Solution
Data Quality Management
Solution
Data Quality Management
Industry
Pharma & Life Sciences
Region
US
Technology
Databricks
Context
Pharma MMM often breaks down due to fragmented, inconsistent, and delayed data across the sources. Manual checks, schema mismatches, and limited visibility slow cycles and weaken decision-making. With 97% of pharma executives concerned about data accuracy and completeness, automated and business-aligned DQ processes are becoming essential to power real-time MMM and omnichannel planning.
Problem Statement
A leading global Pharma company struggled to operationalize MMM due to recurring data quality failures such as missing fields, inconsistent tagging, delayed agency files, and siloed spreadsheet checks. Teams lacked clarity on root causes or trends, resulting in delayed MMM refreshes, high manual effort, and low confidence in insights that drive channel allocation and budget decisions.
Impact
- 60–80% reduction in recurring data quality issues
- Major reduction in manual checks and rework
- Faster MMM cycles enabled more agile planning
- Higher trust in MMM outputs powered budget and channel decisions via MMX