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CHAPTER 3 Data and Knowledge Management

CHAPTER 3 Data and Knowledge Management. 3.1 Managing Data 3.2 The Database Approach 3.3 Database Management Systems 3.4 Data Warehouses and Data Marts

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Page 1: CHAPTER 3 Data and Knowledge Management. 3.1 Managing Data 3.2 The Database Approach 3.3 Database Management Systems 3.4 Data Warehouses and Data Marts

CHAPTER 3 Data and

Knowledge Management

Page 2: CHAPTER 3 Data and Knowledge Management. 3.1 Managing Data 3.2 The Database Approach 3.3 Database Management Systems 3.4 Data Warehouses and Data Marts

3.1 Managing Data

3.2 The Database Approach

3.3 Database Management Systems

3.4 Data Warehouses and Data Marts

3.5 Knowledge Management

Chapter 3: Data and Knowledge Management

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Page 3: CHAPTER 3 Data and Knowledge Management. 3.1 Managing Data 3.2 The Database Approach 3.3 Database Management Systems 3.4 Data Warehouses and Data Marts

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.

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LEARNING OBJECTIVES (continued)

5. Identify the six basic characteristics of data warehouses and data marts.

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.

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OPENING CASE 3.1 BIG DATA

The Problem• In fact, the amount of digital data increases tenfold every five

years. Scientists say that we are undergoing a new revolution, the “Industrial Revolution of Data,” and they have coined the term “Big Data” to describe the superabundance of data available today. This causes issues in storage space, speed, time, structure, quantity and quality of data.

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THE SOLUTION

For many organizations, the first step in managing Big Data was to deal with the problem of information silos. Silos are information that is stored and isolated in separate functional areas. Organizations began to integrate this information into a database environment and then to develop data warehouses to serve as decision-making tools. Next, they turned their attention to the business of data and information management; that is, making sense of their proliferating data. Seeing a market need for data management, Oracle, IBM, Microsoft, and SAP together have spent more than $15 billion in recent years to purchase software firms specializing in data management and business intelligence

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THE RESULTS

• The way information is managed touches all areas of life.

• Today, the availability of abundant yet small-scale data enables companies to cater to niche markets, and even individual customers, anywhere in the world.

• Some industries have led the way in gathering and exploiting data. For example, credit card companies monitor every purchase and can accurately identify fraudulent ones, using rules derived by analyzing billions of transactions.

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DISCUSSION

• What market do you believe will experience the most growth in “Big Data”? Smart Phones? Tablets?

• What type of “Big Data” is used at a university?

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3.1 MANAGING DATA

• The Difficulties of Managing Data• Data Governance

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DIFFICULTIES IN MANAGING DATA

• Amount of data increases exponentially over time• Data are scattered throughout organizations• Data obtained from multiple internal and external sources• Data degrade over time• Data subject to data rot• Data security, quality, and integrity are critical, yet easily

jeopardized• Information systems that do not communicate with each

other can result in inconsistent data;• Federal regulations.

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DATA GOVERNANCE

• Data Governance• Master Data Management• Master Data• See video

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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 Stevens Student

Intro to Management Information Systems Course

ISMN 3140 Course No.

10 AM to 11AM Time

Mondays and Wednesday Weekday

Room 41 Smith Hall Location

Professor Rainer Instructor

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3.2 THE DATABASE APPROACH

• Databases minimize the following problems:– Data redundancy: The same data are stored in many places.– Data isolation: Applications cannot access data associated with

other applications.– Data inconsistency: Various copies of the data do not agree.

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DATABASE APPROACH (CONTINUED)

• Database Management Systems (DBMS) maximize the following issues:– Data security: Databases have extremely high security

measures in place to deter mistakes and attacks. – Data integrity: Data meet certain constraints, such as no

alphabetic characters in a Social Insurance Number field.– Data independence: Applications and data are not linked to each

other, so that all applications are able to access the same data.

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DATABASE MANAGEMENT SYSTEMS

Figure 3.1 University Database Management System

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DATA HIERARCHY

• Bit: (binary digit) represents the smallest unit of data a computer can process.

• Byte: represents a single character. • Field: A logical grouping of related characters• Record: A logical grouping of related fields• File (or table): A logical grouping of related records • Database: A logical grouping of related files

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HIERARCHY OF DATA FOR A COMPUTER-BASED FILE

Figure 3.2 Hierarchy of data in University database

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DATA HIERARCHY (CONTINUED)

• Bit (binary digit): 1 0 0 1• Byte (eight bits): 01101010

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DATA HIERARCHY (CONTINUED)

Example of Field and Record

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DATA HIERARCHY (CONTINUED)

Example of Field and Record

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DESIGNING THE DATABASE

• Data model– Entity is a person, place, thing, or event which an organization

maintains information.– Instance: is a specific, unique representation of the entity. – Attribute is a characteristic or quality of a particular entity – Primary key is a field that uniquely identifies a record.– Secondary keys are other field that have some identifying

information but typically do not identify the file with complete accuracy.

