Managing data resources-organising data | Management Optional Notes for UPSC PDF Download

Introduction

• Managing the flow of data within a company is intricate, as the interpretation of the same data varies across different departments. The process of Data Administration ideally commences at the conception of software, ensuring the presence of a data dictionary for consistency, avoidance of redundancy, and logical usability through the application of normalization techniques in database modeling.
• Frequently, this modeling is misconstrued as diagramming, primarily due to the prevalence of entity-relationship diagrams. Addressing questions about the future of data resource management, the need for a new direction in developing and managing data resources is considered, given the prevailing practices and the current state of data resources in both public and private sector organizations. A potential shift towards enhancing data resource quality and providing increased business support is proposed. The concepts of a database, types of databases, requirements for transaction processing, data mining, data warehousing, and their advantages are also explored.

Data Resource Management

• Data administration, also known as Data Resource Management, involves the organized administration of data typically stored in databases, managed by Database Management Systems or other systems like electronic spreadsheets. It encompasses the analysis, classification, and maintenance of an organization's data and data relationships, involving the development of data models and data dictionaries that serve as raw materials for database design.
• While data administration and database administration are distinct functions, they are often combined into one department and handled by the same personnel. Data administration focuses on modeling data as an organizational resource, treating it comprehensively, while database administration is concerned with the technical design and management of databases. Those handling data administration functions are referred to as "database analysts" or "data administrators."
• Data Resource Management involves the development and maintenance of data models to facilitate data sharing across different systems, particularly in a corporate context. It addresses data quality and compatibility between data models. This managerial activity applies information systems and data management tools to manage an organization's data resource to meet its business needs and provide information to stakeholders.
• In the information age, businesses require diverse data about their activities. As data is created with each business transaction, a new direction is needed to manage data as a critical organizational resource supporting business activities. Organizations must emphasize the information aspect of technology, determine the data needed to support business, and use appropriate technology to build and maintain a high-quality data resource.
• Data resource quality is crucial and is measured by how well the data resource supports both current and future business information demands. Stability across changing business needs and technology is the ultimate goal of data resource quality. A corporate data resource should be developed within a single, organization-wide common data architecture, which is a formal, comprehensive structure providing a common context for all data within the organization. It is subject-oriented, based on real-world objects and events, and supports business information demands effectively.

Data Administration

• Guiding the creation and overseeing the utilization of data and information as essential assets for the agency.
• Establishing and communicating agency standards, procedures, and guidelines pertaining to data names and definitions.
• Managing the inventory of agency data assets.
• Facilitating comprehension of the significance, accuracy, and timeliness of data assets.
• Encouraging the reuse of standardized data names, definitions, elements, and values.

Central Data Administration

The CMS Central Data Administration team delivers the following services to support the agency's data management and utilization goals:

• Support for Project Development: Assisting project development across the agency.
• Reviewing and Approving Logical Data Models: Conducting reviews and approvals of logical data models during the requirement analysis phase.
• Validating Naming Convention Compliance: Ensuring compliance with naming conventions in logical data models and the initial physical data model during the development phase.
• Participation in Data Governance Processes: Participating in the development of data governance processes and procedures.
• Data Change Request Management: Developing and/or supporting existing data change request management processes.
• Publishing Standardized Data Naming Conventions: Publishing standardized data naming conventions and implementing automated tools for the enforcement of naming standards.
• Enterprise Data Model (EDM) Stewardship: Managing and distributing an Enterprise Data Model (EDM) containing common and reusable data objects, along with standardized data modeling templates for expediting new software development projects or re-engineering legacy applications.
• Administration and Maintenance of Metadata Repository: Administering and maintaining the Enterprise Metadata repository.
• Data and Impact Analysis Services: Providing data and impact analysis services in collaboration with new and ongoing application development efforts.
• Demonstrating Source-to-Target Data Lineage: Demonstrating source-to-target data lineage among systems and objects.

Additionally, DRM offers common services, including the installation of databases and their Database Management Systems (DBMS), configuration, troubleshooting, backups, disaster recovery preparation, and general maintenance functions such as implementing DBMS version updates. DRM extends support for various database environments, including Datacom/DB, Oracle, and MS SQL Server, and provides assistance with Lotus Domino, TIBCO Business Works, business objects, data analysis, and data design.