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INTRODUCTION :

·                      DATA MINING

·                      DATA WAREHOUSING

ABOUT

·                                     Design issues for Data Warehousing

·                                     Analysis process of Data warehousing

  • Standard reports and Quarries
  • Tool to be used against the Data Warehousing

·                                     Data Conversion Process

·                                    Process of Data Warehouse

o   Practical Best Practices for Data Warehousing

·                                     Application of Data Warehouse

 

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DATA MINING

By this point in time, you've probably heard a good deal about data mining -- the database industry's latest buzzword.  What's this trend all about?  To use a simple analogy, it's finding the proverbial needle in the haystack.  In this case, the needle is that single piece of intelligence your business needs and the haystack is the large data warehouse you've built up over a long period of time.

Through the use of automated statistical analysis (or "data mining") techniques, businesses are discovering new trends and patterns of behavior that previously went unnoticed.  Once they've uncovered this vital intelligence, it can be used in a predictive manner for a variety of applications. 

DATA WAREHOUSING

 

Executives today expect, and often receive, the good, timely information they need. To make inform decisions and lead their companies into the next decade. The wasn’t always the case.

 

Data warehousing as quickly evolved into a unique and popular business already consider their system to be key components of their It strategy and architecture deployed for businesses of all sizes and all types. Hardware vendors have quickly developed modules and services that specifically target the data warehousing market. This paper will introduce key concepts surrounding the data warehousing system.

 

What is Data Warehousing? Simply defined, a Data Warehousing is a collection of data designed to support managements decision making Data warehousing contain a wide variety of data that present a coherent picture of business conditions at a single point in time Typically a Data Warehousing is housed on enterprise mainframe server. It is a central repository for all or a significant part of all data that on enterprise’s numerous business systems collects.

 

Design issues for Data Warehousing

 

Discussion of building a Data Warehousing a Data Warehousing is a repository (or archive) of INFO gathered from multiple sources stored, under a unified scheme, at a single site. Once gathered, data are stored for a long time permitting excess to historical data.

 

Analysis process of Data warehousing

 

Analysis process of data ware houses range from the most basic (Query & Reporting to the more complex (Statically analysis) to the most complex (Data mining)

 

ç    Standard reports and Quarries

 

Many users of the Data Ware Houses need to access set of the standard reports and quarries. It is desirable to periodically automatically produce a setup standard reports that are required by many different users. When these users need a particular report, they can just view the report that has already been run by the Data Warehousing system rather then running bye particularly useful for reports that take a long time to run. Such a facility would require report server software.

 

 

 

ç    Tool to be used against the Data Warehousing

 

One of the objectives of the data warehousing is to make it as flexible and as open as possible. It is not desirable to set a steep entry price in terms of software and training for using the Data warehouse. The Data Warehouse should be accessible by as many end-users tools and platforms as possible. Yet it is not possible to make every feature of the Data Warehouse available from every end user too.

.

 

DATA CONVERSION PROCESS

 

The data conversion process for a data warehousing is complex, time consuming and unglamorous. It is also the very root of a good functional “data” is the operational operative word in “Data Warehouse” Quality data conversion are important to the Data Warehouse because the warehouse holds the information that is by to a corporation decision making process. For a corporation to obtained the ultimate goals and promises of data warehouse, It must understand that how critical the data conversion process is. Who involved before beginning the conversion process the Data Warehousing team completes the design and physical data model for the data warehouse and generates the toyed schemes.

 

Process of Data Warehouse

 

Data warehousing describes the process of defining populating and using a data warehouse. Data Warehouses emphasis the capture of data from diverse sources for useful analysis and access. Data from various transaction processing application and other sources is selectively extracted and organized on the data warehouse Database for use by analytical applications and user quarries.

 

 

ç    Practical Best Practices for Data Warehousing

 

Data Warehousing is an on going process not something that it built once and left to run. Some best practices for Data Warehousing consist of the following.

 

Build organizational commitment while managing user expectation.

A dynamic model should consider the following-

  • Business Goals / Objectives.
  • Business Areas / Functions.
  • Improvement opportunity.
  • Knowledge / Information.
  • Data
  • Structured agenda frames
  • Improved participants.
  • Trained objective 3rd party facilitations.
  • Make smart technology investments that are driven by business needs Businesses face concerns with
  • Data Source Challenges
  • End User Requirement
  • Deployment Maintenance Issues

 

Application of Data Warehouse

 

Some of the activity against today data warehousing is pre determined and not much different from traditional analysis activity. Other process such as multi dimension analysis and visualization where not available with traditional analysis tools and method. Some of the users of Data Warehouses are as under.

