1. Managing Corporate Information Resources
Chapter 7
Information Systems Management in Practice
Dr. Leida Chen
Creighton University

2. Introduction IS continually manages new forms of information resources
Corporate databases
Documents (electronic or paper) and Web content
Knowledge management
Data—Information—Knowledge

3. Four Types of Information

4. Records versus Documents

5. Content Management
To create a content management strategy, companies need to understand the three phases of content
Managing content creation and acquisition
Content administration and safeguarding
Content deployment and presentation

6. Content Management
Source: Adapted from Chuck, Tueber, Dealing in Web Currency, Gartner EXP,
56 Top Gallant, Stamford, CT, June 2001.

7. Content Management cont’d
Managing content creation and acquisition
Focus on creating content quality
Content can be purchased from external specialists
syndicated content
Decentralized organizational structure for content creation and maintenance to content-expert employees is best approach
Central direction for dispersed experts however required
Standardized organization-wide formats to integrate content into overall workflow also needed

8. Content Management cont’d
Content Administration and Safeguarding
Efficiency is key in this phase
Content management tools can be used to identify types of content and the business rules that apply to each type
Content administration should be centralized
Organizational workflow
How content fits into overall organization process
Content approval
e.g., foreign language translation

9. Content Management cont’d
Content Deployment and Presentation
Effectiveness is the emphasis in this phase— maximize effect of Web content presentation
Attract visitors and gain desired outcomes
Easy site navigation, pertinent and accurate information
This phase is crucial to success of e-commerce effort and so Web site design should start here
Most Web sites today require certain features to attract and retain visitors
Personalization
Localization

10. Managing Data Data management
Structured way data is represented, stored and retrieved for use.
Database management systems (DBMS) are the main tool for managing corporate data
Two principles of DBMS
Three-level conceptual model
Several alternative data models for organizing the data

11. The Three-Level Database Model
Level 1: External, conceptual or local level
Various “user views” of corporate data
Each application program has own view
Level 2: Logical or enterprise data level
“Technical” (human) view of all corporate data
Controlled by database administrators
Level 3: Physical or storage level
Specifies the way data is physically stored
For use by database administrators

12. The Three-Level Database Model cont’d

13. Four Data Models
Alternative ways of defining relationships among types of data
Hierarchical Model
Structures data so that each element is subordinate to another in a distinct hierarchical manner
e.g., Parent-child relationship

14. Four Data Models cont’d
Network Model
Allows each data item to have more than one parent
Relationships stated by pointers stored with the data
Relational Model
Data stored in tables (intuitive for users)
“Key” uniquely defines each record
“Tuple-calculus” operations
Commands such as Select, Join, Match, etc.
Data normalization (optimization)
Not as efficient as hierarchical but more flexible
Database technology of choice (e.g., SQL, MS Access)

15. Four Data Models cont’d
Object-Oriented Database Model
Extension of concept of data to objects:
Piece of data
Methods that can perform work on data
Attributes describing the data
Relationships between objects
Objects can be used to store any type of data
e.g., spreadsheet, video clip, photograph, music segment
Object database

16. Corporate Data Management Challenges
Inconsistent Data Definitions
Proliferation of heterogeneous database
Data availability, integrity and security
Availability
How to get the terabytes of data at the right place and right time?
Integrity
Data should be entered correctly once in the system
Security
Data protection is a top concern in organizations today
Turning data into information and knowledge

17. Data and Database Administration *
Data administration
Data policies, procedures, and standards
Planning
Data conflict resolution
Managing the information repository
Internal marketing

18. Data and Database Administration *
Selecting DBMS and related software tools
Installing and upgrading the DBMS
Tuning database performance
Improving database query processing performance
Managing data security, privacy, and integrity
Data backup and recovery

19. Managing Information
Once enterprises get their data into shape, that data can more easily be turned into information
“Information is power”
Managing information is a critical activity
Technology = infrastructure
Asset = information that runs on that infrastructure

20. Value Creation with Customer Data *
Analysis of the value proposition
Theoretical repurchase frequency
The regularity with which the average customer acquires goods and services offered by the firms in the industry or segment.
Degree of customizability
The extent to which the product or service your firm offers can be tailored to the specific needs and requirements of individual customers or a segment of the customer base.

21. Customer Data Strategies *
Rewards Strategy
Personalization
Low Payoff
Acquisition Strategy
Theoretical repurchase frequency
Degree of customizability

22. Customer Data Strategies *
Personalization Strategy
High theoretical repurchase frequency and a high degree of customizability
Operations and differentiation
Rewards Strategy
High theoretical repurchase frequency and standardized product and service
Loyalty rewards
Reporting
Acquisition Strategy
Low theoretical repurchase frequency and a high degree of customizability
Analytics and new prospects
Low payoff

23. Data Warehouses Data warehouse: Stores data used to make decisions
Obtained periodically from transaction databases
Snapshot of situation at specific time
Different from operational databases
Customer data is most common type of data housed
Corporate use transcends reporting internal data to business intelligence today

24. Data Warehouse * Characteristics Need for data warehousing
Subject-oriented
Integrated
Time-variant
Nonupdatable
Need for data warehousing
A business requires an integrated, company-wide view of high-quality information.
The IS department must separate informational from operational systems.

25. Star Schema * Fact Dimension
PRODUCT
Product_Code
Color
Size
Fact
Dimension
SALES
Product_Code
Period_Code
Store_Code
Units_Sold
Dollars_Sold
Dollars_Cost
STORE
Store_Code
Store_Name
City
PERIOD
Period_Code
Year
Quarter
Fact table contains factual or quantitative data about a business.
Dimension table holds descriptive data (context) about the subject of the business

26. Size of the Data Warehouse *
Size of the fact table
Total number of stores = 1,000
Total number of products = 10,000
Total number of periods = 24 (2yrs’ worth of monthly data)
Total rows = 1,000 * 5,000 (active products) * 24 = 120,000,000 rows
Total size = 120,000,000 * 6 fields * 4 bytes (average size per field) = 2,880,000,000 bytes
What if the grain of the table is changed to daily sales?

27. Data Warehouses cont’d
Metadata
The part of the warehouse that defines the data
Explains meaning of each data element and sets standards
e.g. to reconcile data from legacy systems
Quality data
The cleaning process to adhere to metadata standards
The older the data the more suspect its quality
Datamart
A subset of data pulled off the warehouse for a specific group of users
Faster search time and lower costs

28. Data Warehouses cont’d
Data warehousing projects still fail about 40% of the time.
Five steps in a data warehousing project:
Define the business uses of the data
Create the data model for the warehouse
Cleanse the data
Select the user tools
Monitor usage and system performance

29. Data Mining * Goals of data mining Outcomes Explanatory Confirmatory
Exploratory
Outcomes
Association
Cluster
Classification
Sequence
Similar time sequence
Exception
Forecast

30. Data Mining Applications *
Profiling populations
Analysis of business trends
Target marketing
Usage analysis
Campaign effectiveness
Product affinity
Customer retention and churn
Profitability analysis
Customer value analysis
Up-selling

31. Conclusion
IS job of managing information resources is widening significantly
Get corporate data in shape
Create and build infrastructure to manage full spectrum of information types
Mine information from data
Internet provides an easily accessible place to store and retrieve information ad hoc.