Industry experts have several ways to define master data. They say:
The common theme in master data
- Master data is critical for operational and analytical business decision-making.
- Master data is scattered throughout the enterprise.
- Master data establishes a standard definition for business-critical data that is shared across the enterprise and collectively represents a “single source of truth.”
How does Semarchy define master data?
Master data is your business-critical data that is stored in disparate systems across your enterprise.
The Four Categories of Master Data
- Parties: everyone who conducts business with the enterprise, including customers, prospects, suppliers, and partners.
- Things: what the enterprise sells, such as products and services.
- Places: actual places and how they are segmented, such as geographies, locations, subsidiaries, sites, and zones.
- Financial and Organizational: reporting and accounting categories, including organization structures, sales territories, chart of accounts, cost centers, business units, profit centers, and price lists.
Other Types of Data
Reference Data: often considered a subset of master data, reference data, or reference master data, is shared by and used across different internal and external systems and used to give meaning to master data.
Transactional Data: Transactional data describes business events and it responsible for generating the largest volume of data in the enterprise. It resides in the CRM, ERP, SCM, or other systems.
Log Data: Log data records events or takes snapshots of process states at moments in time. It is extremely important to system operational efficiencies and preventive maintenance applications. Most big data, such as sensor data, machine data, and change-in-state data, are examples of log data.
Metadata: Metadata is data that describes other data; it is the underlying definition or description of data. Master data, reference data, and log data all have related metadata.
Big Data: Big data has many different definitions, but the most common is from Gartner’s Doug Laney. He characterized “big data” by 3Vs: volume, variety, and velocity. By its very nature, big data cannot be effectively maintained with traditional technology. Quite simply, it is the combination of the previous four types of data: log data, transactional data, reference data, and master data.
For more information, please read this helpful blog post.