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.

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)

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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

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Knowledge Management System Cycle

Chapter Closing Case

• The Problem

• The Solution

• The Results