What is Data
Warehouse?
Ø Defined in many different
ways, but not rigorously
- A decision support
database that is maintained separately from the organization’s operational
database.
- A consistent
database source that bring together information from multiple sources for
decision support queries.
- Support information
processing by providing a solid platform of consolidated, historical data for
analysis.
History of Data
Warehousing
Ø In the 1990’s executives
became less concerned with the day-to-day business operations and more
concerned with overall business functions
Ø The data warehouse provided
the ability to support decision making without disrupting the day-to-day
operations, because;
- Operational information is
mainly current – does not include the history for better decision making
- Issues of quality information
- Without
information history, it is difficult to tell how and why things change over
time
Data warehouse
fundamentals
Ø Data warehouse – A logical
collection of information – gathered from many different operational databases
– that supports business analysis activities and decision-making takes
Ø The primary purpose of a
data warehouse is to combined information throughout an organization into a
single repository for decision-making purposes – data warehouse support only
analytical processing
Data warehouse model
Ø Extraction, transformation
and loading (ETL) – A process that extracts information from internal and
external databases, transforms the information using a common set of enterprise
definitions, and loads the information into a data warehouse.
Ø Data warehouse then send
subsets of the information to data mart.
Ø Data mart – contains a
subset of data warehouse information.
Multidimensional Analysis and Data Mining
Ø Relational Database
contains information in a series of two-dimensional tables.
Ø In a data warehouse and
data mart, information is multidimensional, it contains layers of columns and
rows
- Dimension – A
particular attribute of information
Ø Cube – common term for the
representation of multidimensional information
Ø Once a cube of information
is created, users can begin to slice and dice the cube to drill down into the
information.
Ø Users can analyze
information in a number of different ways and with number of different
dimensions.
Ø Data Mining – the process
of analyzing data to extract information not offered by the raw data alone.
Also known as “knowledge discovery” – computer-assisted tools and techniques for
sifting through and analyzing vast data stores in order to finds trends,
patterns and correlations that can guide decision making and increase
understanding
Ø To perform data mining
users need data-mining tools
- Data-mining tool –
uses a variety of techniques to finds patterns and relationships in large
volumes of information. Eg: retailers and use knowledge of these patterns to
improve the placement of items in the layout of a mail-order catalog page or
Web page.
Information Cleansing or Scrubbing
Ø An organization must
maintain high-quality data in the data warehouse
Ø Information cleansing or
scrubbing – A process that weeds out and fixes or discards inconsistent,
incorrect or incomplete information
Ø Occurs during ETL process
and second on the information once if is in the data warehouse
Ø Contract information in an
operational system
Ø Standardizing Customer name from Operational Systems
Ø Information cleansing
activities
- Missing Records
or Attributes
- Redundant
Records
- Missing Keys or
Other Required Data
- Erroneous
Relationships or References
- Inaccurate Data
Ø Accurate and complete
information
Business Intelligence
Ø Business Intelligence –
refers to applications and technologies that are used to gather, provides
access, analyze data and information to support decision making efforts
Ø These systems will
illustrate business intelligence in the areas of customer profiling, customer
support, market research, market segmentation, product profitability,
statistical analysis, and inventory and distribution analysis to name a few
Ø Eg; Excel, Access
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