Master Data

Master data refers to data units that are non-transactional, top level and relational business entities or elements that are joinable in observable ways. An organization may use master data on more than one platform or across a variety of software programs or technologies.

Master Data - Other Definitions

• Gartner defines Master Data as “Master data is the consistent and uniform set of identifiers and extended attributes that describes the core entities of the enterprise including customers, prospects, citizens, suppliers, sites, hierarchies and chart of accounts.”
• Master data is having “consistent definitions of business entities (e.g., customer or product) and data about them across multiple IT systems and possibly beyond the enterprise to partnering businesses.” Phillip Russom, TDWI

No matter where it comes from, the definition of master data has several common themes:

• 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.”

source: WinShuttle

Deciding what information to share as master data typically involves several considerations:

• Costs: Information such as market data that is purchased from data vendors may have a significant cost. As such, organizations typically want to purchase it and use it efficiently.
• Carnality: Data with a large number of elements. For example, reference data with millions of rows may have more value as master data than reference data with four rows.
• Life Cycle: Data that remains valuable for a significant period of time. For example, customers records may be relevant for years or decades.
• Volatility: Data that doesn't change at all may not require a master data management solution. Such data can be defined as constants in a variety of systems.
• Value: Data that is critical to business strategy and operations.
• Reuse: Data that is used in many places has more value as master data. Typical candidates for master data include customer, product and market data that tend to get used by a large number of systems and analytical tools.

Types of Data
Master data is a single source of common business data used across multiple systems, applications, and/or processes. Yet other data types and distinctions exist:

• Reference data represents the set of permissible values to be used by other (master or transaction) data fields. Reference data classifies and describes data and normally changes slowly, reflecting changes in the modes of operation of the business, rather than changing in the normal course of business.
• Enterprise master data represents the single source of basic business data used across the entire enterprise, regardless of location.
• Market master data represents the single source of basic business data used across a marketplace, regardless of location. This stands in contrast from enterprise master data in that it can be used by multiple enterprises within a value chain, facilitating "integration of multiple data sources and literally (putting) everyone in the market on the same page." An example of market master data is the UPC (Universal Product Code) found on consumer products.

Master Data versus Reference Data
Master data is defined as data about the key business entities of an organization. Examples include customer, product, organizational structure, and chart of accounts. A common question about master data is, What is the difference between master data and reference data? Some people take the position that they are the same thing, but it can be argued that not all reference data is master data. For example, lookup and code tables that are used to encode information, such as state names and order codes, are not strictly master data tables. The dividing line between master data and reference data is not always clear cut. One solution is to break master data into two types: master reference data and master business entity data. Master reference data has well defined and simple data structures, has simple keys and governance rules, is often standardized (US state codes, for example), involves only a few applications, and is reasonably stable. Master business entity data, such as customer, on the other hand, is usually ill-defined, has complex data structures and relationships, requires compound and intelligent keys and complex governance rules, is not usually standardized, involves many business processes, and changes frequently. Does this distinction really matter? When developing data quality management and master data management systems it can do. Cleaning and managing master reference status is a reasonable easy job. The opposite is true for master business entity data.

Master Data Governance
Master Data governance is the overall management of the quality, integrity, availability, usability and security of the master data of the organization. The governance also refers to the consistent management of data completeness, accuracy and relevancy of the data including business process management and risk management surrounding the handling of data in an organization. The governance strategy enables to gain visibility into your data assets and get early warning of potential data breaches

source: DataPals

Master Data Purificationbr /> There are usually numerous reasons for Companies to embarked on a Master Data purification journey, the main goals of such an exercise are to improve business systems, business processes as well as the customer experience.

• Targeting Pain Points
  • Fragmented customer data impacting the customer experience.
  • Inconsistencies in disparate systems as a result of acquisitions that cause governance challenges.
  • Limited self-service capability that impact the business users in becoming efficient.
• Best Practices
  • Clearly defining the business problem.
  • Getting leadership and ongoing commitment from business.
  • Understanding the data issues and knowing how they are affecting the company.
  • Defining data ownership, governance and steering data stewards.
  • Starting small, celebrating early successes and incrementally delivering solution with strong focus on business value.
  • Continuously measuring and monitoring data quality.
  • Following best practices and ensuring that the program addresses the business problem.

See Also

Enterprise Data Warehouse (EDW)
Data Cleansing
Data Management
Predictive Analytics
Customer Data Management (CDM)
Big Data
Business Intelligence
Data Analysis
Data Analytics
Data Cleansing
Data Mining
Data Warehouse
Enterprise Data Warehouse (EDW)
Metadata Management
Master Data Management (MDM)

Further Reading

• MDM 101 - What Is Master Data Profisee
• Master Data in the Cloud Matt How
• Three Common Master Data Problems You Never Knew You Had DesignMind