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
No comments:
Post a Comment