Data resource management involves applying information systems technology to manage data resources. It includes activities like creating, storing, organizing, and retrieving data using database management systems. There are different types of databases like operational, distributed, data warehouses, data marts, and end user databases. Data warehouses store historical data from various operational databases to help identify trends. Data mining techniques are used to better understand data through analysis, sorting, extracting patterns and relationships to gain insights. Common applications of data mining include banking, customer relationship management, targeted marketing, fraud detection, and scientific data analysis.

1. Data Resource Management

2. Data Resource Management
 A managerial activity
 Applies information systems technology to
managing data resources

3. What is a DataBase?
 Database – a collection of related data organized in
a way to facilitate data processing (ie searches)
 DBMS – Database Management Systems
 Use a DBMS software to create, store, organize, and
retrieve data from a single database or several databases
 Example: Microsoft Access

4. Foundation Data Concepts
 Levels of data
 Character
 Single alphabetical, numeric, or other symbol
 Field
 Groupings of characters
 Represents an attribute of some entity
 Records
 Related fields of data
 Collection of attributes that describe an entity
 Fixed-length or variable-length

5. Foundation Data Concepts (continued)
 Files (table)
 A group of related records
 Classified by
 Primary use
 Type of data
 permanence

6. Foundation Data Concepts (continued)
 Data Elements

7. Foundation Data Concepts (continued)
 Database (wrap up)
 Integrated collection of logically related data elements
 Consolidates records into a common pool of data
elements
 Data is independent of the application program using
them and type of storage device

8. Major Types of Databases External
Databases on
the Internet &
Online
Services
Client
PC or
Distributed
NC
Databases on
Intranets &
Network Operational
Databases of
Other Server the Organization
Networks
End User Data Data
Databases Warehouse Mart

9. Types of Databases
 Operational
 Supports business processes and operations
 Also called subject-area databases, transaction
databases, and production databases.
 These also include databases of Internet and
electronic commerce activity, such as click
stream data or data describing online behavior
of visitors at a company’s website.

10. Types of Databases (continued)
 Distributed
 Replicated and distributed copies or parts of
databases on network servers at a variety of sites.
 Done to improve database performance and security
 These are the databases of local workgroups and
departments at regional offices, branch offices, and other
work sites needed to complete the task at hand.

11. Data Warehouse Databases. These store data
from current and previous years that has been
extracted from the various operational and
management databases of the organization. As
a standardized and integrated central source of
data, warehouses can be used by managers for
pattern processing, where key factors and trends
about operations can be identified from the
historical record.

12.  Data Marts. Are subsets of the data included in
a Data Warehouse which focus on specific
aspects of a company, e.g. department,
business process, etc.
 End User Databases. These consist of a variety
of data files developed by end users at their
workstations. For example, an end user in sales
might combine information on a customer’s
order history with her own notes and
impressions from face-to-face meetings to
improve follow-up.

13.  External Databases. Many organizations make
use of privately generated and owned online
databases or data banks that specialize in a
particular area of interest. Access is usually
through a subscription for continuing links or a
one-time fee for a specific piece of information
(like the results of a single search). Other
sources like those found on the Web are free.

14. Data Warehouse and Data Mining
Client
Operational PC or
Analytical NC
Databases
Data Data Store
Management Enterprise
Subsystem Warehouse
Data Data Mart
Acquisition
Data Access
Data Access
Subsystem and Delivery
and Delivery
Metadata Subsystem
Subsystem
Metadata Directory
Management
Warehouse Subsystem Metadata
Design Repository Web
Web
Subsystem Information
Information
System
System

15. Data Warehouses and Data Mining
 Data warehouse
 Stores data extracted from operational, external,
or other databases of an organization
 Central source of “structured” data
 May be subdivided into data marts

16. How Organizations Get the Most
from Their Data
 Data Mining
 A method for better understanding data
 Information on customers, products, markets, etc.
 Drill down: from summary to more detailed data
 Sort and extract information
 Trends, correlations, forecasting, statistics

17. How Organizations Get the
Most from Their Data
 Data Mining
 Online Transaction Processing (OLTP)
 Immediate automated responses to user requests
 Multiple concurrent transactions
 A big part of interactive Internet e-commerce

18. How Organizations Get the
Most from Their Data
 Data Mining
 Online Analytical Processing (OLAP)
 Graphical software tools that provide complex analysis
of data stored in a database
 Drills down to deeper levels of consolidation
 Time series and trend analysis
 “What if” and “why” questions

19. Applications
 Banking: loan/credit card approval
 predict good customers based on old customers
 Customer relationship management:
 identify those who are likely to leave for a competitor.
 Targeted marketing:
 identify likely responders to promotions
 Fraud detection: telecommunications, financial
transactions
 from an online stream of event identify fraudulent events
 Manufacturing and production:
 automatically adjust knobs when process parameter changes

20. Applications (continued)
 Medicine: disease outcome, effectiveness of
treatments
 analyze patient disease history: find relationship between
diseases
 Molecular/Pharmaceutical: identify new drugs
 Scientific data analysis:
 identify new galaxies by searching for sub clusters
 Web site/store design and promotion:
 find affinity of visitor to pages and modify layout

21. Data Mining in Use
 Basketball teams use it to track game strategy
 Cross Selling
 Target Marketing
 Holding on to Good Customers
 Weeding out Bad Customers