Throughout the history of Information Resource Management, there have been questions surrounding the necessity for multiple disciplines within the IRM domain. Many organizations do not recognize
the essential differences between Data Administration and Database Administration. As a result, there exists much confusion over the roles of Data Administration and Database Administration, and
their respective responsibilities. Each discipline is necessary for the proper management of the corporate resource of information, but these activities should never be combined in one person or
sub-group. Each discipline requires different skills, training and talents, therefore, most people do not make a successful transition from one discipline to the other. Data Administration and its
sub disciplines: Data Modeling, Data Definitions, Planning and Analysis, is a relative newcomer to the field of data processing. It is only within the last 10-15 years that the industry has given
serious consideration to the logical management and control of information as a corporate resource. There is a lack of understanding of the purpose and objectives of Data Administration even among
experienced data processing professionals.
Following is a chart of the major responsibilities of Data Administration and Database Administration:
Data Administration – Logical Design
- Perform business requirements gathering
- Analyze requirements
- Model business based on requirements (conceptual and logical)
- Define and enforce standards and conventions (definition, naming, abbreviation)
- Conduct data definition sessions with users
- Manage and administer meta data repository and Data Administration CASE (modeling) tools
- Assist Database Administration in creating physical tables from logical models
Database Administration – Physical Design / Operational
- Define required parameters for database definition
- Analyze data volume and space requirements
- Perform database tuning and parameter enhancements
- Execute database backups and recoveries
- Monitor database space requirements
- Verify integrity of data in databases
- Coordinate the transformation of logical structures to properly performing physical structures
Perhaps more than any other of the discrete disciplines within IS, Data Administration requires a concrete grasp of the real business the company is in, not just the technical aspects of
interaction with a computer. Frequently, a DBA or systems programmer is arguably portable from one industry to another, with minimal retraining as long as the technology remains constant. A DA, on
the other hand, has much to learn in an unfamiliar industry to be truly effective. Having an impact on data design and information management requires an understanding of the goals, objectives and
tactics of the organization and its core industry (insurance, pharmaceuticals, banking, etc…). Logical Modeling is part of the Data Administration function, and is a full-time responsibility for
those involved in a major development or enhancement project. It is frequently augmented by other data administration functions, such as developing data element definitions and managing the models
and associated items in a meta data repository. One role of data administration is to advocate the planning and coordination of the information resource across related applications and business
areas. By doing so, the amount of data sharing can be maximized, and the amount of design and data redundancy can be minimized.
One way data administrators (also called “data analysts”) can assist in making data sharable and consistent across applications is to use the techniques of logical data modeling. Logical data
design is a specialty that requires its own specialists. Developers and database administrators are not trained in logical data modeling, and should not be expected to perform this specialized
task. The overall objective of Data Administration is to plan, document, manage and control the information resources of an entire organization. The main objective of Data Administration is to
integrate and manage corporate-wide information resources. This integration can be achieved by a combination of refined skills and techniques, proper use of Data Administration tools such as a meta
data repository and CASE (modeling) products, and logically designed data structures.
In the final analysis, the coordination of Data Administration and Database Administration skills, talents, roles and responsibilities will enable an organization to realize the goal of proper
management of its information resource.
Anne Marie Smith has been an IS professional since 1983, within various industries. She has demonstrated leadership and technical skills and has extensive experience in the areas of data
administration, data architecture, methodology, business process analysis, data warehouse architecture and metadata management.
Anne Marie’s Areas of Expertise include: Data Administration Organization, Business Process Evaluation and Analysis, Logical Data Modeling, Enterprise Data Management/Stewardship, Metadata
Management, Client Focus and Collaboration, Project Management, Data Warehouse Architecture, Analytical and Critical Thinking.
Anne Marie holds a Bachelor of Arts and a Master’s of Business Administration degrees, both from La Salle University in Philadelphia, PA. She is active in the Philadelphia area chapter of the Data
Management Association (DAMA) and has served on the Board of DAMA International. Anne Marie is also a frequent contributor to the Data Administration Newsletter (http://www.tdan.com). Anne Marie
speaks at industry and academic conferences on the topics of metadata management, data and process modeling techniques, business aspects of electronic commerce and data warehousing. Currently, Anne
Marie is Assistant Professor of Management Information Systems (MIS) at La Salle University (http://www.lasalle.edu) in Philadelphia, PA. She is also a consultant in her areas of expertise with a
Philadelphia-area information management consultancy.