 

  1. Data Warehouses are based for customer relationship. Management System because they can be used for consolidator customers data and identifying areas customer satisfaction and frustration.
  2. Warehouse are also used for dictation, product repositioning analysis profit center discovery and cooperate assets management for retailers can help identify customers demographic characteristic identify shopping patterns direct mailing responses.

 

 

 

 

 

PROJECT ON DATA MINING AND DATA WAREHOUSING

 

 

Introduction :

 

What is Data Warehousing? Simply defined, a Data Warehousing is a collection of data designed to support managements decision making Data warehousing contain a wide variety of data that present a coherent picture of business conditions at a single point in time Typically a Data Warehousing is housed on enterprise mainframe server. It is a central repository for all or a significant part of all data that on enterprise’s numerous business systems collects. . The wasn’t always the case.

 

Data warehousing as quickly evolved into a unique and popular business already consider their system to be key components of their It strategy and architecture deployed for businesses of all sizes and all types. Hardware vendors have quickly developed modules and services that specifically target the data warehousing market. This paper will introduce key concepts surrounding the data warehousing system.

 

NATIONAL COMPUTER COLLEGE

 

GUIDED BY :                                                       CREATED BY :

Mrs. Chandani Dave                                            Miss Ashwini Harke         

Mrs. Ragini Sharma                                            Miss Preetiba Jadeja

Miss Sahin Alware                                                

 

 

 

 

 

 

 

 

 

 

 INTRODUCTION :

·                      DATA MINING

·                      DATA WAREHOUSING

ABOUT

·             Design issues for Data Warehousing

·             Analysis process of Data warehousing

  • Standard reports and Quarries
  • Tool to be used against the Data Warehousing
  • Practical Best Practices for Data Warehousing

·             Data Conversion Process

·            Process of Data Warehouse

·             Application of Data Warehouse

 

 

DATA MINING

By this point in time, you've probably heard a good deal about data mining -- the database industry's latest buzzword.  What's this trend all about?  To use a simple analogy, it's finding the proverbial needle in the haystack.  In this case, the needle is that single piece of intelligence your business needs and the haystack is the large data warehouse you've built up over a long period of time.

Through the use of automated statistical analysis (or "data mining") techniques, businesses are discovering new trends and patterns of behavior that previously went unnoticed.  Once they've uncovered this vital intelligence, it can be used in a predictive manner for a variety of applications. 

DATA WAREHOUSING

 

Executives today expect, and often receive, the good, timely information they need. To make inform decisions and lead their companies into the next decade. The wasn’t always the case.

 

Data warehousing as quickly evolved into a unique and popular business already consider their system to be key components of their It strategy and architecture deployed for businesses of all sizes and all types. Hardware vendors have quickly developed modules and services that specifically target the data warehousing market. This paper will introduce key concepts surrounding the data warehousing system.

 

What is Data Warehousing? Simply defined, a Data Warehousing is a collection of data designed to support managements decision making Data warehousing contain a wide variety of data that present a coherent picture of business conditions at a single point in time Typically a Data Warehousing is housed on enterprise mainframe server. It is a central repository for all or a significant part of all data that on enterprise’s numerous business systems collects.

 

Design issues for Data Warehousing

 

Discussion of building a Data Warehousing a Data Warehousing is a repository (or archive) of INFO gathered from multiple sources stored, under a unified scheme, at a single site. Once gathered, data are stored for a long time permitting excess to historical data.

 

Analysis process of Data warehousing

 

Analysis process of data ware houses range from the most basic (Query & Reporting to the more complex (Statically analysis) to the most complex (Data mining)

 

ç    Standard reports and Quarries

 

Many users of the Data Ware Houses need to access set of the standard reports and quarries. It is desirable to periodically automatically produce a setup standard reports that are required by many different users. When these users need a particular report, they can just view the report that has already been run by the Data Warehousing system rather then running bye particularly useful for reports that take a long time to run. Such a facility would require report server software.

 

 

 

ç    Tool to be used against the Data Warehousing

 

One of the objectives of the data warehousing is to make it as flexible and as open as possible. It is not desirable to set a steep entry price in terms of software and training for using the Data warehouse. The Data Warehouse should be accessible by as many end-users tools and platforms as possible. Yet it is not possible to make every feature of the Data Warehouse available from every end user too.

.