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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

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RELATIONSHIPS BETWEEN ENTITIES

Figure 3.3 Cardinality and Modality Symbols23Copyright John Wiley & Sons Canada

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ENTITY-RELATIONSHIP DIAGRAM MODEL

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3.3 DATABASE MANAGEMENT SYSTEMS

• Database management system (DBMS)• Relational database model

– Structured Query Language (SQL)– Query by Example (QBE)

• Data Dictionary

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STUDENT DATABASE EXAMPLE

Figure 3.5 Example of Student Database26Copyright John Wiley & Sons Canada

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NORMALIZATION

• Normalization –Minimizes redundancy–Maximizes data integrity–Optimizes processing performance

• Normalized data occurs when attributes in the table depend only on the primary key.

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NON-NORMALIZED RELATION

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NORMALIZING THE DATABASE (PART A)

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NORMALIZING THE DATABASE (PART B)

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NORMALIZATION PRODUCES ORDER

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3.4 DATA WAREHOUSING AND DATA MARTS

• Data warehouses and Data Marts– Organized by business dimension or subject– Use On-line Analytical Processing– Integrated– Time Variant– Nonvolatile– Multidimensional

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THE ENVIRONMENT FOR DATA WAREHOUSING AND DATA MARTS

• Source systems that provide data to the data warehouse or data mart

• Data integration technology and processes that are needed to prepare the data for use

• Different architectures for storing data in an organization’s data warehouse or data marts

• Different BI tools and applications for the variety of users

• The need for metadata, data quality, and governance processes to be in place to ensure that the data warehouse or data mart meets its purposes

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DATA WAREHOUSE FRAMEWORK

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RELATIONAL DATABASES

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MULTIDIMENSIONAL DATABASE

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EQUIVALENCE BETWEEN RELATIONAL AND MULTIDIMENSIONAL DATABASES

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DATA INTEGRATION (ETL)

• To extract data from source systems, transform them, and load them into a data mart or warehouse.

• Can be performed by hand-written code (e.g., SQL queries) or by commercial data-integration software.

• Can be transformed to make them more useful.

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STORING THE DATA

• The most common architecture is one central enterprise data warehouse, without data marts.

• Independent data marts, which store data for a single or a few applications, such as in marketing or finance.

• Hub and spoke stores data in a central data warehouse while simultaneously maintaining dependent data marts that obtain their data from the central repository.

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STORING DATA (CONTINUED)

• Metadata is Data about data. • Data Quality: The quality of the data in the warehouse

must be adequate to satisfy users’ needs• Governance requires that people, committees, and

processes be in place. • Users: There are a large number of potential BI users,

including IT developers; front-line workers; analysts; information workers; managers and executives; and suppliers, customers, and regulators.

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3.5 KNOWLEDGE MANAGEMENT

• Knowledge management (KM) • Knowledge• Intellectual capital (or intellectual assets)

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KNOWLEDGE MANAGEMENT (CONTINUED)

• Explicit knowledge: objective, rational, technical knowledge that has been documented.– Examples: policies, procedural guides, reports, products,

strategies, goals, core competencies

• Tacit knowledge: cumulative store of subjective or experiential learning.– Examples: experiences, insights, expertise, know-how, trade

secrets, understanding, skill sets, and learning

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KNOWLEDGE MANAGEMENT (CONTINUED)

• Knowledge management systems (KMSs)• Best practices

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KNOWLEDGE MANAGEMENT SYSTEM CYCLE

• Create knowledge• Capture knowledge• Refine knowledge• Store knowledge• Manage knowledge• Disseminate knowledge

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KNOWLEDGE MANAGEMENT SYSTEM CYCLE

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CHAPTER CLOSING

• Organizations can use knowledge management to develop best practices, the most effective and efficient ways of doing things, and to make these practices readily available to a wide range of employees.

• The database approach minimizes the following problems: data redundancy, data isolation, data inconsistency, data security, data integrity, and data independence.

• Master data management provides companies with the ability to store, maintain, exchange, and synchronize a consistent, accurate, and timely “single version of the truth” for the company’s core master data.

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CopyrightCopyright © 2014 John Wiley & Sons Canada, Ltd. All rights reserved. Reproduction or translation of this work beyond that permitted by Access Copyright (the Canadian copyright licensing agency) is unlawful. Requests for further information should be addressed to the Permissions Department, John Wiley & Sons Canada, Ltd. The purchaser may make back-up copies for his or her own use only and not for distribution or resale. The author and the publisher assume no responsibility for errors, omissions, or damages caused by the use of these files or programs or from the use of the information contained herein.

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