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  • Casual Articles - Decision Support Systems, Part 4 – DSS Evolution

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    loped and matured, as the Business evaluates the benefit derived of each investment:
    • Processes to extract data from operational systems, mature and become more automated
    • Additional business processes are selected to be monitored. The data warehouse is ex
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      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-?-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
      If a Business aims at developing predictive capability to support the evaluation of future actions, it shall prioritize the development of such models, even without the implementation of a data warehouse, just by using files or data extracted from operational systems. All DSS functionality categories are developed and matured, as the Business evaluates the benefit derived of each investment:
      • Processes to extract data from operational systems, mature and become more automated
      • Additional business processes are selected to be monitored. The data warehouse is ext
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      • 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
      If a Business aims at developing predictive capability to support the evaluation of future actions, it shall prioritize the development of such models, even without the implementation of a data warehouse, just by using files or data extracted from operational systems. All DSS functionality categories are developed and matured, as the Business evaluates the benefit derived of each investment:
      • Processes to extract data from operational systems, mature and become more automated
      • Additional business processes are selected to be monitored. The data warehouse is ex
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        ess 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
      If a Business aims at developing predictive capability to support the evaluation of future actions, it shall prioritize the development of such models, even without the implementation of a data warehouse, just by using files or data extracted from operational systems. All DSS functionality categories are developed and matured, as the Business evaluates the benefit derived of each investment:
      • Processes to extract data from operational systems, mature and become more automated
      • Additional business processes are selected to be monitored. The data warehouse is ex
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        • Processes to extract data from operational systems, mature and become more automated
        • Additional business processes are selected to be monitored. The data warehouse is ex
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          loped and matured, as the Business evaluates the benefit derived of each investment:
          • 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
          At the same time, a new culture on information quality and information driven decision making, is cultivated. The Business is gradually deriving more value from the DSS system, given that:

          • 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
          Copyright 2006 – Kostis Panayotakis

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