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Abstract
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 of data 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 thence the overview of, the so hyped, Data
Warehousing and Data Mining.
Introduction
“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 cross-functional 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.
The Transformation of Data into Knowledge and associated tools.
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
decision-making 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.
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
*
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 characterized 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
The Data Mining Process.
Data Mining and 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 and Knowledge Discovery
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.
Compendium
* A data warehouse takes
the organisations operational data, historical data and external data
*
Consolidates it into a separately designed database (which can either be
relational or multi-dimensional in nature)
*
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.
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