MDM Data Quality – Enterprise Data Validation

Fortune 50, Global operations with 48 SAP instances

Consumer Goods

MDM

Data Quality

SAP

Master Data

1The Background

Procter & Gamble manages a complex global data landscape with 48 SAP instances across a 4-tier landscape. Master data and metadata were not being synchronized between all operational reporting systems, with users maintaining and modifying master data in regional instances. Integration processes had data leakages which resulted in updates being unable to propagate to other systems.

2The Challenge

Managing metadata and master data across 48 instances with a 4-tier landscape created significant complexity. Customers and vendors were experiencing delays, and the lack of synchronization between global and regional systems was impacting operational efficiency. P&G needed to retire a legacy third-party tool and implement a modern solution for enterprise-wide data quality.

Technical Environment

48 SAP instances across 4-tier landscape
32 unique SAP instances optimized for master data
Multiple downstream application servers
Global and regional reporting systems

3The Solution

P&G leveraged DataTrust to implement a comprehensive 5-step data quality lifecycle: Define (establish framework), Build (create scenarios), Operate (schedule Quality Control Processes), Monitor (dashboards and alerts), and Evaluate (trend analysis and continuous improvement).

Defined a comprehensive data quality framework across the enterprise

Built validation scenarios for master data synchronization

Scheduled automated Quality Control Processes (QCPs)

Implemented monitoring dashboards for real-time visibility

Established trend analysis for continuous improvement

The RightData Edge

DataTrust provided P&G with unified data governance, limited data leakage, ensured consistency across the enterprise, and provided auditability for data performance. The platform replaced manual reconciliation processes and enabled synchronized master data across all 48 instances.