Data Quality & Data Governance
When numbers in reports disagree and analysts spend their time hunting for errors, the problem is usually not the reports but the data. We find where and why data loses quality and set up the rules and processes that keep it under control.
What is included
- Data quality audit: completeness, accuracy, consistency, timeliness
- Quality rules and metrics, automated checks and monitoring
- Master data management (MDM): single reference data for customers, products and counterparties
- Data catalog and business glossary
- Roles and policies: data owners, accountability, error resolution process
What you get
- A map of data issues and their business impact
- Automated quality control instead of manual reconciliation
- Shared metric definitions across all departments
- Clear ownership of every dataset
Who it is for
Companies that already make decisions based on reports but do not trust the numbers: data from different systems disagrees, reference data is duplicated and errors are found too late.
Let's discuss your task
Tell us about your situation and we will suggest a format for the first meeting, with no obligation.