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Tuesday, August 13, 2013

PPT On Data Management


Data Management Presentation Transcript:
1.Chapter Outline
Data Management: A Critical Success Factor
Data Warehousing
Information and Knowledge Discovery with Business Intelligence
Data Mining Concepts and Applications
Data Visualization Technologies
Web-Based Data Management Systems
Introduction to Knowledge Management
Information Technology in Knowledge Management

2.Learning Objectives
Recognize the importance of data, managerial issues, and life cycle .
Describe the sources of data and their collection
Describe document management systems.
Explain the operation of data warehousing and its role in decision support
Describe information and knowledge discovery and business intelligence
Understand the power and benefits of data mining.
Describe data presentation methods, and explain geographical information systems, visual simulations, and virtual reality as decision support tools
Recognize the role of the web in data management.
Define knowledge and describe the different types of knowledge.
Describe the technologies that can be utilized in a knowledge management system

3.Data Management
A critical success factor: IT applications cannot be done without using data. Data should be high-quality (accurate, complete, timely, consistent, accessible, relevant, and concise).
The Difficulties of managing Data:
The amount of data increases exponentially with time
 Data are scattered throughout  organization and are collected by many individuals using several methods and devices.
An  ever- increasing amount of  external data needs to be considered in making organizational decisions.
Data security, quality, and integrity are critical, yet  are easily jeopardized (put at risk). 

4.Critical Success Factors (CSF)
Those few things that must go right in to ensure an organization’s survival and success

5.Data Life Cycle

6.Data Sources
Internal Data Sources: data about people, products, services, and processes.
Personal Data: IS users or other corporate employees may document their own expertise by creating personal data.
External Data Sources: Data from commercial databases to sensors and satellites.

7.Characteristics of a Data Warehouse 
 Organization. Data are organized by subject  and contain information relevant for decision support only .
 Consistency. Data in different operational databases may be encoded differently . In the data warehouse, though, they will be coded in a consistent manner.
Time variant. The data are kept for many years so that  they can be used for trends, forecasting, and  comparisons over time.
Non-volatile. Data are not updated once entered into the warehouse.
 Multidimensional. Typically the data warehouse uses a multidimensional structure .
Web-based. Today’s data warehouse are designed to provide an efficient computing environment for web-based applications.

8.Building a Data Warehouse

9.Relational and Multidimensional Database
Relational databases store data in two – dimensional tables. Multidimensional databases typically store data in arrays, which consist of at least three business dimension.

10.Data Marts
Data Mart: A small data warehouse designed for a strategic business unit ( SBU) or a department
The advantage of data marts include:: low cost (Prices under $100,000 versus $1million or more for data warehouses); significantly shorter lead time for implementation (often less than 90 days), local rather than central control (conferring power on the using group), More rapid response and more easily understood and navigated than an enterprise wide data warehouse .

11.Information & Knowledge Discovery with Business Intelligence
Business Intelligence: A broad category of applications and techniques for gathering, storing, analyzing , and providing access to data to help enterprise users make better business and strategic decisions.

12.How Business Intelligence works?

13.The process of extracting knowledge from volumes of data; includes  data mining .

14.Data Mining Concepts Data mining: The process of searching for valuable business information in a large database, data warehouse, or data mart.
Data mining capabilities include:
     1) Automated prediction of trends and      behaviours, and
    2) Automated discovery of previously     unknown patterns.

15.Data Mining Application Retailing and sales
Manufacturing and production
Police work
Health care

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