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Business Intelligence: From Theory to Reality (Part one) by Shaku Atre - J...
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http://www.technologytransfer.eu/article/26/2003/7/Business_Intelligenc...
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August:
Simplicity and Requirements
by Suzanne Robertson
Articles of 2010
by Shaku Atre
July 2003
Articles of 2009
Articles of 2008
Business intelligence (BI) decision-support initiatives are
Articles of 2007
ultimately fail by curtailing the initial goals. But it needn’t
Articles of 2006
Articles of 2005
Articles of 2004
Articles of 2003
December:
Using Function Point Analysis
for an Object Oriented Methodology
by Koni Thompson
October:
The evolution of the Enterprise Portal
by Colin White
costly and difficult. A staggering 60% of all BI projects
be that way.
BI projects are costly because disparate business data must be extracted and merged from
a variety of data sources, including online transaction processing (OLTP) systems, batch
systems, and external syndicates. Also, many BI decision-support initiatives require new
technologies, new tasks, and new roles and responsibilities. Making the projects even more
costly, analysis and decision-improving applications must be delivered promptly and with
high quality.
BI projects are difficult to implement, and as a result they fail in such great number due to
a long list of mistakes and missteps. These include inadequate planning, missed tasks,
missed deadlines, poor project management, undelivered business requirements, and
low-quality deliverables.
There is an alternative. With this alternative, the high costs of BI implementations can be
reduced. The failure rates can be lowered. But to achieve these improvements, technology
managers must use the right tools and an appropriate method for implementing the BI
decision-support systems. They must also open their eyes to the four new realities of BI.
First, technology managers must realize that BI is neither a product nor a system. Instead, it
August:
is an architecture and a collection of integrated operational systems. BI can also include
Reality (Part Two)
by Shaku Atre
Examples of BI decision-support applications include:
Business Intelligence: From Theory to
July:
Business Intelligence: From Theory to
Reality (Part one)
by Shaku Atre
June:
Corporate and E-Business Portals:
The Next Generation Workplace
by Mike Ferguson
April:
decision-support applications and databases that ease access to business data.
Multi-dimensional analysis (OLAP)
Click-stream analysis
Data mining
Forecasting
Business analytics
Balanced scorecard
Examples of BI decision-support databases include:
Enterprise-wide data warehouses
Data marts (functional and departmental)
Exploration warehouses (statistical)
Data mining databases
Web warehouses (click-stream)
8/30/2010 1:23 PM
Business Intelligence: From Theory to Reality (Part one) by Shaku Atre - J...
2 of 2
Top 10 Questions to Ask/Mistakes to
Avoid
When Building a Meta Data Repository
(Part II)
by David Marco
http://www.technologytransfer.eu/article/26/2003/7/Business_Intelligenc...
Second, managers must learn that BI implementations, like nearly every kind of
engineering project, pass through six stages between inception and implementation. These
six stages are:
March:
Top 10 Questions to Ask/Mistakes to
Avoid
When Building a Meta Data Repository(Part
I)
by David Marco
February:
Using Patterns to Capture Design
Experience
by James Hobart
January:
Building the Intelligent Business
by Colin White
Articles of 2002
1. Justification: An assessment is made of a business problem or a business
opportunity, which gives rise to the engineering project.
2. Planning: Strategic and tactical plans are developed, which lay out how the
engineering project will be accomplished.
3. Business Analysis: Detailed analysis of the business problem or business
opportunity is performed to gain a solid understanding of the business requirements
for a potential solution (product).
4. Design: A product is conceived, which solves the business problem or enables the
business opportunity.
5. Construction: The conceived product is built, and it is expected to provide a return
on the development investment within a predefined timeframe.
6. Deployment: The finished product is implemented (or sold) and its effectiveness is
measured to determine whether the solution meets, exceeds, or fails to meet the
expected return on investment.
Third, managers must realize that BI systems, like many other engineering processes, are
iterative. This means that after a BI system is deployed, it's continually improved and
enhanced, based on feedback from its business users. Each iteration, in turn, produces a
new product release, or version. In this way, the product evolves and matures. Also, BI's
iterative process produces an important side effect: It renders the system-development
practices of the past both inadequate and inappropriate.
In the past, every system had a clear beginning and an end. Each system was also
developed for one set of knowledge workers from one business line. As a result, crossorganizational activities were unnecessary. In fact, early designers viewed crossorganizational activity as a barrier that only slowed project progress.
Today, however, these development practices are no longer suitable. Modern, integrated BI
initiatives must take into account the cross-organizational activities necessary to sustain an
enterprise-wide decision-support environment. That, in turn, requires new development
skills, practices, and techniques.
Fourth, managers must learn that the dynamic, integrated BI decision-support environment
is iterative in nature. In other words, it cannot be built in one big bang. Instead, data and
functionality must be rolled out in releases. Each deployment will then likely trigger new
requirements for the next release.
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