The business glossary is an approved, governed compendium of business term names and definitions. The process defines how the organization creates, approves, updates, and promulgates consistent business terms and definitions, fostering shared data usage across the organization. Consistent terms and definitions, with corresponding metadata, are essential to managing corporate data in the context of meaning.
The business glossary provides a stable foundation for understanding and integrating data across the organization. The goal of the business data glossary is to ensure that each term refers to a specific, atomic fact without ambiguity or duplication. This enables a broad range of important data management functions and accomplish- ments, which include the following:
Example Work Products
- Data quality
- Target data architecture and shared data repositories Enterprise data warehouse
- Content management
- Consolidation of data stores
- Custom-to-COTS migrations
- Business process automation
- Risk analysis
The business glossary is the core of the organization’s data infrastructure. Although it is simple in concept, it can be a significant challenge to define, reconcile, harmonize, qualify, approve, and maintain shared business terminology. Consistent business meaning is important, although not always obvious to the lines of business within an organization, because distinctions between business terms employed by a business unit, versus an organization-wide perspective, are not typically well defined or documented. In addition, terms used by multiple business units often have connotative meanings that must be explored, rationalized, and decided upon through agreements.
An approved standard business glossary underlies e ective transition to the target data architecture. Without it, re-architecting, consolidation, and e ective sharing of corporate data assets will be slower, more complex, and more costly. Development of new data stores, consolidations, and redesigns are often driven by events, which frequently results in ad hoc naming and definitions of business terms that are then instantiated as logical attributes and physical data elements. Each time this scenario occurs, another obstacle to creating and building a corporate body of approved standard terms is created, and it inevitably results in future rework.
Within the corporate data layer, consistent approved business terms are also a cornerstone for improving the organization’s data interfaces, which are usually plagued with di erent names and representations for the same atomic fact, resulting in a high steady-state cost burden. Similarly, consistent business terms and definitions are critical to enable data integration, aggregation, accurate analytics, trend and predictive analysis, semantic modeling, taxonomies, and ontologies.
The broad scope of this activity—to achieve a comprehensive compendium of business terms that is understood and applied across all applications, systems, and processes— requires a policy mandate from executive management, emphasizing the importance of establishing and controlling data consistency. Projects may try to circumvent adoption of approved terms, to accomplish short-term objectives. Strong governance (supported by compliance enforcement) is essential to intercept implementation e orts early to ensure that the organizational policy is followed.
It is critical to develop a defined process by which business terms and their corre- sponding definitions are created, updated, maintained, and promulgated. Management of the organization’s business glossary requires adopting the view that synthesis of meaning is essential for long-term data management success. Because reconciliation of data names, definitions, allowed values, and other associated metadata cuts across existing systems, it is advisable to align the sequence with the priorities established in the data management strategy.
How best to sequence and prioritize portions of the glossary depends upon the nature of the initiative. Data warehousing projects (e.g., designing and building an opera- tional data store) tend to sequence and prioritize sets of data defined by subject area (e.g., transactions, products, customers, etc.). The data needed to perform business processes, for applications and business intelligence purposes, frequently extends horizontally across subject areas. Ideally, once the organization has mandated the goal of building out a full and comprehensive business glossary, progress is achieved through multiple avenues: top-down, fed by multiple glossaries maintained by the lines of business; by leveraging major initiatives; and bottom-up, by harvesting data models or database scripts.
Establishing standards for business terms, including naming conventions, abbrevi- ations, standard definition representations, and standard metadata, provides the foundation for representing consistent meaning across the organization. These standards should be applied through data governance, and corresponding approval and change processes should be developed, followed, and maintained. A proper communication or feedback loop should be instantiated to ensure that changes and recommendations within the business process are properly communicated to ensure accuracy within the glossary.