CHAPTER 5
Data and Knowledge Management
CHAPTER OUTLINE
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
LEARNING OBJECTIVES
1. 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 swipes
E-mails
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 a trillion gigabytes
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5.1 Managing Data
The Difficulties of Managing Data
Data Governance
Data Governance
See 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 data
Big data can have big data errors
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 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 Instructor
5.2 The Database Approach
Database management system (DBMS) minimize the following problems:
Data redundancy
Data isolation
Data inconsistency
Database Approach (continued)
DBMSs maximize the following issues:
Data security
Data integrity
Data independence
Database Management Systems
Data Hierarchy
Bit
Byte
Field
Record
File (or table)
Database
A zero or a one
8 bits, a single character or number
A column in a spreadsheet like a name
A row in a spreadsheet like name and address and phone #
A collection of related records
A collection of related files
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 modelEntity
Attribute
Primary key
Secondary keys
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 may not identify the file with complete accuracy.
Entity-Relationship Modeling
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
Relationships Between Entities (see page 120)
Maximum number of instances
Minimum number of instances
Entity-relationship diagram model
5.3 Database Management Systems
Database 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 database
Student Database Example
Can you determine an attribute?
A primary key?
A secondary key?
An instance?
Normalization
Normalization
Minimum redundancy
Maximum data integrity
Best processing performance
Normalized data occurs 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
Non-Normalized Relation
5.4 Data Warehousing
Data warehouses and Data Marts Organized by business dimension or subject
Multidimensional
Historical
Use online analytical processing
A 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 & 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 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.
Data Concepts
Metadata – data about data such as relationships between tables or table definitions
Data quality – data is seldom 100% “clean” Data governance (link) Users include information producers and
consumers
5.5 Knowledge Management
Knowledge management (KM) Knowledge (should be contextual, relevant, and
actionable)
Intellectual capital (a.k.a. knowledge or intellectual assets)
© Peter Eggermann/Age Fotostock America, Inc.
Knowledge Management (continued)
Tacit Knowledge(below the waterline, all the stuff you know but that you don’t explicitly realize you know)
Explicit Knowledge (above the waterline)
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Knowledge Management (continued)
Knowledge management systems (KMSs)
Best practices
© Peter Eggermann/Age Fotostock America, Inc.
Knowledge Management System Cycle
Chapter Closing Case
• The Problem
• The Solution
• The Results