The main value that analytic applications bring is built-in business intelligence a predefined set of metrics and key performance indicators that the business users would have to build otherwise. The analytic application will require minor modifications during deployment to suit the custom needs. By purchasing an analytic application, organizations also purchase business expertise with which they can gain valuable insight into their businesses.
Analytic applications are true "applications" that contain the key components of business intelligence systems such as ETL, standard and ad hoc queries, alerts, etc. The best analytic applications offer three additional functionality elements:
- Complex analysis
- Decision deployment
- Closed-loop analysis
Figure 1: Processes and Applications
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 complex analytics component also allows the decision-makers to perform analysis such as sensitivity and scenario analysis. The results of complex analysis are collected in the data mart along with the internal and external data. These results are presented to the decision-makers in various forms using conventional visualization methods. The decision-maker or analyst goes through a decision-making process and, when satisfied, submits recommendations to the application system. In other words, the analytic application then submits a "decision" to the operational application systems. The decision is carried out through the operational application in time.
The analytic application collects data from the operational application continually so at some point picks 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 traditional systems where system of record is transformed into organized data in the data warehouse, the conclusion of a typical analysis is rarely noted back in the system of record. In the best analytic applications, the technology and the data framework are built so 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.
Figure 2: Analytic Application Interactions
One challenge with analytic applications is that they are very focused in terms of 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 the decision-making process is such that two things are likely to happen: the decision-maker will need data from more than one business processes, or after initial analysis the decision-maker will increase the scope of analysis trying to find solutions to more complex decision issues (problems or opportunities). When either one of the scenarios happen, 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, or
- The analytic application automatically extends its scope.
The second challenge of analytic applications is integration with the heterogeneous source systems. The claim of the analytic applications is that they are more cost-effective than data warehousing systems or they can be add- ons to the existing data warehousing systems. This claim assumes that the issues with data such as integration, cleansing, etc. are not the main responsibility of the analytic application.
The data issues are very high risk and the main reason for failure in analytic projects. At one end of the spectrum are several applications from several vendors for different business and decision processes and at 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 such an application.
What’s key is for every organization to 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 it 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, which could be specific data marts, 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 organization with decision issues. They do differ from business applications because the level of involvement during implementation is far beyond typical IT involvement. Although the 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 to 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." At the end of the day, the main reason why anyone needs a business intelligence solution (whether end-to-end or process-specific analytic applications) is to ensure competitive advantage. For a fraction of a major scale business intelligence implementation cost, analytic applications do provide insight and decision support capabilities given that the core functionality previously mentioned does exist in the application.
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