Data and Knowledge Management CHAPTER 5

5.1 Managing Data 5.2 The Database Approach 5.3 Database Management Systems 5.4 Data Warehouses and Data Marts 5.5 Knowledge Management CHAPTER OUTLINE

ANNUAL FLOOD OF DATA FROM….. Credit card swipes s Digital video Online TV RFID tags Blogs Digital video surveillance Radiology scans Source: Media Bakery

ANNUAL FLOOD OF NEW DATA! In the zettabyte range A zettabyte is 1000 exabytes © Fanatic Studio/Age Fotostock America, Inc.

DIFFICULTIES OF MANAGING DATA Amount of data increasing exponentially Data are scattered throughout organizations and collected by many individuals using various methods and devices. Data come from many sources. Data security, quality, and integrity are critical.

DIFFICULTIES OF MANAGING DATA Amount of data increasing exponentially Data are scattered throughout organizations and collected by many individuals using various methods and devices. Data security, quality, and integrity are critical.

DATA GOVERNANCE See videovideo Data Governance Master Data Management Master Data

MASTER DATA MANAGEMENT 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 Master Data John StevensStudent Intro to Management Information SystemsCourse ISMN 3140Course No. 10 AM until 11 AMTime Mondays and WednesdaysWeekday Room 41 Smith HallLocation Professor RainerInstructor

Database management system (DBMS) minimize the following problems: Data redundancy Data isolation Data inconsistency 5.2 THE DATABASE APPROACH

DBMSs maximize the following issues: Data security Data integrity Data independence DATABASE APPROACH (CONTINUED)

DATABASE MANAGEMENT SYSTEMS

Bit Byte Field Record File (or table) Database DATA HIERARCHY

HIERARCHY OF DATA FOR A COMPUTER-BASED FILE

Bit (binary digit) Byte (eight bits) DATA HIERARCHY (CONTINUED)

Example of Field and Record DATA HIERARCHY (CONTINUED)

Example of Field and Record DATA HIERARCHY (CONTINUED)

Data model Entity Attribute Primary key Secondary keys DESIGNING THE DATABASE

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-RELATIONSHIP MODELING

RELATIONSHIPS BETWEEN ENTITIES

ENTITY-RELATIONSHIP DIAGRAM MODEL

Database management system (DBMS) Relational database model Structured Query Language (SQL) Query by Example (QBE) 5.3 DATABASE MANAGEMENT SYSTEMS

STUDENT DATABASE EXAMPLE

Normalization Minimum redundancy Maximum data integrity Best processing performance Normalized data occurs when attributes in the table depend only on the primary key. NORMALIZATION

NON-NORMALIZED RELATION

NORMALIZING THE DATABASE (PART A)

NORMALIZING THE DATABASE (PART B)

NORMALIZATION PRODUCES ORDER

Data warehouses and Data Marts Organized by business dimension or subject Multidimensional Historical Use online analytical processing 5.4 DATA WAREHOUSING

DATA WAREHOUSE FRAMEWORK & VIEWS

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. BENEFITS OF DATA WAREHOUSING

Knowledge management (KM) Knowledge Intellectual capital (or intellectual assets) 5.5 KNOWLEDGE MANAGEMENT © Peter Eggermann/Age Fotostock America, Inc.

KNOWLEDGE MANAGEMENT (CONTINUED) Tacit Knowledge (below the waterline) Explicit Knowledge (above the waterline) © Ina Penning/Age Fotostock America, Inc.

Knowledge management systems (KMSs) Best practices KNOWLEDGE MANAGEMENT (CONTINUED) © Peter Eggermann/Age Fotostock America, Inc.

Create knowledge Capture knowledge Refine knowledge Store knowledge Manage knowledge Disseminate knowledge KNOWLEDGE MANAGEMENT SYSTEM CYCLE