30/08/2010 1 Chapter 3 Managing Data to Improve Business Performance Information Technology for Management Improving Performance in the Digital Economy 3-1 Copyright 2010 John Wiley & Sons, Inc. Chapter Outline • 3.1 Data, Master Data, and Data Management • 3.2 File Management Systems • 3.3 Database Management Systems • 3.4 Data Warehouses, Data Marts, and Data Centers • 3.5 Enterprise Content Management • 3.6 Managerial Issues 3-2 Copyright 2010 John Wiley & Sons, Inc.
Transcript
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Chapter 3
Managing Data to Improve Business Performance
Information Technology for ManagementImproving Performance in the Digital Economy
3-1Copyright 2010 John Wiley & Sons, Inc.
Chapter Outline
• 3.1 Data, Master Data, and Data Management• 3.2 File Management Systems• 3.3 Database Management Systems• 3.4 Data Warehouses, Data Marts, and Data
1.Describe how data and document management impact profits and performance.
2.Understand how managers are supported or constrained by data quality.
3.Discuss the functions of databases and database management systems.
4.Understand how logical views of data provide a customized support and improve data security.
5.Describe the tactical and strategic benefits of data warehouses, data marts, and data centers.
3-3Copyright 2010 John Wiley & Sons, Inc.
Learning Objectives cont’d
6. Describe transaction and analytic processing systems.1.Explain how enterprise content management and
electronic records management reduce cost, support business operations, and help companies meet their regulatory and legal requirements.
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Figure IT 7eU The Business Performance Management Cycle and IT Model
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Applebee’s International Learns & Earns
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Problem: Huge quantities of data in many databases.
Solution: Enterprise data warehouse implemented.
Results: Improved profitability.
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Figure 3.1 Applebee’s enterprise data warehouse and feedback loop.
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3.1 Data, Master Data, and Document Management
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Importance of data management
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• Helps companies to improve productivity. Ensuring that people can find what they need without a long and difficult search.
• Transforms raw data into usable corporate information.
• Opportunity to earn revenues.
• Ability to cut expenses.
Uncertainty: A Constraint on Managers
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Examples of damages due to data analysis failures:
• TransAlta: data error cost the firm US$24M
• Healthcare industry: increased costs by billions ofand maybe of thousands of lives.
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Data Management
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Data Management:
Structured approach for capturing, storing, processing, integrating, distributing and archiving
data effectively throughout their life cycle
Life cycle:
The way data travels through an organization from their capture or creation to their use and final
disposal
Figure 3.2 Data life cycle.
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Data Management
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• Principle of diminishing data value: value of data diminishes as the data ages.
• Principle of 90/90 data use: as high as 90 percent of data is seldom accessed after 90 days.
• Principle of data in context: end users need to see data in a meaningful format and context.
Data Visualization
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Dow Jones industrial average (DJIA) for a single day in tabular display and graphical display.
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Data Management: Problems and Challenges
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Data management is a major challenge because:
• Volume of data increases exponentially, only a small amount of data is relevant
• Data is collected and created by many individuals using different methods and devices
• Data security, quality and integrity are critical
MDM
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Master Data Management:
Process whereby companies integrate data from various sources or enterprise applications to provide
a more unified view of it
MDM consolidates data into a master reference file
Demystifying Master Data Management
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MDM
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Data entity:
Anything real or abstract about which a company wants to collect and store data
• Main entities: customers, products, suppliers,employees and assets
MDM
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Benefits of a unified view of customers:
• Better, more accurate customer data
• Ensure that all employees have reliable information on customers
• More responsive business decisions
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Figure 3.4. Model of an enterprise data warehouse.
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(Source: From Syncsort, synchsort.com. Used with permission.)
Data quality and integrity
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Data quality:
Measure of the data's usefulness as well as the quality of the decisions based on it.
• Five dimensions: accuracy, accessibility, relevance, timeliness and completeness
Captaris® Alchemy®: Document Management Software
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Table 3.2
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3.2 File Management Systems
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Document Management
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•Companies create business records
•Document management:
Automated control of these records from initial creation to final archiving or destruction
•DMS:
Software + Hardware that manages documents.
PAPER DOCUMENTS E -DOCS
Document Management
• Medical records
• Customer satisfaction service
• Emergency services
• Legal cases
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Figure 3.6Hierarchy of data for a computer-based
file.
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Limitations of data file environment• In the past the applications functioned
independently of one another.• Problems:
o Redundancyo Inconsistencyo Isolationo Securityo Integrity
o Concurrency
• In the present: DBMS
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Figure 3.8
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Computer-based files of this type cause problems such as redundancy,inconsistency, and data isolation.
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3.3 Databases and Database Management Systems (DBMS)
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Databases:
Optimal way to store and access organizational data
OTLP:Online transaction processing
DBMS (Database Management System):
Permits an organization to centralize data, manage it efficiently, and provide access to the stored data by application programs.
Databases
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Figure 3.9 (a) Centralized database. (b) Distributed database with complete or partial copies of the central database in more than one location.
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Table 3.3
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Figure 3.10
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Database management system provides access to all data in the database.
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3.4 Data Warehouses, Data Marts, and Data Centers
Data warehouse:
Is a regular database. The main difference is that data warehouse is designed and optimized to respond to analysis
questions that are critical for a business.
OLAP:
Online analytical processing
Data Warehouse
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Figure 3.11Data warehouse framework and views.
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Figure 3.12Teradata Corp.’s enterprise data
warehouse.
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(Source: Teradata Corporation [teradata.com], with permission.)
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Table 3.4
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Table 3.5
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Data Mart:
Lower-cost, scaled-down version of data warehouse
Data Center:
Facilities containing mission-critical information and components that deliver data and IT services to the
enterprise.
Data Mart and Data Center
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3.5 Enterprise Content Management
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ECM
• Enterprise Content Management:
Includes electronic document, web content, digital asset and electronic records management (ERM)
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ERM
• Creating backups is not a form of ERM, content must be organized so it can be easily retrieved by the enterprise or the government
• IT personnel needs to establish ERM policies: retaining and destroying records
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Figure 3.13
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Electronic records management from creation to retention or destruction.
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3.6 Managerial Issues
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Managerial Issues
1. Reducing uncertainty.2. Cost-benefit issues & justification.3. Where to store data physically.4. Legal issues.5. Internal or external collection, storage,
maintenance, & purging of databases of information.
National Security depends on Intelligence and Data Mining
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More information
Data management's top eight stories of 2008
Operational vs. Analytical MDM
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