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Data Warehousing and Data Mining

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niranjan chella

on 12 October 2012

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Transcript of Data Warehousing and Data Mining

Data Storage Concepts are divided into two Basic Different

By :- Niranjan
Co - related with each Other INTRODUCTION

In computing, a data warehouse (DW or DWH)
is a database used for reporting and data analysis.
It is a central repository of data which is created
by integrating data from multiple disparate
It stores current as well as historical data and
are commonly used for creating trending reports
for senior management reporting such as annual
and quarterly comparisons. NEED FOR DATA WAREHOUSE If you want to get information on all the techniques of designing, maintaining, building and retrieving data, Data warehousing is the ideal method. A data warehouse is premeditated and generated for supporting the decision making process within an organization. When the production databases are copied in the warehouse, it becomes easier to answer all the queries without hampering the consistency of the production system.

A data warehouse is actually a set of new concepts and important tools evolved into a technology. With the help of data warehousing, it becomes easy for an organization to counter all the problems faced during providing key information to concerned people.

Is a Data Warehouse really necessary? Organisations can use the data in an direct way without storing in the database by using one of the BI(Business Intelligence) Tool so as to obtain the business queries, reports, Analytical Applications and Dashboards. Countless BI pioneers have discovered
the hard way that the “direct access” approach
simply does not work very well. Reasons : • New releases of application software frequently introduce changes that make it necessary to rewrite and test reports.
• These changes make it difficult to create and maintain reports that summarize data originating within more than one release. "What do you think are the primary motivators for investing in a Data Warehouse/Data Mart? i.e. identifying new customers, reduce cost of operations, etc." In response, "I think the primary motivation for investing in data warehouses is to provide more timely information for decision-making. Some managers claim the purpose is to serve current customers better, but managers aren't always clear about how that will happen. The growth of the Business Intelligence/OLAP software market indicates that managers need more than faster access to data. Managers need analysis of data to gain benefits from data warehouses/data marts." The Advantages from developing a data warehouse : 1. Integrating data from multiple sources.

2. Performing new types of analysis; and

3. Reducing cost to access historical data. Other benefits may include :
1. Standardizing data across the organization, a "single
version of the truth".

2. Improving turnaround time for analysis and

3. Sharing data and allowing others to easily access
data. INTRODUCTION We are in an age often referred to as the information age. In this information age, because we believe that information leads to power and success, and thanks to sophisticated technologies such as computers, satellites, etc. we have been collecting tremendous amounts of information. Initially, with the advent of computers and means for mass digital storage, we started collecting and storing all sorts of data, counting on the power of computers to help sort through this problem of information. Today, we have far more information than we can handle: from business transactions and scientific data, to satellite pictures, text reports and military intelligence. Information retrieval is simply not enough anymore for decision-making. Confronted with huge collections of data, we have now created new needs to help us make better managerial choices. These needs are automatic summarization of data, extraction of the "essence" of information stored, and the discovery of patterns in raw data. NEED FOR DATA MINING

Nowadays, large quantities of data is being accumulated. The amount of data collected is said to be almost doubled every 9 months. Seeking knowledge from massive data is one of the most desired attributes of Data Mining. Data could be large in two senses. In terms of size, e.g. for Image Data or in terms of dimensionality, e.g. for Gene expression data.

Usually there is a huge gap from the stored data to the knowledge that could be construed from the data. This transition won't occur automatically, that's where Data Mining comes into picture. In Exploratory Data Analysis, some initial knowledge is known about the data, but Data Mining could help in a more in-depth knowledge about the data.

Increasing data dimensionality and data size.

Various forms of data.

New types of data like streaming data and multimedia

Efficiency in data access and information search methods.

Intelligent upgrade and integration methods. Data Mining Challenges THANK YOU
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