6. Data and Database in university of science and technology
- 1. Database Systems DATA AND DATABASE ADMINISTRATION
- 2. Organizational Context for Managing Databases Database Support for Management Decision Making Approaches for Managing Data Resources Responsibilities of Data Specialists Challenges of Big Data
- 3. Database Support for Management Decision Making Operational Database: a database to support the daily functions of an organization. • Databases support business operations and management decision making at various levels. Most large organizations have developed many operational databases to help conduct business efficiently. Operational databases directly support major functions such as order processing, manufacturing, accounts payable, and product distribution. The reasons for investing in an operational database are typically faster processing, larger volumes of business, and reduced personnel costs. As organizations achieve improved operations, they begin to realize the decision- making potential of their databases. Operational databases provide the raw materials for management decision making
- 4. Database Support for Management Levels
- 5. Lower level management can obtain exception and problem reports directly from operational databases. However, much value must be added to leverage the operational databases for middle and upper management. The operational databases must be cleaned, integrated, and summarized to provide value for tactical and strategic decision making. Integration is necessary because operational databases often are developed in isolation without regard for the information needs of tactical and strategic decision making.
- 6. Examples of Management Decision Making • Lower-level management deals with short-term problems related to individual transactions. Periodic summaries of operational databases and exception reports assist operational management. Middle management relies on summarized data that are integrated across operational databases. Middle management may want to integrate data across different departments, manufacturing plants, and retail stores. Top management relies on the results of middle management analysis and external data sources.
- 7. Approaches for Managing Data Resources As a response to the challenges of leveraging operational databases and information technology for management decision making, several management approaches have been developed. The original approach known as information resource management was developed in the 1990s. Information resource management involves processing, distributing, and integrating information throughout an organization. A key element of information resource management is control of information life cycles Information Life Cycle: the stages of information transformation in an organization. Each entity has its own information life cycle that should be managed and integrated with the life cycles of other entities.
- 8. Typical Stages of an Information Life Cycle • Each level of management decision making and business operations has its own information life cycle. For effective decision making, the life cycles must be integrated to provide timely and consistent information. For example, information life cycles for operations provide input to life cycles for management decision making.
- 9. Starting about the mid-1990s, a movement developed to extend information resource management into knowledge management. Traditionally, information resource management has emphasized technology to support predefined recipes for decision making rather than the ability to react to a constantly changing business environment. To succeed in today's business environment, organizations must emphasize fast response and adaptation to extend planning efforts. To meet this challenge, organizations should develop systems that facilitate knowledge creation rather than information management. For knowledge creation, a greater emphasis is on human information processing and organization dynamics to balance the technology emphasis .
- 10. Knowledge Management: applying information technology with human information processing capabilities and organization processes to support rapid adaptation to change. Three Pillars of Knowledge Management
- 11. Data Governance: according to the Data Governance Institute, data governance involves the application of decision-making and authority for data-related issues. Data governance provides a system of checks and balances to develop data rules and policies, support application of data rules and policies, and evaluate compliance of data rules and policies. The system of data governance operates in a manner similar to separate government branches in which the legislative branch makes laws, the executive branch enforces laws, and the judicial branch resolves disputes about the meaning and application of laws.
- 12. Responsibilities of Data Specialists The data administrator (DA) is a middle- or upper-management position with broad responsibilities for information resource management. The database administrator (DBA) is a support role with responsibilities related to individual databases and DBMSs
- 13. Responsibilities of Data Administrators and Database Administrators
- 14. Enterprise Data Model: a conceptual data model of an organization. An enterprise data model can be used for data planning or business intelligence.
- 15. In large organizations, various titles are used for database specialists. The following list explains some common titles used in large organizations. Database architect: primarily specializes in data modeling and logical database design System DBA: interfaces with system administration and analyzes database impact on hardware and operating system Application DBA: specializes in management and usage of procedural objects including triggers, stored procedures, and transaction design Senior DBA: a highly experienced DBA who supervises junior DBAs and provides expert trouble shooting Performance DBA: specializes in physical database design and performance tuning Data warehouse administrator: specializes in operation and development of data warehouses
- 16. In small organizations, the boundary between data administration and database administration is fluid. There may not be separate positions for data administrators and database administrators. The same person may perform duties from both positions.
- 17. Challenges of Big Data In many organizations, data specialists confront the problem of exploding data growth. The growth in data comes from a variety of sources such as sensors in smart phones, energy meters, and automobiles, interaction of individuals in social media websites, radio frequency identification tags in retail, and digitized multimedia content in medicine, entertainment, and security. This data growth breaks existing systems and business processes providing challenges and opportunities for both vendors developing database technology and organizations using database technology.
- 18. The phenomenon of explosive data growth known as big data was frst stated by Doug Laney of the Meta Group in 2012. According to Laney's report, big data contains three dimensions: volume (amount of data), velocity (rate of generating and processing data), and variety (type of data especially the distinction between structured and unstructured data). Most attention is focused on data volumes but the other two dimensions must be managed effectively to deal with problems of big data.
- 19. Big Data: the phenomenon of exploding data growth. Big data has three dimensions, volume, velocity, and variety. The volume of big data that exceeds the limits of database software depends on technology, industry sector, and time.
- 20. REFERENCES 1. Michael V. Mannino, Database Design, Application Development and Administration.