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Principles and Practice of Data Naming

Abstract
One-day experiential workshop describes and illustrates the basic principles and standards for the description and naming of the company data assets. Included in the course materials is a Data Description and Naming document that can form the basis for naming standards, procedures and guidelines for your company. Participants execute all aspects of the standard. They create precise and understandable data descriptions and derive standard names and abbreviated names for use in data dictionaries, data warehouses, data marts, and the data structures that support business applications.

Audience
Business and IS personnel who are involved in the process of describing and naming company data, including functional managers, data managers, data administrators, database administrators, and data warehouse administrators. Course size is limited to 20 participants. A minimum of 9 participants is recommended.

Course Outline
I. Data Description and Naming
II. Establishing Standard Descriptions
III. Establishing Standard Business Names
IV. Deriving the "Dictionary" Name
V. Naming Exercise Presentations
VI. Creating Implementation Names
VII. Course Wrap-up

Data Description and Naming
Describing and naming data correctly is probably the most important pillar of the Data Management function in a company. Providing for the descriptions of the essential data of the enterprise: data entities, data elements, objects, records, fields, and the like, are at the heart of the Data Administrators' work.

Why is this work so important? The precise and consistent language of names and definitions throughout the company facilitates high performance. With that shared understanding the company can:

set and achieve common business goals,
maximize customer satisfaction,
reduce time to market for products,
accurately represent the financial status of the company to stockholders, and in general,
minimize the common misunderstandings among business functions that can negatively impact the company's performance.

Data Warehouses highlight the need for structure when naming the data assets of the corporation. Effective use of the data warehouse is dependent on resolving definition synonyms and homonyms, eliminating data ambiguity, and implementing effective keyword searching mechanisms so that the correct data can be found. How disconcerting to ask the same business question two ways only to receive different responses from the warehouse query! Well-defined data is the fuel that drives reliable company information.

Data Management Concepts | Data and Process Modeling | Implementing MetaStage | Meta Data Best Practices for DataStage Developers |