3 data mgmt

Chapter 3

Data Management: Data,
Databases and Warehousing

1

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)

2

Learning Objectives (Continued)

 Understand conceptual, logical, and physical data
 Understand ERD
 The importance of Marketing
 The Internet and Data Management

3

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

4

Goal of data management
 The goal of DM is provide the infrastructure to
transform raw data into corporate information of
the highest quality.

5 Chapter 3

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

6 Chapter 3

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.

7

-cont…
 Selection of data management tools.
 Validity of the data.
 Maintenance of data to eradicate redundancy and
obsolete.

8

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.

9

Data Life Cycle Process

10 Chapter 3

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

-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.

12

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..

13

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.

14

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

15

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)

16

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.

17

??? 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?

18

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

19

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.

20

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.

21

Major Tools for DM
 Workflow software
 Authoring tools
 Scanners
 Databases

22

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.

23

Hierarchy of Data

24 Chapter 3

Hierarchy of Data (cont’d)

25 Chapter 3

The Data Warehouse & Data Management

26 Chapter 3

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.

27

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.
28

Web-based Data Management Systems –
content and information

29 Chapter 3

Managerial Issues

Cost-benefit issues and justification

Where to store data physically

Legal issues

Internal or external?

Data Delivery

30 Chapter 3

Managerial Issues (Continued)
Disaster recovery

Data security and ethics

Ethics: Paying for use of data

Privacy

Legacy Data

31 Chapter 3

3 data mgmt

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