Decision Process Improvement (DPI) is an idea that I think goes hand in hand with the development of linked data. The very realities of the Semantic Web, the feasibility of totally integrated data across the enterprise must necessarily be a paradigm shift for the way decisions are made in business. The assembly of knowledge assets and the business intelligence that will be derived will cause us to rethink and improve those processes that lead to how we make many decisions throughout a business day.

Decision Support Systems (DSS) have been around a long time, as have data warehousing & mining, OLAP, executive decision support systems and enterprise resource planning applications to name a few to help us get through a day in business. DSSs are a class of information systems that in many ways are processes toward decision making. And, if there is any universally accepted taxonomy of a DSS, I would like to hear about it. The basic classifications are passive, active and cooperative systems. Then, there are the different modes of assistance that are required:

1. A model-driven DSS emphasizes access to and manipulation of a statistical, financial, optimization, or simulation model.

2. A communication-driven DSS supports more than one person working on a shared task.

3. A data-driven DSS or data-oriented DSS emphasizes access to and manipulation of a time series of internal company data and, sometimes, external data.

4. A document-driven DSS manages, retrieves, and manipulates unstructured information in a variety of electronic formats.

5. A knowledge-driven DSS provides specialized problem-solving expertise stored as facts, rules, procedures, or in similar structures.

Other means of supporting decision processes include reports, dashboards and scorecards.

Now a new factor comes into play, that of structured data. All the previous aforementioned support systems pull information resources from distributed repositories or silos. The trouble though is no business has ever achieved the holy grail of IS and that is a complete integration of data across the enterprise in terms of interoperability. Consequently, while each has its purpose they all have drawbacks as a result. Most of the data lacks structure and semantics.

As more firms take initiatives to structure their data internally, machine to machine processing and search for knowledge across this integration will be offering up this shift putting pressure on the way we think about DSS. This thought process is Decision Process Improvement in the form of new tools that may cause the marriage between some of the existing assistant models.