IT CONSULTING LLC
BI applications use data gathered from a data warehouse (DW)
or from a data mart, and the concepts of BI and DW sometimes
combine as "BI/DW" or as "BIDW". A data
warehouse contains a copy of analytical data that facilitates
decision support. However, not all data warehouses serve for
business intelligence, nor do all business intelligence applications
require a data warehouse.
distinguish between the concepts of business intelligence and
data warehouses, Forrester Research defines business intelligence
in one of two ways:
1.Using a broad definition: "Business Intelligence is a
set of methodologies, processes, architectures, and technologies
that transform raw data into meaningful and useful information
used to enable more effective strategic, tactical, and operational
insights and decision-making." Under this definition, business
intelligence also includes technologies such as data integration,
data quality, data warehousing, master-data management, text-
and content-analytics, and many others that the market sometimes
lumps into the "Information Management" segment. Therefore,
Forrester refers to data preparation and data usage as two separate
but closely linked segments of the business-intelligence architectural
2.Forrester defines the narrower business-intelligence market
as, "...referring to just the top layers of the BI architectural
stack such as reporting, analytics and dashboards."
with competitive intelligence
the term business intelligence is sometimes a synonym for competitive
intelligence (because they both support decision making), BI
uses technologies, processes, and applications to analyze mostly
internal, structured data and business processes while competitive
intelligence gathers, analyzes and disseminates information
with a topical focus on company competitors. If understood broadly,
business intelligence can include the subset of competitive
in an enterprise
intelligence can be applied to the following business purposes,
in order to drive business value.
1.Measurement program that creates a hierarchy of performance
metrics (see also Metrics Reference Model) and benchmarking
that informs business leaders about progress towards business
goals (business process management).
2.Analytics program that builds quantitative processes
for a business to arrive at optimal decisions and to perform
business knowledge discovery. Frequently involves: data mining,
process mining, statistical analysis, predictive analytics,
predictive modeling, business process modeling, data lineage,
complex event processing and prescriptive analytics.
3.Reporting/enterprise reporting program that builds
infrastructure for strategic reporting to serve the strategic
management of a business, not operational reporting. Frequently
involves data visualization, executive information system and
4.Collaboration/collaboration platform program that gets
different areas (both inside and outside the business) to work
together through data sharing and electronic data interchange.
5.Knowledge management program to make the company data
driven through strategies and practices to identify, create,
represent, distribute, and enable adoption of insights and experiences
that are true business knowledge. Knowledge management leads
to learning management and regulatory compliance.
addition to the above, business intelligence can provide a pro-active
approach, such as alert functionality that immediately notifies
the end-user if certain conditions are met. For example, if
some business metric exceeds a pre-defined threshold, the metric
will be highlighted in standard reports, and the business analyst
may be alerted via email or another monitoring service. This
end-to-end process requires data governance, which should be
handled by the expert.
can be difficult to provide a positive business case for business
intelligence initiatives, and often the projects must be prioritized
through strategic initiatives. BI projects can attain higher
prioritization within the organization if managers consider
As described by Kimball the BI manager must determine the tangible
benefits such as eliminated cost of producing legacy reports.
Data access for the entire organization must be enforced.
In this way even a small benefit, such as a few minutes saved,
makes a difference when multiplied by the number of employees
in the entire organization.
As described by Ross, Weil & Roberson for Enterprise Architecture,
managers should also consider letting the BI project be driven
by other business initiatives with excellent business cases.
To support this approach, the organization must have enterprise
architects who can identify suitable business projects.
Using a structured and quantitative methodology to create defensible
prioritization in line with the actual needs of the organization,
such as a weighted decision matrix.
factors of implementation
to Kimball et al., there are three critical areas that organizations
should assess before getting ready to do a BI project:
1.The level of commitment and sponsorship of the project from
2.The level of business need for creating a BI implementation
3.The amount and quality of business data available.