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

  • Published on

  • View

  • Download

Embed Size (px)


  • CHAPTER 4Data and Knowledge Management

  • CHAPTER OUTLINE4.1 Managing Data4.2 The Database Approach4.3 Database Management Systems4.4 Data Warehousing4.5 Data Governance4.6 Knowledge Management

  • LEARNING OBJECTIVESRecognize 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 ModelProducts

  • Chapter Opening Case (continued) Pull ModelOrders

  • Examples of Data Sources E-mailsCredit card swipesRFID tagsDigital video surveillance Radiology scansBlogs

  • 4.1 Managing DataDifficulties in Managing DataAmount 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)

  • Data, Information, Knowledge, Wisdom

  • 4.2 The Database ApproachDatabase management system (DBMS) provides all users with access to all the data.DBMSs minimize the following problems:Data redundancyData isolationData inconsistency

  • Database Approach (continued)DBMSs maximize the following issues:Data securityData integrityData independence

  • Database Management Systems

  • Data HierarchyBitByteFieldRecordFile (or table)Database

  • 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 modelEntityAttributePrimary keySecondary keys

  • Entity-Relationship ModelingDatabase designers plan the database design in a process called entity-relationship (ER) modeling.ER diagrams consists of entities, attributes and relationships.Entity classesInstanceIdentifiers

  • Entity-Relationship Diagram Model

  • 4.3 Database Management SystemsDatabase management system (DBMS)Relational database modelStructured Query Language (SQL) Query by Example (QBE)

  • Student Database Example

  • NormalizationNormalization is a method for analyzing and reducing a relational database to its most streamlined form for:Minimum redundancyMaximum data integrityBest processing performanceNormalized 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 (ITs About Business 4.1)A Turnitin originality report

  • 4.4 Data Warehousing Data warehouseData warehouses are organized by business dimension or subject.Data warehouses are multidimensional.A Data Cube

  • Data Warehousing (continued)Data warehouses are historical.Data warehouses use online analytical processing.

  • Data Warehouse Framework & Views

  • Relational Databases

  • Multidimensional Database

  • Equivalence Between Relational and Multidimensional Databases

  • Equivalence Between Relational and Multidimensional Databases

  • Equivalence Between Relational and Multidimensional Databases

  • Benefits of Data WarehousingEnd 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 GovernanceData governance Master data management Master data

  • Data Governance (continued)

  • Data Governance (continued)

  • 4.6 Knowledge ManagementKnowledge management (KM) KnowledgeIntellectual capital (or intellectual assets)

  • Knowledge Management (continued)Tacit Knowledge(below the waterline)Explicit Knowledge (above the waterline)

  • Knowledge Management (continued)Knowledge management systems (KMSs)Best practices

  • Knowledge Management System CycleCreate knowledgeCapture knowledgeRefine knowledgeStore knowledgeManage knowledgeDisseminate knowledge

  • Knowledge Management System Cycle

  • Chapter Closing CaseHigh CVM passengerstravel in style

    ******Figure 4.1 illustrates the processing of data into information and then knowledge.*This figure puts data, information, knowledge, and wisdom into perspective. *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.*Data security: Keeping the organizations 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.*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.*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 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.*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.

    *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.*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.*This figure (Figure 4.9) shows the process of building and using a data warehouse.*This is the first slide (Figure 4.10) of five showing the relationship between relational databases and a multidimensional data structure (or data cube).*Figure 4.11 a, b, and c.*Figure 4.12 a, b, and c.**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.*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.*This image shows where data governance and master data management fit into the organizations IT governance.*Knowledge management is a process that helps organizations manipulate important knowledge that is part of the organizations memory, usually in an unstructured format.

    Knowledge that is contextual, relevant, and actionable.

    Intellectual capital is another term often used for knowledge.*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 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.***