Information Management Preliminary PDF

Information Management Preliminary Peak exam questions Reviewing is not easy 1. Data - Raw facts, or facts that have not yet been processed to reveal their meaning to the end user. 2. Information – The result of processing raw data to reveal it...

Information Management Preliminary Peak exam questions Reviewing is not easy 1. Data - Raw facts, or facts that have not yet been processed to reveal their meaning to the end user. 2. Information – The result of processing raw data to reveal its meaning. Information consists of transformed data and facilities decision making 3. Database - is a shared, integrated computer structure that houses a collection 4. End-user data – that is, raw facts of interest to the end user. 5. Metadata, or data about data - through which the end-user data is integrated and managed. 6. Database Management System (DBMS) - is a collection of programs that manages the database structure and controls access to the data stored in the database. 7. Improved data sharing - The DBMS serves as the intermediary between the user and the database. 8. Improved data security - The more users access the data, the greater the risks of data security breaches. 9. Better data integration - Wider access to well-managed data promotes an integrated view of the organization’s operations and a clearer view of the big picture. 10. Minimized data inconsistency - Data inconsistency exists when different versions of the same data appear in different places. 11. Improved data access - The DBMS makes it possible to produce quick answers to ad hoc queries. 12. Improved decision making - Better-managed data and improved data access make it possible to generate better quality information, on which better decisions are based. 13. Increased end-user productivity - The availability of data, combined with the tools that transform data into usable information, empowers end users to make quick, informed decisions that can make the difference between success and failure in the global economy. 14. Single-user database – A type of database that supports only one user at a time. 15. Desktop database – A single user database that runs on a personal computer. 16. Multiuser database – A type of database that supports multiple users at the same time. 17. Workgroup database – A type of database that supports a relatively small number of users or a specific department within an organization. 18. Enterprise database – A type of database that is used by the entire organization and supports many users across many departments. 19. Centralized database – A type of database that supports data located at a single site. 20. Distributed database – A type of database that supports data distributed across several different sites. 21. Cloud database – A database that is created and maintained using cloud services, such as Microsoft Azure or Amazon AWS 22. General-purpose database – A database that contains a wide variety of data used in multiple disciplines. 23. Discipline-specific database – A type of database that contains data focused on specific subject areas 24. Operational database – A type of database designed primarily to support a company's day-to-day operations. 25. Analytical database – A type of database focused primarily on storing historical data and business metrics used for tactical or strategic decision making. 26. Database Design - refers to the activities that focus on the design of the database structure that will be used to store and manage end-user data. 27. Lengthy development times – The first and most glaring problem with the file system approach is that even the simplest data-retrieval task requires extensive programming. With the older file systems, programmers had to specify what must be done and how to do it. 28. Difficulty of getting quick answers – The need to write programs to produce even the simplest reports makes ad hoc queries impossible. 29. Complex system administration – System administration becomes more difficult as the number of files in the system expands. 30. Lack of security and limited data sharing – Another fault of a file system data repository is a lack of security and limited data sharing. 31. Extensive programming – Making changes to an existing file structure can be difficult in a file system environment. 32. Structural dependence – A data characteristic in which a change in the database schema affects data access, thus requiring changes in all access programs. 33. Structural independence – A data characteristic in which changes in the database schema do not affect data access. 34. Data dependence – A data condition in which data representation and manipulation are dependent on the physical data storage characteristics. 35. Data independence – A condition in which data access is unaffected by changes in the physical data storage characteristics. 36. Data redundancy – It exists when the same data is stored unnecessarily at different places. 37. Poor data security – Having multiple copies of data increases the chances for a copy of the data to be susceptible to unauthorized access. 38. Data inconsistency – Data inconsistency exists when different and conflicting versions for the same data appear in different places. 39. Data-entry errors – Data-entry errors are more likely to occur when complex entries are made in several different files or recur frequently in one or more files. 40. Data integrity problems – It is possible to enter a nonexistent sales agent's name and phone number into the Customer file, but customers are not likely to be impressed if the insurance agency supplies the name and phone number of an agent who does not exist. 41. Data integrity problems – It is possible to enter a nonexistent sales agent's name and phone number into the Customer file, but customers are not likely to be impressed if the insurance agency supplies the name and phone number of an agent who does not exist. 42. Data Modeling - the first step in designing a database, refers to the process of creating a specific data model for a determined problem domain. 43. Data model - is relatively simple representation, usually graphical, of more complex real- world data structures. In general terms, a model is an abstraction of a more complex real-world object or event. 44. Entity – It is a person, place, thing, or event about which data will be collected and stored. 45. Attribute – It is a characteristic of an entity. 46. Relationship – It describes an association among entities. 47. Hierarchical Model - It was developed in the 1960s to manage large amounts of data for complex manufacturing projects. 48. Network Model - It was created to represent complex data relationships more effectively than the hierarchical model, to improve database performance, and to impose a database standard. 49. Schema – It is the conceptual organization of the entire database as viewed by the database administrator. 50. Subschema – It defines the portion of the database by the application programs that actually produce the desired information from the data in the database. 51. Data Manipulation Language (DML) – It defines the environment in which data can be managed. 52. Data Definition Language (DDL) – It allows the database administrator to define the schema components. 53. Relational Model - It was introduced in 1970 by E. F. Codd of IBM. 54. Entity Relationship Model - It was introduced in 1976 by Peter Chen 55. Big Data - It refers to a movement to find new and better ways to manage large amounts of web and sensor-generated data and derive business insight from it, while simultaneously providing high performance and scalability at a reasonable cost. 56. Volume – It refer to the amounts of data being stored. 57. Velocity – It refers not only to the speed with which data grows but also to the need to process this data quickly in order to generate information and insight. 58. Variety – It refers to the fact that the data being collected comes in multiple different data formats. 59. NoSQL - It is a large-scale distributed database system that stores structured and unstructured data in efficient ways.