CHAPTER 5CHAPTER OUTLINELEARNING OBJECTIVESLearning Objectives (continued)Big Data Case – pages 112 & 113Annual Flood of Data from…..Annual Flood of New Data!5.1 Managing DataData GovernanceMaster Data Management5.2 The Database ApproachDatabase Approach (continued)Database Management SystemsData HierarchyHierarchy of Data for a Computer-Based FileData Hierarchy (continued)Slide 18Slide 19Designing the DatabaseEntity-Relationship ModelingRelationships Between Entities (see page 120)Entity-relationship diagram model5.3 Database Management SystemsStudent Database ExampleNormalizationNon-Normalized RelationNormalizing the Database (part A)Normalizing the Database (part B)Normalization Produces OrderSlide 315.4 Data WarehousingData Warehouse Framework & ViewsRelational DatabasesMultidimensional DatabaseEquivalence Between Relational and Multidimensional DatabasesSlide 37Slide 38Benefits of Data WarehousingData Concepts5.5 Knowledge ManagementKnowledge Management (continued)Slide 43Slide 45Chapter Closing CaseCHAPTER 5Data and Knowledge ManagementCHAPTER OUTLINE5.1 Managing Data5.2 The Database Approach5.3 Database Management Systems5.4 Data Warehouses and Data Marts5.5 Knowledge ManagementLEARNING OBJECTIVES1. Identify three common challenges in managing data, and describe one way organizations can address each challenge using data governance.2. Name six problems that can be minimized by using the database approach.3. Demonstrate how to interpret relationships depicted in an entity-relationship diagram.4. Discuss at least one main advantage and one main disadvantage of relational databases.Learning Objectives (continued)5. Identify the six basic characteristics of data warehouses, and explain the advantages of data warehouses and marts to organizations.6. Demonstrate the use of a multidimensional model to store and analyze data.7. List two main advantages of using knowledge management, and describe the steps in the knowledge management system cycle.Big Data Case – pages 112 & 113Walmart processes over 1,000,000 transactions per hourFrom 2006 to 2010 IBM invested over $12,000,000,000 for setting up business intelligence centersUsing big data to spot trends before your competitors spot them can be a strategic advantage (Best Buy success, Nestle failure)Annual Flood of Data from….. Credit card swipesE-mailsDigital videoOnline TVRFID tagsBlogsDigital video surveillanceRadiology scansSource: Media BakeryAnnual Flood of New Data!In the zettabyte rangeA zettabyte is a trillion gigabytes© Fanatic Studio/Age Fotostock America, Inc.5.1 Managing DataThe Difficulties of Managing DataData GovernanceData GovernanceSee video•Data Governance – manage data across the entire organization•Master Data Management – have all organization processes access a single version of the data•Master Data – an enterprise system of core dataBig data can have big data errorsMaster Data ManagementJohn Stevens registers for Introduction to Management Information Systems (ISMN 3140) from 10 AM until 11 AM on Mondays and Wednesdays in Room 41 Smith Hall, taught by Professor Rainer.Transaction Data Master DataJohn Stevens StudentIntro to Management Information Systems CourseISMN 3140 Course No.10 AM until 11 AM TimeMondays and Wednesdays WeekdayRoom 41 Smith Hall LocationProfessor Rainer Instructor5.2 The Database ApproachDatabase management system (DBMS) minimize the following problems:Data redundancyData isolationData inconsistencyDatabase Approach (continued)DBMSs maximize the following issues:Data securityData integrityData independenceDatabase Management SystemsData HierarchyBitByteFieldRecordFile (or table)DatabaseA zero or a one8 bits, a single character or numberA column in a spreadsheet like a nameA row in a spreadsheet like name and address and phone #A collection of related recordsA collection of related filesHierarchy of Data for a Computer-Based FileData Hierarchy (continued)Bit (binary digit)Byte (eight bits)Data Hierarchy (continued)Example of Field and RecordData Hierarchy (continued)Example of Field and RecordDesigning the Database Data modelEntityAttributePrimary keySecondary keysThe 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 may not identify the file with complete accuracy.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 classes Instance IdentifiersRelationships Between Entities (see page 120) Maximum number of instancesMinimum number of instancesEntity-relationship diagram model5.3 Database Management SystemsDatabase management system (DBMS)[defines both the data structure and the data relationships]Relational database model Structured Query Language (SQL) Query by Example (QBE)One table is a “flat file”, it is the relationship between tables that make a databaseStudent Database ExampleCan you determine an attribute? A primary key? A secondary key? An instance?NormalizationNormalization Minimum redundancyMaximum data integrityBest processing performanceNormalized data occurs when attributes in the table depend only on the primary key.Non-Normalized RelationNormalizing the Database (part A)Normalizing the Database (part B)Normalization Produces OrderNon-Normalized Relation5.4 Data Warehousing Data warehouses and Data Marts Organized by business dimension or subject Multidimensional Historical Use online analytical processingA data warehouse is a repository of historical data organized by subject to support decision makers in the organization.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 & ViewsRelational