The main value that analytic applications bring is built-in business intelligence, which is a
predefined set of metrics and key performance indicators that business users would have had to
build otherwise. By purchasing an analytic application, organizations also purchase business
expertise with which they can gain valuable insight into their businesses.
(See
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Fig.1 Processes and applications.)True analytic applications contain the key components of business intelligence systems such as ETL, standard and ad-hoc queries, alerts, etc. The best applications offer three additional functionality elements:
- Complex analysis
- Decision deployment
- Closed-loop analysis
The complex analysis component allows for detailed statistical analysis of the data collected in
the data mart for solving a specific problem such as pricing and inventory problems. The results
are collected in the data mart along with the internal and external data, and are then presented to
the decision-makers in forms of conventional visualization methods. The decision-maker submits
recommendations to the application system, and then the application submits a decision to the
operational application systems, where the decision is carried out. The analytic application
collects data from the operational application continually, at some point picking up data relevant
to a decision or suggestion presented to the operational application as mentioned earlier. At this
point, the loop is closed. The analytic application performs the analysis not only based on
internal and external historical data, but also based on data that contains the implications of
previous analysis. In the best analytic applications, the technology and the data framework is
built such that when the decisions are carried out on the business side, the application has the
capability to tie that decision and the results back into the historical data which was the source
of the decision in the first place.
(See Fig. 2 Analytic Application Interactions.)
One challenge with analytic applications is that they are very focused in terms of the business
processes for which they provide decision support. A CRM analytic application will not be helpful
in questions about finance or manufacturing. However, the nature of decision-making process is such
that two things are likely to happen: the decision-maker will need data from more than one business
process, or, after initial analysis, the decision-maker will increase the scope of analysis trying
to find solutions to more complex decision issues. When either of these scenarios occur, one of
these three things must take place:
- The decision-maker manually collects and analyzes data
- The decision-maker tries to use a different system
- The analytic application automatically extends its scope
The second challenge of analytic applications is integration with the heterogeneous source
systems. The claim of analytic applications is that they are more cost effective than data
warehousing systems. This claim assumes that data issues such as integration, cleansing, etc. are
not the main responsibility of the analytic application. Data issues are the main reason for
failure in analytic projects. So one end of the spectrum is several applications from several
vendors for different business and decision processes, and the other end of the spectrum is one
single application for all. It is crucial for a given organization to decide where to position
themselves in this spectrum of applications. This decision is based on the human and capital
resources and the urgency for the application. It's key that every organization have a framework
for information technology and decision-making. After that, the acquisitions for technology and
knowledge must fit or be able to be adapted to the existing framework. Black box applications do
not fit in this criteria and the total cost of ownership may be very high. This doesn't mean that
all the components of the application must be shared, but the data collection mechanisms and data
storage must be open and must allow for integration with existing data marts.
In summary, with all the pros and cons, analytic applications offer value to organizations with
decision issues. They differ from business applications in that the level of involvement during
implementation is far beyond typical IT involvement. Although analytic applications come with a set
of predefined metrics, some custom metrics might still have to be defined and the exercise of
identifying key performance indicators from the long list of metrics must be carried out very
carefully and thoroughly. Integration of the analytic application with the overall business
intelligence framework is another key factor in securing a positive return on investment. To this
end, the analytic application must be "open." Ultimately, the main reason anyone needs a business
intelligence solution is to ensure competitive advantage. And for a fraction of a major scale
business intelligence implementation cost, analytic applications provide insight and decision
support capabilities given that the core functionalities mentioned previously exist in the
application.
Mehmet T. Oguz is the architect for the Pricing Data Mart and Analytics at Zilliant, an Austin,
Texas-based pricing and revenue management company. Prior to Zilliant, Oguz designed and
implemented business intelligence solutions for several Fortune 500 organizations as a consultant
and systems integrator. He can be reached at mtoguz@yahoo.com.
Fig.1 Processes and applications
Fig. 2 Analytic Application Interactions
This was first published in December 2002