Element / Retail - Intelligent Automation for Nielsen Data Mapping
Intelligent Automation for Nielsen Data Mapping
Problem :
Manual mapping of Nielsen product data with sales data across regions was highly labor-intensive and error-prone. Support teams spent significant time correcting mismatches, and stakeholders had to raise incidents for data correction. This inefficiency impacted KPI reliability and delayed insights, leading to frustration among business users.
Solution :
An end-to-end automated solution was developed using NLP, Databricks, PySpark, and Azure Data Factory. An NLP script reads product descriptions from the Azure Data Lake and intelligently maps them to corresponding regional sales data. A scheduled ADF pipeline automates the process every Mars period, eliminating the need for manual intervention.
Outcome :
Manual workload was reduced by 60%, and data mapping became more accurate and up to date. This enhancement improved the reliability of KPIs, reduced incident tickets, and accelerated data readiness for business decision-making.