKU / Customer SKU (CSKU) modules.

Case Studies