Page 204 - DMGT505_MANAGEMENT_INFORMATION_SYSTEM

Unit 10: Enhancing Decision Making for the Digital Firm

Data-Driven DSS Notes

Data-Driven DSS take the massive amounts of data available through the company’s TPS and
MIS systems and cull from it useful information which executives can use to make more informed
decisions. They don’t have to have a theory or model but can “free-flow” the data.
The first generic type of Decision Support System is a Data-Driven DSS. These systems include
file drawer and management reporting systems, data warehousing and analysis systems, Executive
Information Systems (EIS) and Spatial Decision Support Systems. Business Intelligence Systems
are also examples of Data-Driven DSS. Data- Driven DSS emphasize access to and manipulation
of large databases of structured data and especially a time-series of internal company data and
sometimes external data. Simple file systems accessed by query and retrieval tools provide the
most elementary level of functionality. Data warehouse systems that allow the manipulation of
data by computerized tools tailored to a specific task and setting or by more general tools and
operators provide additional functionality. Data-Driven DSS with Online Analytical Processing
(OLAP) provide the highest level of functionality and decision support that is linked to analysis
of large collections of historical data.

Model-Driven DSS

A second category, Model-Driven DSS, includes systems that use accounting and financial models,
representational models, and optimization models. Model-Driven DSS emphasize access to and
manipulation of a model. Simple statistical and analytical tools provide the most elementary
level of functionality. Some OLAP systems that allow complex analysis of data may be classified
as hybrid DSS systems providing modeling, data retrieval and data summarization functionality.
Model-Driven DSS use data and parameters provided by decision-makers to aid them in
analyzing a situation, but they are not usually data intensive. Very large databases are usually
not needed for Model-Driven DSS.

Model-Driven DSS were isolated from the main Information Systems of the organization and
were primarily used for the typical “what-if” analysis. That is, “What if we increase production
of our products and decrease the shipment time?” These systems rely heavily on models to help
executives understand the impact of their decisions on the organization, its suppliers, and its
customers.
Knowledge-Driven DSS

The terminology for this third generic type of DSS is still evolving. Currently, the best term
seems to be Knowledge- Driven DSS. Adding the modifier “driven” to the word knowledge
maintains a parallelism in the framework and focuses on the dominant knowledge base
component. Knowledge-Driven DSS can suggest or recommend actions to managers. These DSS
are personal computer systems with specialized problem-solving expertise. The “expertise”
consists of knowledge about a particular domain, understanding of problems within that domain,
and “skill” at solving some of these problems. A related concept is Data Mining. It refers to a
class of analytical applications that search for hidden patterns in a database.

Did u know? What is data mining?
Data mining is the process of sifting through large amounts of data to produce data
content relationships.

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