paper presentation on data warehousing

 

 

CONTENTS

Introduction

Warehouse with a database.

What is Data-Warehousing ?

-  What it actually is ?

                        -   Information Sources.

                        -   Decision Support Tools.

                        -   The Data Warehouse.

                        -   It’s Functions.

                        -   Architecture.

Data Mining.

        - Data Mining with Data Warehousing.

         -  Data Mining as a Part of the Knowledge Discovery Process.

         -  Goals of Data Mining .

Conclusion.

A B S T R A C T

Organisations are today suffering from a malaise of data overflow. The developments in the transaction processing technology has given rise to a situation where the amount and rate ofdata capture is very high, but the processing of this data into information that can be utilised for decision making, is not developing at the same pace. Data warehousing and data mining (both data & text) provide a technology that enables the decision-maker in the corporate sector/govt. to process this huge amount of data in a reasonable amount of time, to extract intelligence/knowledge in a near real time.

The data warehouse allows the storage of data in a format that facilitates its access, but if  the tools for deriving information and/or knowledge and presenting them in a format that is useful for decision making are not provided the whole rationale for the existence of the warehouse disappears. Various technologies for extracting new insight from the data warehouse have come up which we classify loosely as "Data Mining Techniques".

Our paper focuses on the need for information repositories and discovery of knowledge and hence the overview of, the so hyped, Data Warehousing and Data Mining.

I N T R O D U C T I O N

“Knowledge [no more Information] is not only power, but also has significant competitive  advantage”

Organizations have lately realized that just processing transactions and/or information’s faster and more efficiently, no longer provides them with a competitive advantage vis-à-vis their competitors for achieving business excellence. Information technology (IT) tools that are oriented towards knowledge processing can provide the edge that organizations need to survive and thrive in the current era of fierce competition. The increasing competitive pressures and the desire to leverage information technology techniques have led many organizations to explore the benefits of new emerging technology – viz. "Data Warehousing and Data Mining". What is needed today is not just the latest and updated to the nano-second information, but the crossfunctional information that can help decisions making activity as "on-line" process.

Evolution of Information Technology Tools

The evolution of the information systems characterize the evolution of systems from data maintenance systems, to systems that transform the data into "information" for use in the decision making process. These systems supported the information acquisition from the database of transactional data. The managerial knowledge acquisition function is/was not directly supported by these systems . The evolution of new patterns in the changing scenario could not be provided by these systems directly, the planner was supposed to do this from experience.

Warehouse with a database

          One thing that remains constant , especially in corporate world , is “ Change”

And, these days, change is occurring at an ever-increasing rate. A key challenge is implementing an information infrastructure that allows your company to rapidly respond to change. One solution to this challenge is the data warehouse.

Data warehousing is an information infrastructure based on detail data that supports the decisionmaking process and provides businesses the ability to access and analyze data to increase an organization's competitive advantage.

Data warehousing is a process, not an off-the-shelf solution you buy, but hardware--database and tools integrated into an evolving information infrastructure--that changes with the dynamics of the business.

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What is Data-Warehousing ?

The data warehouse makes an attempt to figure out "what we need", before we know we  need it.

What it actually is?

* A data warehouse stores current and historical data

* This data is taken from various, perhaps incompatible, sources and stored in a uniform format

* Several tools transform this data into meaningful business information for the purpose of comparisons, trends and forecasting 5

* Data in a warehouse is not updates or changed in any way, but is only loaded and accessed later on

* Data is organized according to subject instead of application. In general a database is not a data warehouse unless it has the following two features:

· It collects information from a number of different disparate sources and is the place where this disparity is reconciled, and

· It allows several different applications to make use of the same information.

Conceptually, a Data Warehouse looks like this:

Information Sources

Always include the core operational systems which form the backbone of day-to-day activities. It is these systems which have traditionally provided management information to support decision making.

Decision Support Tools

Are used to analyze the information stored in the warehouse, typically to identify trends and new business opportunities..

The Data Warehouse

Itself is the bridge between the operational systems and the decision support tools. It holds a copy of much of the operational system data in a logical structure which is more conducive to analysis. The Data Warehouse, which will be refreshed in scheduled bursts from operational systems and from relevant external data sources, provides a single, consistent view of corporate data, leaving operational systems

unaffected.

