1. Chapter 3 Data and Knowledge Management alexmillos/Shutterstock

2. Chapter Outline 3.1 Managing Data 3.2 The Database Approach 3.3 Database Management Systems 3.4 Data Warehouses and Data Marts 3.5 Knowledge Management

3. Learning Objectives 1.Discuss ways that common challenges in managing data can be addressed using data governance. 2.Explain how to interpret relationships depicted in an entity-relationship diagram. 3.Discuss the advantages and disadvantages of relational databases. 4.Explain the elements necessary to successfully implement and maintain data warehouses. 5.Describe the benefits and challenges of implementing knowledge management systems in organizations.

4. Introduction Opening Case: Rollins Automotive The amount of digital data increases every year. Data provides information, creating knowledge that is used in decision making. How does IT contribute to the large amount of data?

5. 3.1 Managing Data High quality data are Accurate Complete Timely Consistent Accessible Relevant Concise Explain and give examples of each of the quality data dimensions. Source: Media Bakery

6. The Difficulties of Managing Data Amount of data is increasing exponentially Data are scattered throughout organizations and collected by many individuals using various methods and devices Data come from many sources Data degrade over time (outdated data) Data are subject to data rot (outdated, destroyed storage media) Data security, quality, and integrity are critical, yet easily jeopardized Inconsistent, conflicting data due to nonintegrated information systems Federal regulations (Sarbanes-Oxley) Companies are drowning in unstructured data

7. Data Governance Data governance An approach to managing information across an entire organization Master data management (MDM) Master data management A strategy for data governance A process that spans all of an organization’s business processes and applications Allows companies to store, maintain, exchange, and synchronize a consistent, accurate, and timely “single version of the truth” for the company’s core master data Master data: A set of core data that covers a complete enterprise information system

8. 3.2 The Database Approach 1950s–1970s: File management environment 1970s–present: Database management approach Databases minimize the following problems: Data redundancy: The same data are stored in many places Data isolation: Applications cannot access data associated with other applications Data inconsistency: Various copies of the data do not agree And help maximize Data security: Keeping the organization’s data safe from theft, modification, and/or destruction Data integrity: Data must meet constraints and be reliable Data independence: Applications and data are independent of one another

9. Database Management Systems Database management system (DBMS): Specific type of software for creating, storing, organizing, and accessing data from a database

10. The Data Hierarchy Digital data are organized in a hierarchy from the smallest to the largest Figure 3.2 Hierarchy of data for a computer-based file

11. Designing the Database Data model: A diagram that represents the entities in the database and their relationships Entity-relationship (ER) modeling Entity A person, place, thing, or event about which information is maintained Entity classes: Groups of entities of a certain type (group of records) Entity instance: The representation of a particular entity (a record) Attribute A particular characteristic or quality of a particular entity Primary key (or identifier): A field that uniquely identifies a record Secondary keys: Other fields that have some identifying information (e.g., major, state) Foreign key: Established relationships between tables Relationship Types: One-to-one, One-to-many, Many-to-many Minimum and maximum cardinality

12. ER Diagram

14. 3.3 Database Management Systems Database management system (DBMS) A set of programs that provides users with tools to add, delete, access, modify, and analyze data stored in one location Relational database The most popular database architecture Widely used by organizational employees Examples: Microsoft Access and Oracle

15. Relational Database Model Data represented as two-dimensional tables with columns and rows

16. Query Languages Query languages are used to request information from a database. Structured query language (SQL) The most popular query language Allows users to perform complicated searches using relatively simple statements or key words SELECT (specifies a desired attribute) FROM (specifies the table to be used) WHERE (specifies conditions to apply in the query) Query by example (QBE) Allows users to fill out a grid or template to construct a sample or description of the needed data

17. Data Dictionary and Normalization Data dictionary Defines the format necessary to enter the data into the database Creates standard definitions for all attributes Provides organizational data resource inventory for effective data management Normalization A process of improving the database design structure by putting it into its most streamlined form When data are normalized, attributes in the table depend only on the primary key Streamlines complex groupings of data Minimizes redundant data Maximizes data integrity Provides best processing performance

18. 3.4 Data Warehouses and Data Marts Data warehouse A repository of historical data organized by subject to support decision makers in the organization Data mart A low-cost, scaled-down version of a data warehouse designed for the end-user needs in a strategic business unit (SBU) or a department

19. Characteristics of Data Warehouse Organized by business dimension or subject Use Online analytical processing (OLAP) Involves the analysis of accumulated data by end users Wisconsin Department of Revenue Not the same as Online transaction processing (OLTP) where data from business transactions are processed online as soon as they occur Integrated Collects data from multiple systems that are integrated around subjects Time variant Contains historical data used to detect deviations, trends, and long-term relationships Nonvolatile Only IT professionals, not users, can change or update the data Multidimensional structure Unlike two-dimensional relational databases

20. A Generic Data Warehouse Environment Source systems Provide data to the warehouse or mart Data integration (ETL process) Utilize IT to Extract data from source systems, Transform it, and Load it into a warehouse or mart Storing the data Different architectures are available Metadata (data about the data) Needed by both IT professionals and end users Data quality The quality of the data in the warehouse must meet users’ needs Governance Ensures that the systems meet organizational needs Users Include information producers (create information for others) and information consumers

21. Figure 3.9 Data Warehouse Framework and Views

22. 3.5 Knowledge Management Knowledge is information in action Contextual, relevant, and useful Also called intellectual capital or intellectual assets Explicit knowledge Objective, rational, technical knowledge that has been documented and can be distributed or transformed into a process or a strategy Examples: Policies, procedural guides, reports, products, strategies, goals, core competencies Tacit knowledge Cumulative store of subjective or experiential learning Highly personal, imprecise, and costly to transfer Examples: Experiences, insights, expertise, know-how, trade secrets, understanding, skill sets, learning, and organizational culture

23. Knowledge Management Systems Knowledge management (KM) A process that helps organizations manipulate important knowledge that is part of the organization’s memory, usually in an unstructured format Knowledge management systems Use information technologies to systematize, enhance, and expedite intrafirm and interfirm knowledge management Utilize best practices as the most effective and efficient ways of doing things

24. Figure 3.13 Knowledge Management System Cycle

25. What’s in IT for ME? Accounting Use databases to keep track of the transactions and internal controls of an organization Finance Use external databases to obtain financial data Marketing Access marketing data and transactions Contribute to an organization’s knowledge base Production/Operations Management Use databases to perform optimization analysis Human Resources Management Utilize databases to keep track of employee records Compensate employees who contribute to knowledge base MIS Manage databases, maintain data dictionary, and help users access needed data and generate reports with query tools

26. Closing Case 1: Big Data The Problem The Solution Questions Is Big Data really a problem on its own, or are the use, control, and security of the data the true problem? Provide specific examples to support your answer. What are the implications of having incorrect data points in your Big Data? What are the implications of incorrect or duplicated customer information? How valuable are decisions that were made based on faulty information derived from incorrect data?

27. Closing Case 2: Kayak Uses QuickBase for Global Collaboration The Problem The Solution Questions Describe the many ways that Kayak uses QuickBase. Is QuickBase just a database management system, or is it something more? Support your answer. How can Kayak, a Web-based company, operate without an information systems department? (Hint: See Plug IT In 3.) What are the advantages and disadvantages of operating this way?