How a Large Enterprise Overcame Master Data Chaos: A Practical Case Study

What is Master Data?

Master Data describes core business entities such as customers, partners, employees, products, materials, and accounts. It is high‑value data reused across departments and stored in multiple heterogeneous systems.

Typical Master Data Variations

Beyond common customer data, different industries have specific master data (e.g., telecom services, airline routes). Different business units also need different master data (sales, R&D, HR).

Case Study: Enterprise Master Data Project

01. Business Challenges

The group had dozens of IT systems, causing severe data silos and poor data quality. Four major issues were identified:

Low basic data quality : a material named "Xiaomi Pepper" existed under three different names.

Decentralized maintenance : the same department was named differently in OA, CRM, and other systems.

Lack of standard rules : many attributes (customer, supplier, personnel) lacked unified naming conventions.

No clear ownership : multiple departments shared responsibility for a single master‑data item, making accountability unclear.

02. Solution

Executive sponsorship was secured to address the collaborative nature of master data across procurement, R&D, sales, and other functions.

Master data framework was designed with four layers: a data source layer connecting all business systems, a data aggregation/development/service layer, and a top‑level master data service platform offering modeling, import, entry, workflow, and extraction capabilities.

Business evolution proceeded in three phases: (1) architecture of models and processes, (2) platform construction and system integration, and (3) application rollout and continuous optimization.

Scope covered all business units, focusing on material, customer, supplier, organization/personnel, and financial master data, with training and maintenance services included.

03. Value and Outcomes

The project delivered six key results:

Standardized processes reduced risk and unified data request/change procedures.

Unified data distribution mechanism improved efficiency across systems.

Initial data cleansing organized nearly 20,000 master‑data records.

Data versioning and traceability enabled multi‑version comparison and audit trails.

Similarity detection for master‑data requests prevented duplicate entries by scoring similarity.

Consolidated data rules embedded in the system to enforce quality.

Operational staff now enter data once and reuse it everywhere, improving efficiency and reducing errors. Middle management benefits from solidified processes and lower management costs, while decision makers gain more accurate data for analysis.