Decision support systems are a subset of business intelligence aimed at helping organizations make informed business decisions based on vast troves of analyzed data.
A decision support system (DSS) is an interactive information system that analyzes large volumes of data for informing business decisions. A DSS supports the management, operations, and planning levels of an organization in making better decisions by assessing the significance of uncertainties and the tradeoffs involved in making one decision over another.
A DSS leverages a combination of raw data, documents, personal knowledge, and/or business models to help users make decisions. The data sources used by a DSS could include relational data sources, cubes, data warehouses, electronic health records (EHRs), revenue projections, sales projections, and more.
The concept of DSS grew out of research conducted at the Carnegie Institute of Technology in the 1950s and 1960s, but really took root in the enterprise in the 1980s in the form of executive information systems (EIS), group decision support systems (GDSS), and organizational decision support systems (ODSS). With organizations increasingly focused on data-driven decision making, decision science (or decision intelligence) is on the rise, and decision scientists may be the key to unlocking the potential of decision science systems. Bringing together applied data science, social science, and managerial science, decision science focuses on selecting between options to reduce the effort required to make higher-quality decisions.
Decision support systems are used in a broad array of industries. Example uses include:
DSS and business intelligence (BI) are often conflated. Some experts consider BI a successor to DSS. Decision support systems are generally recognized as one element of business intelligence systems, along with data warehousing and data mining.
Whereas BI is a broad category of applications, services, and technologies for gathering, storing, analyzing, and accessing data for decision-making, DSS applications tend to be more purpose-built for supporting specific decisions. For example, a business DSS might help a company project its revenue over a set period by analyzing past product sales data and current variables. Healthcare providers use clinical decision support systems to make the clinical workflow more efficient: computerized alerts and reminders to care providers, clinical guidelines, condition-specific order sets, and so on.
Research firm, Gartner, declared decision intelligence a top strategic technology trend for 2022. Decision intelligence seeks to update and reinvent decision support systems with a sophisticated mix of tools including artificial intelligence (AI) and machine learning (ML) to help automate decision-making. According to Gartner, the goal is to design, model, align, execute, monitor, and tune decision models and processes.
In the book Decision Support Systems: Concepts and Resources for Managers, Daniel J. Power, professor of management information systems at the University of Northern Iowa, breaks down decision support systems into five categories based on their primary sources of information.
Data-driven DSS. These systems include file drawer and management reporting systems, executive information systems, and geographic information systems (GIS). They emphasize access to and manipulation of large databases of structured data, often a time-series of internal company data and sometimes external data.
Model-driven DSS. These DSS include systems that use accounting and financial models, representational models, and optimization models. They emphasize access to and manipulation of a model. They generally leverage simple statistical and analytical tools, but Power notes that some OLAP systems that allow complex analysis of data may be classified as hybrid DSS systems. Model-driven DSS use data and parameters provided by decision-makers, but Power notes they are usually not data-intensive.
Knowledge-driven DSS. These systems suggest or recommend actions to managers. Sometimes called advisory systems, consultation systems, or suggestion systems, they provide specialized problem-solving expertise based on a particular domain. They are typically used for tasks including classification, configuration, diagnosis, interpretation, planning, and prediction that would otherwise depend on a human expert. These systems are often paired with data mining to sift through databases to produce data content relationships.
Document-driven DSS. These systems integrate storage and processing technologies for document retrieval and analysis. A search engine is an example.
Communication-driven and group DSS. Communication-driven DSS focuses on communication, collaboration, and coordination to help people working on a shared task, while group DSS (GDSS) focuses on supporting groups of decision makers to analyze problem situations and perform group decision-making tasks.
According to Management Study HQ, decision support systems consist of three key components: the database, software system, and user interface.
According to Capterra, the popular decision support system software includes: