Decision Support Systems (DSS) are a set of computerized data systems that assist with certain decision-making processes. They are dynamic computer-based systems and subsystems that come in a variety of shapes and sizes. The document-driven DSS is among them.
DSS can transmit messages in graphical representations and include support system for artificial intelligence (AI). This could be the goal of corporate leaders or other potential domain experts. The systems and subsystems are used to assist decision-makers in completing tasks using data, expertise, communication technologies, and models.
The decision support systems can accumulate and convey a lot of content, including comparative data figures, accessing data assets, the outcomes of various decision alternatives, previous experiences in a specific context, and projecting statistics based on speculation or relevant data.
Communication-driven DSS, knowledge-driven DSS, model-driven DSS, data-driven DSS, and document-driven DSS are some of the various sorts of DSS. Essentially, the document-driven kind is the most popular intelligent decision support system application, with a large customer base. Its primary purpose is to explore online pages and documents for a certain collection of keywords or concepts. Setting up a DSS through a client or server system, or through the web, is a standard technology method.
Unlike data-driven DSS, which depends on available data in a defined format that presents itself to database analysis and retention, document-driven DSS will utilize difficult data to store or standardize. Recorded conversations, streaming videos, and textual data are the three principal types of data used in the document-driven DSS.
Since none of these data formats offer themselves easily to standardized database storage and analysis, administrators will need DSS data retention management software to turn the data into something useful in the corporate decision-making workflow.
