Data and Knowledge Management - .• Entity-Relationship Modeling • Entity-Relationship Diagram

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  • Data and Knowledge Management


  • 1. Discuss ways that common challenges in managing data can be addressed using data governance.

    2. Define Big Data, and discuss its basic characteristics. 3. Explain how to interpret the relationships depicted in

    an entity-relationship diagram. 4. Discuss the advantages and disadvantages of relational

    databases. 5. Explain the elements necessary to successfully

    implement and maintain data warehouses. 6. Describe the benefi ts and challenges of implementing

    knowledge management systems in organizations.

  • 1.Managing Data 2.Big Data 3.The Database Approach 4.Database Management Systems 5.Data Warehouses and Data Marts 6.Knowledge Management

  • [ Opening Case Tapping the Power of Big Data ]

    What We Learned from This Case

  • Managing Data 5.1 The Difficulties of Managing Data Data Governance

  • Difficulties in Managing Data

    Data increases exponentially with time Multiple sources of data Data rot, or data degradation Data security, quality, and integrity Government Regulation

  • Multiple Sources of Data

    Internal Sources Corporate databases, company documents

    Personal Sources Personal thoughts, opinions, experiences

    External Sources Commercial databases, government reports, and

    corporate Web sites.

  • Data Governance

    An approach to managing information across an entire organization.

    Master Data Master Data Management

  • Big Data 5.2 Defining Big Data Characteristics of Big Data Managing Big Data Leveraging Big Data

  • Defining Big Data

    Big data is difficult to define Two Descriptions of Big Data

  • From Gartner Research (Big Data Description 1 of 2) Diverse, high-volume, high-velocity information

    assets that require new forms of processing to enable enhanced decision making, insight discovery, and process optimization. (

  • From the Bid Data Institute (Big Data Description 2 of 2) Exhibit variety Includes structured, unstructured, and semi-structured

    data Are generated at high velocity with an uncertain pattern Do not fit neatly into traditional, structured, relational

    databases Can be captured, processed, transformed, and analyzed in

    a reasonable amount of time only by sophisticated information systems.


  • Defining Big Data

    Big Data Generally Consist of: Traditional enterprise data Machine-generated/sensor data Social Data Images captured by billions of devices located

    around the world Digital cameras, camera phones, medical scanners, and

    security cameras

  • Characteristics of Big Data

    Volume Velocity Variety

  • Managing Big Data

    When properly analyzed big data can reveal valuable patterns and information.

    Database environment Traditional relational databases versus

    NoSQL databases Open source solutions

  • Leveraging Big Data

    Creating Transparency Enabling Experimentation Segmenting Population to Customize

    Actions Replacing/Supporting Human Decision

    Making with Automated Algorithms Innovating New Business Models, Products,

    and Services Organizations Can Analyze Far More Data

  • The Database Approach 5.3 The Data Hierarchy Designing the Database

  • Databases Minimize Three Main Problems

    Data Redundancy Data Isolation Data Inconsistency

  • Databases Maximize the Following

    Data Security Data Integrity Data Independence

  • Data Hierarchy

    Bit Byte Field Data File or Table Database

  • Designing the Database

    Key Terms Data Model Entity Instance Attribute Primary Key Secondary Keys

  • Designing the Database

    Entity-Relationship Modeling Entity-Relationship Diagram Cardinality Modality

  • Database Management Systems

    5.4 The Relational Database Model Databases in Action

  • The Relational Database Model

    Based on the concept of two-dimensional tables

    Database Management System (DBMS) Query Languages Data Dictionary Normalization

  • [about business]

    Database Solution for the German Aerospace Center

  • Data Warehouses and Data Marts

    5.5 Describing Data Warehouses and

    Data Marts A Generic Data Warehouse


  • Describing Data Warehouses & Data Marts Data Warehouse

    A repository of historical data that are organized by subject to support decision makers in the organization

    Data Mart A low-cost, scaled-down version of a data

    warehouse designed for end-user needs in a strategic business unit (SBU) or individual department.

  • Describing Data Warehouses & Data Marts Basic characteristics of data warehouses

    and data marts Organized by business dimension or subject Use online analytical processing (OLAP) Integrated Time variant Nonvolatile Multidimensional

  • A Generic Data Warehouse Environment

    Source Systems Data Integration Storing the Data

    Metadata Data Quality Data Governance Users

  • [about business]

    Hospital Improves Patient Care with Data Warehouse

  • Knowledge Management 5.6 Concepts and Definitions Knowledge Management Systems The KMS Cycle

  • Concepts & Definitions

    Knowledge Management (KM) A process that helps manipulate important

    knowledge that comprises part of the organizations memory, usually in an unstructured format.

    Knowledge Explicit & Tacit Knowledge Knowledge Management System (KMS)

  • Knowledge Management Systems (KMS)

    Refer to the use of modern information technologies the Internet, intranet, extranets, databases to systematize, enhance, and expedite intrafirm and interfirm knowledge management. Best practices

  • The KMS Cycle

    Create Knowledge Capture Knowledge Refine Knowledge Store Knowledge Manage Knowledge Disseminate Knowledge

  • [ Closing Case Can Organizations Have Too Much Data? ]

    The Problem The Solution The Results

    Slide Number 1Slide Number 2Slide Number 3[ Opening Case Tapping the Power of Big Data ]Slide Number 5Slide Number 6Slide Number 7Slide Number 8Slide Number 9Slide Number 10Slide Number 11Slide Number 12Slide Number 13Slide Number 14Slide Number 15Slide Number 16Slide Number 17Slide Number 18Slide Number 19Slide Number 20Slide Number 21Slide Number 22Slide Number 23Slide Number 24Slide Number 25Slide Number 26Slide Number 27Slide Number 28Slide Number 29Slide Number 30Slide Number 31Slide Number 32Slide Number 33Slide Number 34[ Closing Case Can Organizations Have Too Much Data? ]