Data Resource Management

1. Data Resource
Management
James A. O'Brien, and George Marakas. Management Information Systems with MISource 2007, 8th
ed.
Boston, MA: McGraw-Hill, Inc., 2007. ISBN: 13 9780073323091
Management
Information System
By
Rao Majid Shamshad
Faculty, DoMS, University of Okara

2. Case 1 Sharing Business Databases
•Amazon’s data vault
•Product descriptions
•Prices
•Sales rankings
•Customer reviews
•Inventory figures
•Countless other layers of content
•Took 10 years and a billion dollars to build
Chapter 5 Data Resource ManagementChapter 5 2

3. Case 1 Sharing Business
Databases
•Amazon opened its data vault in 2002
• 65,000 developers, businesses, and entrepreneurs have
tapped into it
• Many have become ambitious business partners
•eBay opened its $3 billion databases in 2003
• 15,000 developers and others have registered
to use it and to access software features
• 1,000 new applications have appeared
• 41 percent of eBay’s listings are uploaded to
the site using these resources
Chapter 5 Data Resource ManagementChapter 5 3

4. Case 1 Sharing Business
Databases
•Google recently unlocked access to its
desktop and paid-search products
•Dozens of Google-driven services
cropped up
•Developers can grab 1,000 search
results a day for free; anything more
requires permission
•In 2005, the Ad-Words paid-search
service
was opened to outside applicationsChapter 5 Data Resource ManagementChapter 5 4

5. Case Study Questions
•What are the business benefits to Amazon and
eBay of opening up some of their databases to
developers and entrepreneurs?
• Do you agree with this strategy?
•What business factors are causing Google to
move slowly in opening up its databases?
• Do you agree with its go-slow strategy?
•Should other companies follow Amazon and
eBay’s lead and open up some of their databases
to developers and others?
• Defend your position with an example of the risks and
benefits to an actual company
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6. Logical Data Elements
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7. Logical Data Elements
• Character
• A single alphabetic, numeric, or other symbol
• Field or data item
• Represents an attribute (characteristic or quality)
of some entity (object, person, place, event)
• Example: salary, job title
• Record
• Grouping of all the fields used to describe the attributes of an
entity
• Example: payroll record with name, SSN, pay rate
• File or table
• A group of related records
• Database
• An integrated collection of logically related
data elements
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8. Electric Utility Database
Chapter 5 Data Resource ManagementChapter 5 8

9. Database Development
•Database Administrator (DBA)
•In charge of enterprise database
development
•Improves the integrity and security of
organizational databases
•Uses Data Definition Language (DDL) to
develop and specify data contents,
relationships, and structure
•Stores these specifications in a data
dictionary or a metadata repository
Chapter 5 Data Resource ManagementChapter 5 9

10. Data Dictionary
•A data dictionary
•Contains data about data (metadata)
•Relies on specialized software component to
manage a database of data definitions
•It contains information on..
•The names and descriptions of all types of data
records and their interrelationships
•Requirements for end users’ access and use of
application programs
•Database maintenance
•Security
Chapter 5 Data Resource ManagementChapter 5 10

11. Data Resource Management
• Data resource management is a managerial activity
• Uses data management, data warehousing,
and other IS technologies
• Manages data resources to meet the information
needs of business stakeholders
• Data stewards
• Dedicated to establishing and maintaining the
quality of data
• Need business, technology, and diplomatic skills
• Focus on data content
• Judgment is a big part of the job
Chapter 5 Data Resource ManagementChapter 5 11

12. Case Study Questions
•Why is the role of a data steward
considered to be innovative?
•What are the business benefits
associated with the data steward
program at Emerson?
•How does effective data resource
management contribute to the strategic
goals of an organization?
Chapter 5 Data Resource ManagementChapter 5 12

13. Types of Databases
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14. Operational Databases
•Stores detailed data needed to support
business processes and operations
•Also called subject area databases (SADB),
transaction databases, and production
databases
•Database examples: customer, human
resource, inventory
Chapter 5 Data Resource ManagementChapter 5 14

15. Distributed Databases
• Distributed databases are copies or parts of databases stored on servers at multiple
locations
• Improves database performance at worksites
• Advantages
• Protection of valuable data
• Data can be distributed into smaller databases
• Each location has control of its local data
• All locations can access any data, any where
• Disadvantages
• Maintaining data accuracy
• Replication
• Look at each distributed database and find changes
• Apply changes to each distributed database
• Very complex
• Duplication
• One database is master
• Duplicate the master after hours, in all locations
• Easier to accomplish
Chapter 5 Data Resource ManagementChapter 5 15

16. External Databases
•Databases available for a fee from
commercial online services, or free from
the Web
•Example: hypermedia databases,
statistical databases, bibliographic and
full text databases
•Search engines like Google or Yahoo
are
external databases
Chapter 5 Data Resource ManagementChapter 5 16

17. Hypermedia Databases
•A hypermedia database contains
•Hyperlinked pages of multimedia
•Interrelated hypermedia page
elements, rather than interrelated
data records
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18. Components of Web-Based
System
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19. Data Warehouses
•Stores static data that has been extracted
from other databases in an organization
•Central source of data that has been
cleaned, transformed, and cataloged
•Data is used for data mining, analytical
processing, analysis, research, decision
support
•Data warehouses may be divided into data
marts
•Subsets of data that focus on specific
aspects
of a company (department or business
Chapter 5 Data Resource ManagementChapter 5 19

20. Data Warehouse
Components
Chapter 5 Data Resource ManagementChapter 5 20

21. Applications and Data Marts
Chapter 5 Data Resource ManagementChapter 5 21

22. Data Mining
•Data in data warehouses are analyzed to reveal
hidden patterns and trends
•Market-basket analysis to identify new
product bundles
•Find root cause of qualify or manufacturing
problems
•Prevent customer attrition
•Acquire new customers
•Cross-sell to existing customers
•Profile customers with more accuracy
Chapter 5 Data Resource ManagementChapter 5 22

23. Traditional File Processing
•Data are organized, stored, and processed
in independent files
•Each business application designed to
use specialized data files containing
specific
types of data records
•Problems
•Data redundancy
•Lack of data integration
•Data dependence (files, storage devices,
software) Chapter 5 Data Resource ManagementChapter 5 23

24. Traditional File Processing
Chapter 5 Data Resource ManagementChapter 5 24

25. Database Management
Approach
•The foundation of modern methods of
managing organizational data
•Consolidates data records formerly in
separate files into databases
•Data can be accessed by many different
application programs
•A database management system (DBMS)
is the software interface between users
and databases
Chapter 5 Data Resource ManagementChapter 5 25

26. Database Management
Approach
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27. Database Management
System
•In mainframe and server computer
systems, a software package that is used
to…
•Create new databases and database
applications
•Maintain the quality of the data in an
organization’s databases
•Use the databases of an organization
to provide the information needed by
end users
Chapter 5 Data Resource ManagementChapter 5 27

28. DBMS Major Functions
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29. Database Interrogation
•End users use a DBMS query feature or
report generator
•Response is video display or printed
report
•No programming is required
•Query language
•Immediate response to ad hoc data
requests
•Report generator
•Quickly specify a format for information
you want to present as a report
Chapter 5 Data Resource ManagementChapter 5 29

30. Database Maintenance
•Accomplished by transaction
processing systems and other
applications, with the support of the
DBMS
•Done to reflect new business
transactions and other events
•Updating and correcting data, such
as customer addresses
Chapter 5 Data Resource ManagementChapter 5 30