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
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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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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.
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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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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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30. Managerial Issues
Cost-benefit issues and justification
Where to store data physically
Legal issues
Internal or external?
Data Delivery
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31. Managerial Issues (Continued)
Disaster recovery
Data security and ethics
Ethics: Paying for use of data
Privacy
Legacy Data
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