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CHAPTER 5 Data and Knowledge Management Announcements • Today • Chapter 5 – Data and Knowledge Mgmt • Friday • Access Tutorial • Questions/comments Data and Knowledge Management: Application to other majors • Accounting • Marketing • Finance • Operations Management Annual Flood of Data from….. Credit card swipes E-mails Digital video Online TV RFID tags Blogs Digital video surveillance Radiology scans Source: Media Bakery Industrial Revolution of Data => “Big Data” Measuring amount of data Bit (binary digit) Byte (eight bits) Letter “A” = 01000001 Annual Flood of New Data! In the zettabyte range 1 zettabyte = 1,000,000,000,000,000 ,000,000 bytes “Big Data” is getting bigger Solutions to manage it © Fanatic Studio/Age Fotostock America, Inc. Difficulties in Managing Data Amount of Data increasing Data is subject to data rot (degrade overtime) Multiple sources of data Information systems that do not communicate with each other Spread throughout organizations Source: Media Bakery Solution: Governance Data Governance => Set of Rules Implementation Strategy: Master Data Management Goal: Create a “single version of the truth” Master Data Management: Types of Data John 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 John Stevens Intro to Management Information Systems ISMN 3140 10 AM until 11 AM Mondays and Wednesdays Room 41 Smith Hall Professor Rainer Master Data Student Course Course No. Time Weekday Location Instructor 5.2 The Database Approach Database management system (DBMS) Minimize the following problems: 1. Data redundancy 2. Data isolation 3. Data inconsistency Maximize the following: 1. Data security 2. Data integrity 3. Data independence Database Management Systems How is data organized in a DB: Data Hierarchy Field Is a grouping of Record Is a grouping of File (or table) Is a grouping of Database Data Hierarchy (continued) Example: Field Record Data Hierarchy (continued) Example: Field Record Designing the Database: Data Model Entity (e.g. Student) Instance (e.g. John Doe) Attribute (e.g. Student name) Primary key (e.g. Student ID) Secondary keys (e.g. Major) Entity Primary Secondary Instance Student Student Student ID (pk) Student Name Student Address Student Major 850000000 (pk) John Doe 123 Anywhere St. MIS Data Model Technique: Entity-Relationship Modeling Database designers plan the database design in a process called entity-relationship (ER) modeling/diagrams. ER diagrams consists of entities, attributes and relationships. Entity-relationship diagram model Database Management Systems Database management system (DBMS) Focus of this course: Relational database model Related Tables (PK Important) Data dictionary How do you request data? Structured Query Language (SQL) - keywords Query by Example (QBE) – forms/templates Microsoft Access Student Database Example Relational DB Effectiveness: Normalization Normalization (most streamlined DB) Minimum redundancy Maximum data integrity Best processing performance Normalized data occurs when attributes in the table depend only on the primary key. Non-Normalized Database Normalizing the Database (part A) Normalizing the Database (part B) Normalization Produces Order Beyond Databases: Data Warehousing Data warehouses and Data Marts Organized by business dimension or subject Multidimensional Historical Use online analytical processing Data Warehouse Framework & Views Source Systems Data Integration Data Storage Users 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. Knowledge Management (KM) Knowledge aka. Intellectual Capital Knowledge management systems (KMSs) Goal: Systematize, enhance and expedite Examples © Peter Eggermann/Age Fotostock America, Inc. Challenges to Capturing Knowledge Explicit Knowledge (above the waterline) Policies, Procedures, Goals Tacit Knowledge (below the waterline) Experience, Insights, Know-how © Ina Penning/Age Fotostock America, Inc. Knowledge Management System Cycle DB Exercise Create the entities you would need for a student registration system. Draw out how the tables may look and how they are related. Next Class… • Access Tutorial (bring casebook) • We will be continuing through the first Access Tutorial (Sales Rep) • Bring your flash drive • You will need to save your files either to a flash drive or your Timmy account