Chapter 8: Accersing Organizational Information - Data Warehouse
HISTORY OF DATA WAREHOUSING
•Data warehouses extend the
transformation of data into information
•In the 1990's executives became
less concerned with the day-to-day business operations and more concerned with
overall business functions
•The data warehouse provided the
ability to support decision making without disrupting the day-to-day operations
DATA WAREHOUSE FUNDAMENTALS
Figure 1
The primary purpose of a data warehouse is to aggregate
information throughout an organization into a single repository for
decision-making purposes. The informational can collected from internal or
external database and before it transfer to data warehouse the information will
enter through process extraction, transformation, and loading (ETL).
After that, it will send subsets of the information to
data marts. When the information transfer to data warehouse, the ETL process
will happen again to classify the information into the group or classes. For
example, if the information from internal database is about marketing, so the
information will go the same group that relates with marketing. It will not
mess up with other information.
This is my understanding about data warehouse and how
it operates.
Definition:
1.
Data
warehouse: a logical collection of information - gathered from
many different operational databases - that supports business analysis
activities and decision-making tasks.
The purpose of data warehouse is to aggregate
information throughout an organization into a single repository in such a way
that employees can make decision and undertake business analysis activities.
2.
Extraction,
transformation, and loading (ETL): a
process that extracts information form internal and external databases,
transforms the information using a common set of enterprise definitions, and
loads the information into a data warehouse. The data warehouse then sends
subsets of the information to data marts.
3.
Date mart: contains a subset of data
warehouse information.
To distinguish between data warehouse and
data marts, thinks of data warehouse having a more organizational focus and
data marts having focused information subsets particular to the needs of a
given business unit such as finance/ production and operations.
MULTIDIMENSIONAL ANALYSIS
AND DATA MINING
A relational database contains information
in a series of two-dimensional tables. In a data warehouse and data mart,
information is multidimensional where it contains layers of column and rows.
Most data warehouse and data mart are Multidimensional
Database.
Dimension
: a particular attribute of
information.
Cube:
the common term for the representation
of multidimensional information.
Figure 2
Ø The figure 2 shows a cube (cube a) represents store information (the layers), product information (the rows) and promotion information (the column).
Ø Once a cube of information is created, the users may
begin to slice and dice the cube to drill down into the information.
Ø Later, the second cube (cube b) displays slice representing promotion II information for
all product, at all stores.
Ø Third cube (cube
c) which displays only information for promotion III, product B, at store
2.
Therefore, by using multidimensional analysis, users
may analyze information in a number of different ways and with any number of
different dimensional. For example, users can add dimensions of information to
a current analysis including product category, region and even forecasted
versus actual weather.
Data mining: process of analyzing data to extract information not
offered by the raw data alone.
It also can begin at a summary information level and
progress through increasing levels of detail (drilling down) or the reverse (drilling
up). To perform data mining, users need data mining tools
Data mining tools:
use a variety of techniques to find
patterns and relationships in large volumes of information and infer rules from
them that predict future behavior and guide decision making.
Date-mining tools for data warehouse and data mart
include query tools, reporting tools, multidimensional analysis tools,
statistical tools and intelligent agents.
INFORMATION CLEANSING OR SCRUBBING
An organization must
maintain high-quality data in the data warehouse
Information
cleansing or scrubbing: a
process that weeds out and fixes or discards inconsistent, incorrect, or
incomplete information.
It is
where to increase the quality of organizational information and the
effectiveness of decision making. Specialized software use sophisticated
algorithms to parse, standardized, correct, match and consolidate data
warehouse information.
Figure 3: Contact
information in operational systems.
Figure 4: standardizing customer name from operational systems
Figure 5: information cleansing activities.
Figure 6: accurate and complete information.
Business Intelligence
Business intelligence (BI) refers to applications and technologies that are
used to gather, provide access to, and analyze data and information to support
decision-making efforts. It also information that people use to support
their decision-making efforts
A
certain school of thought draws parallels between the challenges in business
and those of war, specifically:
i.
Collecting information.
ii.
Discerning patterns and meaning in the
information.
iii.
Responding to the resultant information.
ENABLING BUSINESS
INTELLIGENCE
Principle BI enablers include
Technology : the most significant enabler of
business intelligence.
People : Understanding the role of people
in BI allows organizations to systematically create insight and turn these
insights into actions.
Culture : A key responsibility of
executives is to shape and manage corporate culture.
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