 

DATA CONVERSION PROCESS

 

The data conversion process for a data warehousing is complex, time consuming and unglamorous. It is also the very root of a good functional “data” is the operational operative word in “Data Warehouse” Quality data conversion are important to the Data Warehouse because the warehouse holds the information that is by to a corporation decision making process. For a corporation to obtained the ultimate goals and promises of data warehouse, It must understand that how critical the data conversion process is. Who involved before beginning the conversion process the Data Warehousing team completes the design and physical data model for the data warehouse and generates the toyed schemes.

 

Process of Data Warehouse

 

Data warehousing describes the process of defining populating and using a data warehouse. Data Warehouses emphasis the capture of data from diverse sources for useful analysis and access. Data from various transaction processing application and other sources is selectively extracted and organized on the data warehouse Database for use by analytical applications and user quarries.

 

 

ç    Practical Best Practices for Data Warehousing

 

Data Warehousing is an on going process not something that it built once and left to run. Some best practices for Data Warehousing consist of the following.

 

Build organizational commitment while managing user expectation.

A dynamic model should consider the following-

  • Business Goals / Objectives.
  • Business Areas / Functions.
  • Improvement opportunity.
  • Knowledge / Information.
  • Data
  • Structured agenda frames
  • Improved participants.
  • Trained objective 3rd party facilitations.
  • Make smart technology investments that are driven by business needs Businesses face concerns with
  • Data Source Challenges
  • End User Requirement
  • Deployment Maintenance Issues

 

Application of Data Warehouse

 

Some of the activity against today data warehousing is pre determined and not much different from traditional analysis activity. Other process such as multi dimension analysis and visualization where not available with traditional analysis tools and method. Some of the users of Data Warehouses are as under.

 

  1. Data Warehouses are based for customer relationship. Management System because they can be used for consolidator customers data and identifying areas customer satisfaction and frustration.
  2. Warehouse are also used for dictation, product repositioning analysis profit center discovery and cooperate assets management for retailers can help identify customers demographic characteristic identify shopping patterns direct mailing responses.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 INTRODUCTION :

·                      DATA MINING

·                      DATA WAREHOUSING

ABOUT

·             Design issues for Data Warehousing

·             Analysis process of Data warehousing

  • Standard reports and Quarries
  • Tool to be used against the Data Warehousing
  • Practical Best Practices for Data Warehousing

·             Data Conversion Process

·            Process of Data Warehouse

·             Application of Data Warehouse

 

 

 

Introduction to Data Warehouse

 

Executives today expect, and often receive, the good, timely information they need. To make inform decisions and lead their companies into the next decade. The wasn’t always the case.

 

Data warehousing as quickly evolved into a unique and popular business already consider their system to be key components of their It strategy and architecture deployed for businesses of all sizes and all types. Hardware vendors have quickly developed modules and services that specifically target the data warehousing market. This paper will introduce key concepts surrounding the data warehousing system.

 

What is Data Warehousing? Simply defined, a Data Warehousing is a collection of data designed to support managements decision making Data warehousing contain a wide variety of data that present a coherent picture of business conditions at a single point in time Typically a Data Warehousing is housed on enterprise mainframe server. It is a central repository for all or a significant part of all data that on enterprise’s numerous business systems collects. Fig 1 shows the architecture of a typical data warehouses, and illustrates the gathering of data, the storage of data and the querying and data analysis support.

 

 

 

 

 

 

 

 

 

                         

 

 

 

 

 

 

 

Evolution of Data Warehouse

 

This section reviews the factors that have let the evolution of the Data Warehousing class. Traditional approaches to historical data.

 

In reviewing the development of Data Warehousing, we need to be being with a review of what had been done with the data before of evolution of Data Warehouse. Let us first look at how the kind of data that and up in today Data Warehousing had been managed historically.

 

ç    Data from legacy systems

In 1970’s virtually all business systems development done on IBM main frame computers using tool such as COBALS, CICS, IMS, DBL etc. The 1980’s brought in the minicomputer platforms such as AS/400 and VAX/VMS. The late 80’s and early 90’s made UNIX a popular server platform with introduction of client/ server architecture.

 

Data Warehousing and Concept

 

 

This section explores the Data Warehousing concepts and attributes. These concepts are grouped into four sub sections. The first subsection discusses the reasons for separating the data business analysis from the operational data. The logical transformation of the data, including Data Warehouse modeling and de-normalization of the data are introduced in the second subsection. Sup section their reviews the issue associated with physical trams formative of data Sub-section four discusses the generation of summary views. A very simple and broad defination for Data warehouses follows a discussion of the Dashing concepts and attributes.