Data – Warehouse Functions

The main function behind a data warehouse is to get the enterprise-wide data in a format  that is most useful to end-users, regardless of their locations. Data warehousing is used for:

• Increasing the speed and flexibility of analysis.
• Providing a foundation for enterprise-wide integration and access.
• Improving or re-inventing business processes.
• Gaining a clear understanding of customer behavior.

Data Warehouse Architecture

Each implementation of a data warehouse is different in its detailed design (a schematic high-level of the architecture and its components is given in the figure below), but all are characterised by a handful of the following key components:

· A data model to define the warehouse contents.

· A carefully designed warehouse database, whether hierarchical, relational, or

multidimensional. While choosing a DBMS it must be kept in view that the database management system should be powerful enough to handle huge amount of data running up to terabytes.

· A front end for Decision Support System (DSS) for reporting and for structured and

unstructured analysis.

Data Mining

Data base mining or Data mining (DM) (formally termed Knowledge Discovery in Databases – KDD) is a process that aims to use existing data to invent new facts and to uncover new relationships previously unknown even to experts thoroughly familiar with the data. It is like extracting precious metal (say gold etc.) and/or gems, hence the term “mining”, It is based on filtration and assaying of mountain of data “ore” in order to get “nuggets” of knowledge. The data mining process is diagrammatically exemplified in Figure below

Data Mining with Data Warehousing

· The goal of a data warehouse is to support decision making with data.

· Data mining can be used in conjunction with a data warehouse to help with certain types of decisions.

· Data mining can be applied to operational databases with individual transactions.

· To make data mining more efficient, the data warehouse should have an aggregated or summarized collection of data.

· Data mining helps in extracting meaningful new patterns that cannot be found necessarily by merely querying or processing data or metadata in the data warehouse.

Data Mining as a Part of the Knowledge Discovery Process

· Knowledge Discovery in Databases, frequently abbreviated as KDD, typically encompasses more than data mining.

· The knowledge discovery process comprises six phases:

Data selection ,Data about specific items or categories of items, or from stores in a specific

region or area of the country, may be selected.

Data cleansing process then may correct invalid zip codes or eliminate records with incorrect

phone prefixes.

Enrichment typically enhances the data with additional sources of information.

Data transformation and encoding may be done to reduce the amount of data.

Goals of Data Mining

The goals of data mining fall into the following classes:

    Prediction: Data mining can show how certain attributes within the data will behave in the future.

    Identification: Data patterns can be used to identify the existence of an item, an event, or an activity.

   Classification: Data mining can partition the data so that different classes or categories can be identified

based on combinations of parameters.

   Optimization: One eventual goal of data mining may be to optimize the use of limited resources such as time, space, money, or materials and to maximize output variables such as sales or profits under a given set of constraints.

                       

CONCLUSION

              A data warehouse takes the organisations operational data, historical data and external data

a) consolidates it into a separately designed database (which can either be

relational or multi-dimensional in nature)

b) manages it into a format that is optimised for end users to access and analyse.

When a data warehouse has been constructed, it provides a complete picture of the

enterprise. It provides an unparalleled opportunity to the management to learn about their

customers.

The data warehouse technology together with online transaction processing and data mining, allows the management to provide better customer service, create greater  customer loyalty and activity, focus customer acquisition and retention of the most profitable customer, increase revenue, reduce operating cost; provides tools that facilitate sounder decision making; improves worker/management knowledge and productivity; spares the operational database from ad-hoc queries with the resulting performance degradation and clears the legacy database system, while moving the corporate system architecture forward.

With the incorporation of new data delivery and presentation techniques, like hypertext mark up language (HTML), Open Database Connectivity (ODBC) etc. the database mining (Data & Text) operation has gained wide spread recognition as a viable tool for business intelligence gathering. Advances in the document mining technology (database mining of free form text/data, in contrast to the “classical” approach to data mining of fixed length records) are making the data mining technology more powerful.

Last but never the least, the Internet has emerged as the largest data warehouse of unstructured and free form data. The new technologies are geared towards mining this great data warehouse.