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Turban and Volonino
Chapter 3 Managing Data to Improve Business Performance Information Technology for Management Improving Performance in the Digital Economy 7th edition John Wiley & Sons, Inc. Slides contributed by Dr. Sandra Reid Chair, Graduate School of Business & Professor, Technology Dallas Baptist University
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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
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Learning Objectives
Describe how data and document management impact profits and performance.
Understand how managers are supported or constrained by data quality.
Discuss the functions of databases and database management systems.
Understand how logical views of data provide a customized support and improve data security.
Describe the tactical and strategic benefits of data warehouses, data marts, and data centers.
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4. Learning Objectives cont’d
6. Describe transaction and analytic processing systems.
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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5. Applebee’s International Learns & Earns
Problem: Huge quantities of data in many
Databases.
Solution: Enterprise data warehouse implemented.
Results: Improved profitability.
Click Applebee’s image for link to company historic chronology.
Click Teradata for link (solution implemented by Applebee’s)
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6. Applebee’s enterprise data warehouse and feedback loop
Figure illustrates data stages at Applebee’s enterprise: data are collected, processed & stored in data warehouse; processed by
analytical tools such as data mining & decision modeling; knowledge acquired from data analysis directs promotional & other decisions.
Management can receive feedback regarding success of management strategies.
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3.1 Data, Master Data, and Document Management
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Data life cycle
Diminishing data value
Need for (near) real-time operational data (90/90 data use)
Data in context
Informational slide.
Life cycle identifies way data travel through an organization from their capture or creation to their use in supporting
data-driven solutions such as SCM, CRM & EC.
3 general data principles illustrate the importance of the data life cycle perspective & guide IT investment decisions: 1. diminishing data value; 2. principle of 90/90 data use; 3. principle of data in context.
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Data Visualization
Data visualization to format data into meaningful contexts for users. Means to present data in ways that are easier, faster for users to understand
thereby increasing productivity, efficiency, & effectiveness. The table provides more precise data whereas the graph takes much less time &
effort to understand.
Dartmouth University’s development department realized their efforts to target alumnae for contributions to its capital campaign were not as
effective as they could be. Invested in data visualization tools which helped them to know where & when to invest their time to maximize
return on that time.
Dow Jones industrial average (DJIA) for a single day in tabular display and graphical display.
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10. Model of an enterprise data warehouse
E T L = extract, transform, load. Transforming data into information & then to knowledge.
(Source: From Syncsort, synchsort.com. Used with permission.)
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Data Quality
Accuracy
Accessibility
Relevance
Timeliness
Completeness
Who is responsible for data quality?
Data ownership
Who is responsible for data security?
Last few slides for discussion – How does data mining provide intelligence to decision makers? Establishes customers’ patterns for better
decision making. See A Closer Look, 3.3.
What are the 2 types of data mining systems, and how do they provide value to defense organizations?
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Is it possible to eliminate errors? NO….but IT gets us much further toward that goal.
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13. Document Management
Document Management Systems manage and archive electronic documents (and convert paper ones) and then index and store them.
Query and search capabilities
Workflow systems, authoring tools databases
Paperless office, green IT
Business continuity, security, knowledge sharing, collaboration, remote access

14. Hierarchy of data for a computer-based file
Informational slide. Figure illustrates primary & foreign keys.
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Problems: redundancy, inconsistency, data isolation, integrity, concurrency
Data management problems arising from the file environment approach led to the development of databases & database management systems (DBMS).
Why?
Data redundancy – same data duplicated in several files creating silos.
Data inconsistency – data values are not synchronized across various copies of the data such as problems faced by Applebee’s & Dartmouth.
Data isolation – file organization creates silos of data that make it extremely difficult to access data from different applications.
Data security – difficult because new applications are added on an ad hoc basis. Applications increase as the number of people who can
access the data.
Lack of data integrity – much harder to enforce the rules.
Data concurrency – accessing & recording of data may be going on at the exact same time.
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Centralized database
Distributed database with complete or partial copies of the central database in more than one location.
Centralized database stores all related files in one physical location. Why is this negative? Natural & unnatural disasters could be catastrophic.
Redundancy to avoid disasters is extremely expensive.
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Database management system provides access to all data in the database.
DBMS acts as an interface between application programs & physical data files. It provides users with tools to add, delete, maintain,
display, print, search, select, sort & update data.
Companies use DBMS in a broad range of information systems. Microsoft Access, Oracle 11g, & DB2 are examples.
DBMS provides 2 views of data: physical view & logical view.
Major data functions performed by DBMS are:
Data filtering & profiling.
Data quality.
Data synchronization.
Data enrichment.
Data maintenance.
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DBMS answers backup, integrity, security, silos, inconsistency & replication issues. Discuss students’ experiences.
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19. Data warehouse framework and views
Assign students to find examples of organizations for each of the operational systems data modules & describe it for the others.
This figure diagrams the process of building & using a data warehouse.
All share these 9 major characteristics: organization, consistency, time variant, nonvolatile, relational, client/server, web-based, integration,
& real-time.
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20. Teradata Corp.’s enterprise data warehouse.
Centralized approach reduces the amount of data technical team has to transfer, thereby simplifying data management & administration.
Users are provided with access to all data in the data warehouse instead of being limited to individual data marts.
(Source: Teradata Corporation [teradata.com], with permission.)
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Students should expand, or at least give examples of organizations in each industry who have apparently utilized effectively.
American Airlines; Talbots; AT&T to begin.
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Task students to come with counters for each reason.
A few examples:
Clearly communicate & involve users – avoids unrealistic expectations.
Outsource when not core competency – avoids lack of development expertise.
Secure top management support & have champion – avoids lack of effective project sponsorship.
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3.5 Enterprise Content Management
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Electronic records management from creation to retention or destruction
Shows differences between documents & records as well as relationship between document management & records management.
Records are different from documents in that they cannot be modified or deleted except in controlled circumstances. Documents
are generally subject to revision.
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Managerial Issues
Reducing uncertainty.
Cost-benefit issues & justification.
Where to store data physically.
Legal issues.
Internal or external collection, storage, maintenance, & purging of databases of information?
Disaster recovery.
Data security & ethics.
Privacy.
Legacy systems.
Data delivery.
There are always managerial issues in every organization and at every level. Legacy systems, privacy, disaster recovery, legal issues, etc., are all
issues that will not go away possibly for generations to come.
Perhaps reducing uncertainty is the one that overarches all the others.
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