CASE STUDY — CONSUMER GOODS & beyong
Data Quality Optimization
Empower data quality by bridging business and technology
Sector:
Consumer Goods & Beyond
Focus:
Data Quality Management
BUSINESS CHALLENGE
Inconsistent data quality across business processes
Inconsistent data quality across business processes caused delays in reporting and impacted the accuracy of decision-making. Lacking a standardized approach for translating business data quality rules into technical queries led to frequent errors and required manual corrections.
SOLUTION IMPLEMENTED
A collaborative platform for defining and applying data quality rules
Implemented a data quality platform that allows both business and technical users to collaboratively develop and apply data quality rules. Users can define rules in natural language, which are then translated into SQL queries for execution on data stored in a database.
TECHNOLOGY
A platform designed to turn business rules into executable queries
DATA PLATFORM
Azure Data Stack
Stores and processes large volumes of data, providing a centralized platform for data management across business processes.
SERVING
Power platform
Delivers an intuitive interface, enabling business and technical users to collaborate directly on defining data quality rules.
AI
azure foundry & power apps
Automatically generates SQL queries from natural language descriptions, applying data quality rules without requiring technical expertise.
Results & ROI
What changed once the platform went live
ACCURACY
Enhanced data quality across business processes, leading to reliable insights and informed decision-making, while reducing errors and improving efficiency.
TIME SAVINGS
Automated translation of business rules into SQL queries saved significant time, reducing manual processing and letting teams focus on higher-value work.
COLLABORATION
Enabled seamless collaboration between business and technical users, fostering a more agile approach to data management and aligning standards.
