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DATA WAREHOUSING AND DATA MININGDATA WAREHOUSING AND DATA MINING
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DATA WAREHOUSING
 Data warehousing is combining data from
multiple sources into one comprehensive and
easily manipulated database.
 The primary aim for data warehousing is to
provide businesses with analytics results from
data mining, OLAP, Scorecarding and
reporting.

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NEED FOR DATA WAREHOUSINGNEED FOR DATA WAREHOUSING
Information is now considered as a key
for all the works.
Those who gather, analyze, understand,
and act upon information are winners.
Information have no limits, it is very hard
to collect information from various
sources, so we need an data warehouse
from where we can get all the
information.
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TODAYS BUISNESS INFORMATIONTODAYS BUISNESS INFORMATION
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Retrieving data
Analyzing data
Extracting data
Loading data
Transforming data
Managing data
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DATA WAREHOUSING INCLUDES:-

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DATA WAREHOUSE ARCHITECTUREDATA WAREHOUSE ARCHITECTURE
Data warehousing is designed to
provide an architecture that will make
cooperate data accessible and useful to
users.
There is no right or wrong architecture.
The worthiness of the architecture can
be judge by its use, and concept behind
it .
Data Warehouses can be architected in
many different ways, depending on the
specific needs of a business. 6

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Typical Data Warehousing Environment

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An operational data store (ODS) is
basically a database that is used for being
an temporary storage area for a
datawarehouse.
Its primary purpose is for handling data
which are progressively in use.
Operational data store contains data which
are constantly updated through the course
of the business operations.
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ETL (Extract, Transform, Load) is used to
copy data from:-
ODS to data warehouse staging area.
Data warehouse staging area to data
warehouse .
Data warehouse to data mart .
ETL extracts data, transforms values of
inconsistent data, cleanses "bad" data,
filters data and loads data into a target
database.
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The Data Warehouse Staging Area is
temporary location where data from
source systems is copied.
It increases the speed of data warehouse
architecture.
It is very essential since data is increasing
day by day.
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The purpose of the Data Warehouse is to
integrate corporate data.
The amount of data in the Data Warehouse is
massive. Data is stored at a very deep level of
detail.
This allows data to be grouped in unimaginable
ways.
Data Warehouses does not contain all the data
in the organization ,It's purpose is to provide
base that are needed by the organization for
strategic and tactical decision making.
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ETL extract data from the Data Warehouse
and send to one or more Data Marts for
use of users.
Data marts are represented as shortcut to
a data warehouse ,to save time.
It is just an partition of data present in
data warehouse.
Each Data Mart can contain different
combinations of tables, columns and rows
from the Enterprise Data Warehouse.
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REASONS FOR CREATING AN DATAREASONS FOR CREATING AN DATA
MARTMART
Easy access to frequently needed data.
Creates collective view by a group of
users.
Improves user response time.
Ease of creation.
Lower cost than implementing a full
Data warehouse
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DATA MININGDATA MINING
The non-trivial extraction of implicit,
previously unknown, and potentially
useful information from large databases.
– Extremely large datasets
– Useful knowledge that can improve
processes
– Cannot be done manually
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Where Has it Come From ?Where Has it Come From ?
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MotivationMotivation
 Databases today are huge:
– More than 1,000,000 entities/records/rows
– From 10 to 10,000 fields/attributes/variables
– Giga-bytes and tera-bytes
 Databases a growing at an unprecendented rate
 The corporate world is a cut-throat world
– Decisions must be made rapidly
– Decisions must be made with maximum
knowledge
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How does data mining work?How does data mining work?
Extract, transform, and load transaction data
onto the data warehouse system.
Store and manage the data in a multidimensional
database system.
Provide data access to business analysts and
information technology professionals.
Analyze the data by application software.
Present the data in a useful format, such as a
graph or table
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DATA MINING MEASURESDATA MINING MEASURES
Accuracy
Clarity
Dirty Data
Scalability
Speed
Validation
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Typical Applications of Data MiningTypical Applications of Data Mining
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ADVANTAGES OF DATA MININGADVANTAGES OF DATA MINING
 Engineering and Technology
 Medical Science
 Business
 Combating Terrorism
 Games
 Research and Development
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Engineering and TechnologyEngineering and Technology
In Electrical Power Engineering
- used for condition monitoring of
high
voltage electrical equipment
- vibration monitoring and analysis
of
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Engineering and TechnologyEngineering and Technology
transformer on-load tap-changers
Education
- to concentrate their knowledge
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Medical ScienceMedical Science
Data mining has been widely used in area
of bioinformatics , genetics
DNA sequences and variability in disease
susceptibility which is very important to
help improve the diagnosis, prevention
and treatment of the diseases
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BUSINESSBUSINESS
In Customer Relationship Management
applications
It Translate data from customer to
merchant Accurately
Distribute Business Processes
Powerful Tool For Marketing
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Combating terrorismCombating terrorism
Concept used by Interpol against
terrorists for searching their records by
Multistate Anti-Terrorism Information
Exchange
In the Secure Flight program , Computer
Assisted Passenger Pre screening
System , Semantic Enhancement
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GamesGames
for certain combinatorial games, also
called table bases (e.g. for 3x3-chess)
It includes extraction of human-usable
strategies
Berlekamp in dots-and-boxes and Joh
Nunn in chess endgames are notable
examples
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Research And DevelopmentResearch And Development
Helps to Develop the search algorithms
It offers huge libraries of graphing and
visualisation softwares
The users can easily create the models
optimally
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List of the top eight data-miningList of the top eight data-mining
software vendors in 2008software vendors in 2008
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Angoss Software
Infor CRM Epiphany
Portrait Software
SAS
G-Stat
SPSS
ThinkAnalytics
Unica
Viscovery

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THANK YOU
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