Writen by Kostis Panayotakis
The evolution path of a DSS system, varies according to the priorities of each Business.
Development and maturity stages of a DSS system, are the following:
- Initially, certain critical business processes are selected to be monitored vis-à-vis their performance
- a data quality mechanism is developed. In order to produce quality information, quality input data are needed.
- A data warehouse which can produce multidimensional views of the selected business processes, is implemented
- Standard business process performance reports, are developed
- Tools for the analysis of multidimensional data (OLAP tools) and the ad-hoc analysis of data, are used
- Predictive models on areas of interest are developed
- Processes to extract data from operational systems, mature and become more automated
- Additional business processes are selected to be monitored. The data warehouse is extended with facts on the additional business processes
- New ways to analyze and drill down on the enriched data
- New reports which combine data from different process areas (drill across mechanism) are developed
- Quality of predictive models (the ability to produce a sufficient level of prediction) is evaluated based on the business results. Models are improved based on the feedback received.
- Predictive capability on new business areas is developed
- quality of data stored in the data warehouse is gradually improving
- larger subject areas are supported by the DSS
- a higher number of Users is accessing and using the information which is produced
- information is gradually perceived as more reliable and a single version of truth is gradually achieved
Material relevant to business intelligence can be found at http://www.pleroforea.com Kostis Panayotakis - http://www.pleroforea.com/Kostis_Panayotakis.htm |
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