Data and Knowledge Management CHAPTER 4 Data and Knowledge Management

CHAPTER OUTLINE 4.1 Managing Data 4.2 The Database Approach 4.3 Database Management Systems 4.4 Data Warehousing 4.5 Data Governance 4.6 Knowledge Management

LEARNING OBJECTIVES Recognize the importance of data, issues involved in managing data and their lifecycle. Describe the sources of data and explain how data are collected. Explain the advantages of the database approach.

Learning Objectives (continued) Explain the operation of data warehousing and its role in decision support. Explain data governance and how it helps to produce high-quality data. Define knowledge, and describe different types of knowledge.

Chapter Opening Case

Chapter Opening Case (continued) Push Model Products

Chapter Opening Case (continued) Pull Model Orders

Examples of Data Sources RFID tags Credit card swipes Digital video surveillance Blogs E-mails Radiology scans

4.1 Managing Data Difficulties in Managing Data Amount of data increases exponentially. Data are scattered and collected by many individuals using various methods and devices. Data come from many sources. Data security, quality and integrity are critical.

Difficulties in Managing Data (continued) An ever-increasing amount of data needs to be considered in making organizational decisions. The Data Deluge

Data Life Cycle (Figure 4.1) Figure 4.1 illustrates the processing of data into information and then knowledge.

Data, Information, Knowledge, Wisdom This figure puts data, information, knowledge, and wisdom into perspective.

4.2 The Database Approach Database management system (DBMS) provides all users with access to all the data. DBMSs minimize the following problems: Data redundancy Data isolation Data inconsistency 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.

Database Approach (continued) DBMSs maximize the following issues: Data security Data integrity Data independence Data security: Keeping the organization’s data safe from theft, modification, and/or destruction. Data integrity: Data must meet constraints (e.g., student grade point averages cannot be negative). Data independence: Applications and data are independent of one another. applications and data are not linked to each other, meaning that applications are able to access the same data.

Database Management Systems

Data Hierarchy Bit Byte Field Record File (or table) Database A bit is a binary digit, or a “0” or a “1”. A byte is eight bits and represents a single character (e.g., a letter, number or symbol). A field is a group of logically related characters (e.g., a word, small group of words, or identification number). A record is a group of logically related fields (e.g., student in a university database). A file is a group of logically related records. A database is a group of logically related files.

Hierarchy of Data for a Computer-Based File

Data Hierarchy (continued) Bit (binary digit) Byte (eight bits)

Data Hierarchy (continued) Example of Field and Record

Data Hierarchy (continued) Example of Field and Record

Designing the Database Data model Entity Attribute Primary key Secondary keys The data model is a diagram that represents the entities in the database and their relationships. An entity is a person, place, thing, or event about which information is maintained. A record generally describes an entity. An attribute is a particular characteristic or quality of a particular entity. The primary key is a field that uniquely identifies a record. Secondary keys are other field that have some identifying information but typically do not identify the file with complete accuracy.

Entity-Relationship Modeling Database designers plan the database design in a process called entity-relationship (ER) modeling. ER diagrams consists of entities, attributes and relationships. Entity classes Instance Identifiers Entity classes are groups of entities of a certain type. An instance of an entity class is the representation of a particular entity. Entity instances have identifiers, which are attributes that are unique to that entity instance.

Entity-Relationship Diagram Model

4.3 Database Management Systems Database management system (DBMS) Relational database model Structured Query Language (SQL) Query by Example (QBE) A database management system is a set of programs that provide users with tools to add, delete, access, and analyze data stored in one location. The relational database model is based on the concept of two-dimensional tables. Structured query language allows users to perform complicated searches by using relatively simple statements or keywords. Query by example allows users to fill out a grid or template to construct a sample or description of the data he or she wants.

Student Database Example

Normalization Normalization is a method for analyzing and reducing a relational database to its most streamlined form for: Minimum redundancy Maximum data integrity Best processing performance Normalized data is when attributes in the table depend only on the primary key.

Non-Normalized Relation

Normalizing the Database (part A)

Normalizing the Database (part B)

Normalization Produces Order

Turnitin (IT’s About Business 4.1) A Turnitin originality report

4.4 Data Warehousing Data warehouse Data warehouses are organized by business dimension or subject. Data warehouses are multidimensional. A data warehouse is a repository of historical data organized by subject to support decision makers in the organization. The data cube has three dimensions: customer, product, and time. A Data Cube

Data Warehousing (continued) Data warehouses are historical. Data warehouses use online analytical processing. Historical data in data warehouses can be used for identifying trends, forecasting, and making comparisons over time. Online analytical processing (OLAP) involves the analysis of accumulated data by end users (usually in a data warehouse). In contrast to OLAP, online transaction processing (OLTP) typically involves a database, where data from business transactions are processed online as soon as they occur.

Data Warehouse Framework & Views This figure (Figure 4.9) shows the process of building and using a data warehouse.

Relational Databases This is the first slide (Figure 4.10) of five showing the relationship between relational databases and a multidimensional data structure (or data cube).

Multidimensional Database Figure 4.11 a, b, and c.

Equivalence Between Relational and Multidimensional Databases Figure 4.12 a, b, and c.

Equivalence Between Relational and Multidimensional Databases

Equivalence Between Relational and Multidimensional Databases

Benefits of Data Warehousing End users can access data quickly and easily via Web browsers because they are located in one place. End users can conduct extensive analysis with data in ways that may not have been possible before. End users have a consolidated view of organizational data.

Data Marts A data mart is a small data warehouse, designed for the end-user needs in a strategic business unit (SBU) or a department.

4.5 Data Governance Data governance Master data management Master data Data governance is an approach to managing data and information across an entire organization. Master data management is a method that organizations use in data governance. Master data are a set of core data that span all enterprise information systems.

Data Governance (continued) This slide shows the relationship among executive management, IT governance, and data governance. The slide also shows the relationship between data governance and data management. The green square should really read “master data management” rather than just “data management” as we see on the next slide.

Data Governance (continued) This image shows where data governance and master data management fit into the organization’s IT governance.

4.6 Knowledge Management Knowledge management (KM) Knowledge Intellectual capital (or intellectual assets) Knowledge management is a process that helps organizations manipulate important knowledge that is part of the organization’s memory, usually in an unstructured format. Knowledge that is contextual, relevant, and actionable. Intellectual capital is another term often used for knowledge.

Knowledge Management (continued) Explicit Knowledge (above the waterline) Tacit Knowledge (below the waterline) Explicit knowledge: objective, rational, technical knowledge that has been documented. Examples: policies, procedural guides, reports, products, strategies, goals, core competencies Tacit knowledge: cumulative store of subjective or experiential learning. Examples: experiences, insights, expertise, know-how, trade secrets, understanding, skill sets, and learning

Knowledge Management (continued) Knowledge management systems (KMSs) Best practices Knowledge management systems refer to the use of information technologies to systematize, enhance, and expedite intrafirm and interfirm knowledge management. Best practices are the most effective and efficient ways of doing things.

Knowledge Management System Cycle Create knowledge Capture knowledge Refine knowledge Store knowledge Manage knowledge Disseminate knowledge

Knowledge Management System Cycle

Chapter Closing Case High CVM passengers travel in style