 

 

 

 

Design issues for Data Warehousing

 

Discussion of building a Data Warehousing a Data Warehousing is a repository (or archive) of INFO gathered from multiple sources stored, under a unified scheme, at a single site. Once gathered, data are stored for a long time permitting excess to historical data. Thus Data Warehousing provides the user a single consolidated interface to data, making decision support queries easier to write.

 

Setting up a Data Warehousing is not easy. Just identifying where all business data comes get exerted into a system & where it is all stored can be difficult & setting up a data cleansing processing is quite complicated. It all depends on how large and complex the data and storing operation is large Data Warehouse project a years and million of dollars implement.

 

Analysis process of Data warehousing

 

Analysis process of data ware houses range from the most basic (Query & Reporting to the more complex (Statically analysis) to the most complex (Data mining)

 

ç    Standard reports and Quarries

 

Many users of the Data Ware Houses need to access set of the standard reports and quarries. It is desirable to periodically automatically produce a setup standard reports that are required by many different users. When these users need a particular report, they can just view the report that has already been run by the Data Warehousing system rather then running bye particularly useful for reports that take a long time to run. Such a facility would require report server software.

 

 

 

ç    Tool to be used against the Data Warehousing

 

One of the objectives of the data warehousing is to make it as flexible and as open as possible. It is not desirable to set a steep entry price in terms of software and training for using the Data warehouse. The Data Warehouse should be accessible by as many end-users tools and platforms as possible. Yet it is not possible to make every feature of the Data Warehouse available from every end user too.

 

In most Data Warehouse projects, there is a need to select a preferred Data Warehouse access tool for the most active users. A small number of users generate most of the analysis activity against the Data Warehouse. The Data Warehouse performance can be tuned to be the requirements of the tools appropriate for these active users.

This tool can be used for training and demonstration of the data warehouses.

 

DATA CONVERSION PROCESS

 

The data conversion process for a data warehousing is complex, time consuming and unglamorous. It is also the very root of a good functional “data” is the operational operative word in “Data Warehouse” Quality data conversion are important to the Data Warehouse because the warehouse holds the information that is by to a corporation decision making process. For a corporation to obtained the ultimate goals and promises of data warehouse, It must understand that how critical the data conversion process is. Who involved before beginning the conversion process the Data Warehousing team completes the design and physical data model for the data warehouse and generates the toyed schemes. The data conversion team consist of business and technical people who design the warehouse structure analysis the source data identify data mapping gathered and for the external data determines the logical to convert the data plan and generates the conversion routine and quality assure the data they also select and use data migration, conversion and cleaning tools.

Process of Data Warehouse

 

Data warehousing describes the process of defining populating and using a data warehouse. Data Warehouses emphasis the capture of data from diverse sources for useful analysis and access. Data from various transaction processing application and other sources is selectively extracted and organized on the data warehouse Database for use by analytical applications and user quarries.

 

The high qualities of the product is most important, therefore, data undergoes extraction, cleansing and transformation based on business rules about its suitability learning to systemize business process that are effective means a better bottom line and profitability.

 

ç    Practical Best Practices for Data Warehousing

 

Data Warehousing is an on going process not something that it built once and left to run. Some best practices for Data Warehousing consist of the following.

 

Build organizational commitment while managing user expectation.

A dynamic model should consider the following-

  • Business Goals / Objectives.
  • Business Areas / Functions.
  • Improvement opportunity.
  • Knowledge / Information.
  • Data
  • Structured agenda frames
  • Improved participants.
  • Trained objective 3rd party facilitations.
  • Make smart technology investments that are driven by business needs Businesses face concerns with
  • Data Source Challenges
  • End User Requirement
  • Deployment Maintenance Issues

Application of Data Warehouse

 

Some of the activity against today data warehousing is pre determined and not much different from traditional analysis activity. Other process such as multi dimension analysis and visualization where not available with traditional analysis tools and method. Some of the users of Data Warehouses are as under.

 

  1. Data Warehouses are based for customer relationship. Management System because they can be used for consolidator customers data and identifying areas customer satisfaction and frustration.
  2. Warehouse are also used for dictation, product repositioning analysis profit center discovery and cooperate assets management for retailers can help identify customers demographic characteristic identify shopping patterns direct mailing responses.

 

For banks, it can assist in spotting credit card fraud help identify the most profitable customers, and highlight the most local customers.

 

  • Tele communication firms used Data Warehousing to predict which customers are likeliest to switch and then with special incentives to stay.
  • Insurances co. use Data Warehouse for claims analysis to see which procedures are claimed together and to identify patterns of risky customers
  • Manufacturer can use data warehousing to compare costs of each of their product lives over the last general years, determine which factors produced increases and see what effect these increases overall margins. 

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