This chapter discusses key concepts in data management including data sources, collection and quality; data warehousing; database management systems; and the data life cycle. It describes how data moves from raw to processed to analytical stages and how tools like data profiling, quality management, and integration improve data infrastructure. Finally, it addresses managerial issues around data storage, delivery, security, and ethics.

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

25. Hierarchy of Data (cont’d)
25 Chapter 3

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
29 Chapter 3

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