Information Resource Management: Data Administration versus Database Administration

By Anne Marie Smith, Ph.D.


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, much confusion exists 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, Metadata Analysis, Data Analysis, is a relative newcomer to the field of data processing. It is only within the last 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.  The overall objective of Data Administration is to plan, document, manage and control the information resources of an entire organization.  The role of Data Administration is to integrate and manage corporate-wide information resources by using data dictionaries, repositories, Computer Assisted Software Engineering (CASE – modeling) tools and logically designed data structures.

A difference between data administration (DA) and database administration (DBA) can be outlined using this example.  It is the responsibility of data administration to determine the contents and logical boundaries of each database. Database administrators are responsible for the design, implementation, maintenance and security of physical structures (databases) once these structures have been logically designed.  DA first builds a logical model of the database which is later implemented by DBA; this is analogous to the distinction between systems analysts and systems designers/programmers.  However, planning for the effective use of data throughout the organization is a function that data administration should fulfill in advance of any application design (logical or physical).  It is a task that should not be left for the developers to fulfill.  For example, a Business Area Analysis (BAA) should be performed before any application in that business area is designed.  Multiple design efforts can occur from the results of a single BAA, and can be more easily coordinated using a solid BAA as the foundation.

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, and abbreviation)
  • Conduct data definition sessions with users
  • Manage and administer metadata 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 database administrator (DBA) or systems programmer is portable from one industry to another, with minimal retraining as long as the technology remains constant. A data analyst (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 can be 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 metadata 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. They should be instructed in the basic concepts of modeling to understand the relevance of model-driven development and its application to their work. 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 metadata repository and CASE (modeling) products, and logically designed data structures.

Roles in Information Resource Management

Data Administration: Planning, Analysis; Identification and monitoring of the content of corporate databases; Control of standard definition for data elements; Define and enforce naming conventions; Implement data administration tools such as repository and modeling products.

Database Administration: Physical Design and Operational Use; Definition of physical databases using logical design; Analysis of data volumes and space requirements; Performance tuning of databases; Execution of backup and recovery functions for databases; Verification of integrity of databases.

Application Development: Systems Design and Implementation: Creation of programming routines and screen formats that address the use of data discovered in the logical modeling and business requirements phase; Testing of all components of the application in conjunction with the database; Interaction with designated users.

The coordination of Data Administration, Database Administration and application development skills, talents, roles and responsibilities will enable an organization to realize the goal of proper management of its information resource.

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