1. Chapter 3
Data Management: Data,
Databases and Warehousing
1

2. Learning Objectives
 Recognize the importance of data, managerial
issues, and life cycle
 Describe sources of data, collection, and quality
 Describe DMS
 Describe Data Warehousing and Analytical
Processing
 Describe DBMS (benefits and issues)
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3. Learning Objectives (Continued)
 Understand conceptual, logical, and physical data
 Understand ERD
 The importance of Marketing
 The Internet and Data Management
3

4. Introduction
 Corporate data are key strategic assets.
 Managing data quality is vital to the organizations.
 Dirty data results in:
 Poor business decisions
 Poor customer service
 Inadequate product design
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5. Goal of data management
 The goal of DM is provide the infrastructure to
transform raw data into corporate information of
the highest quality.
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6. Building Blocks of DM
 Data profiling:
 Understanding the data
 Data quality management:
 Improving the quality of data
 Data integration:
 Combining similar data from multiple sources
 Data augmentation:
 Improving the value of the data
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7. Data Problems and Difficulties
 Exponential increase in the data volume with
time.
 Scattered data across organization collected and
stored through various methods and devices.
 Consideration of external data for decision
making
 Data security, quality and integrity.
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8. -cont…
 Selection of data management tools.
 Validity of the data.
 Maintenance of data to eradicate redundancy and
obsolete.
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9. Solution to Managing Data
 Organizing data in a hierarchical format in one
location.
 Supports secured and efficient high-volume
processing.
 RDBMS add to facilitate end-user computing and
decision support.
 Data-warehousing.
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10. Data Life Cycle Process
10 Chapter 3

11. Transactional vs.
Analytical Data Processing
 Transactional processing takes place in
operational systems (TPS) that provide the organization
with the capability to perform business transactions and
produce transaction reports.
The data are organized mainly in a hierarchical
structure and are centrally processed.
 This is done primarily for fast and efficient
processing of routine, repetitive data.
11 Chapter 3

12. -cont…
 Supplementary activity to transaction processing is
called analytical processing, which involves the
analysis of accumulated data.
 Analytical processing, sometimes referred to as
business intelligence, includes data mining, decision
support systems (DSS), querying, and other
analysis activities.
 These analyses place strategic information in the
hands of decision makers to enhance productivity
and make better decisions, leading to greater
competitive advantage.
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13. Data Sources
 Organizational Data: Organizational internal data
about people, products, services, processes,
equipment, machinery etc.
 End User Data: Data created by the IS users or
other corporate employees. They may include
facts, concepts, thoughts and opinions.
 External Data: Commercial databases, sensors,
satellites, government reports, secondary storage
devices, internet servers, etc..
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14. Methods for Collecting Raw Data
 Manually
 Surveys, observations, contributions from experts..
 Electronically
 H/W and S/W for data storage, communication,
transmission and presentation.
 Online surveys, online polls, data warehousing,
website profiling, data that is scanned/transmitted.
 CLICKSTREAM DATA: Data that can be collected
automatically using special software from the company’s web
site.
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15. Data Quality
 Data quality determines the data’s usefulness as
well as the quality of the decisions based on the
data.
 Data quality dimensions:
 Accuracy
 Accessibility
 Relevance
 Timeliness
 Completeness
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16. Categories of Data Quality
 Standardization (for consistency)
 Matching (of data if stored in different places)
 Verification (against the source)
 Enhancement (adding of data to increase its
usefulness)
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17. 3-Step Method for DM
 Analyzing the actual organizational processes
 Moving on to analyzing the entities that these
elements comprise
 Finish by analyzing the relationship between the
data in the business processes that the data must
support.
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18. ??? To Understand Business Flow
 What information is input to the process?
 What information is changed or created during
the process?
 What happens to the information once the
process is complete?
 What value-added information does the process
produce?
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19. Data Privacy, Cost and Ethics
 Collecting data raises the concern about privacy
protection.
 Data collected is about:
 Employees
 Customers
 Other people
 Accessible to authorized people only.
 Reasonable costs for collection, storage and use
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20. Document Management
 DM is the automated control of
 electronic documents,
 page images,
 spreadsheets,
 voice word processing documents, and
 other complex documents through their entire life
cycle within an organization.
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21. Benefits of DM
 Allows organization to exert greater control over
 production, storage and distribution of documents,
 yielding greater efficiency in the reuse of information ,
 the control of a documents through a workflow
process and
 the reduction of product cycle times.
 Deals with knowledge,data and information.
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22. Major Tools for DM
 Workflow software
 Authoring tools
 Scanners
 Databases
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23. DMSs
 Document Management Systems:
 provide decision makers with information in an
electronic format and usually include computerized
imaging systems that can result in substantial savings.
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24. Hierarchy of Data
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25. Hierarchy of Data (cont’d)
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26. The Data Warehouse & Data Management
26 Chapter 3

27. Marketing Databases in Action
 Introduction:
 Data warehouses and data marts serve end users
in all functional areas.
 Dramatic applications of DW and DM are seen in
Marketing Databases.
 Marketing today requires:
 New databases oriented towards targeting
personalizing marketing messages in real time.
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28. Marketing Transaction Databases
 MD provides effective means of capturing
information on customer preferences and needs.
 Enterprises, use this knowledge to create new
and/or personalized products and services.
 Combines many of the characteristics of current
databases and marketing data sources into a new
database that allows marketers to engage in real-
time personalization and target every interaction
with customers.
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29. Web-based Data Management Systems –
content and information
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30. Managerial Issues
Cost-benefit issues and justification
Where to store data physically
Legal issues
Internal or external?
Data Delivery
30 Chapter 3

31. Managerial Issues (Continued)
Disaster recovery
Data security and ethics
Ethics: Paying for use of data
Privacy
Legacy Data
31 Chapter 3