Ad-Hoc Query: Any spontaneous or unplanned question or query. It is a query that consists of dynamically constructed SQL and is one capability in a data-driven DSS.
Aggregate Data: Structured data that results from applying a process to more detailed data—data that is summarized or averaged.
Alerts: A notification from an event that a trigger has exceeded a predefined threshold. Alerts are used with data-driven DSS.
Algorithm: A set of rules for calculating results or solving problems that have been programmed for use in a model-driven DSS.
Analytics: A broad term that includes quantitative analysis of data and building quantitative models. Analytics is the science of analysis and discovery. Analysis may process data from a data warehouse, may result in building model-driven DSS or may occur in a special study using statistical or data mining software. In general, analytics refers to quantitative analysis and manipulation of data.
Big Data: A term used to describe large sets of structured and unstructured data. Data sets are continually increasing in size and may grow too large for traditional storage and retrieval. Data may be captured and analyzed as it is created and then stored in files.
Business Activity Monitoring (BAM): BAM is a real-time version of business performance monitoring and operational BI and is a data-driven DSS.
Business Analytics: Application of analytical tools to business questions. Business Analytics focuses on developing insights and understanding related to business performance using quantitative and statistical methods. Business Analytics includes Business Intelligence and Reporting.
Business Intelligence: BI is a popularized, umbrella term that describes a set of concepts and methods used to improve business decision making by using fact-based support systems. The term is sometimes used interchangeably with briefing books and executive information systems. A Business Intelligence system is a data-driven DSS.
Business Process Management (BPM): Using a specialized information system to improve business processes such as planning and forecasting to help managers define, measure, and manage performance against strategic goals. Management translates goals into key performance indicators (KPIs) that are monitored using computerized systems. A computer-based dashboard is a BPM or corporate performance management (CPM) tool.
Business Rules: If-then statements of business policies and guidelines. Rules specify operations, definitions, and constraints. The rules are in a syntax that can be included in a computerized system and should be understandable to managers.
Client–server architecture: A network architecture in which computers on a network act as a server managing files and network services, or as a client where users run applications and access servers.
Cognitive Overload: A psychological phenomenon characterized by an excessive amount of information for a decision maker. The amount of information exceeds the person’s cognitive capacity. DSS can reduce or increase cognitive overload.
Communications-Driven DSS: A decision support system that uses network and communications technologies to facilitate collaboration, communication, and decision making.
Computer Supported Special Study: Use of general purpose computer software tools like Excel or a data mining tool for analyzing specific questions that are nonroutine and unstructured.
Cost–Benefit Analysis: A tool used in decision support special studies that can assist in the allocation of capital. Cost–Benefit Analysis is a systematic, quantitative method for assessing the life cycle costs and benefits of competing alternatives. One identifies both tangible and intangible costs and benefits.
Cycle Time: The time interval required to complete a task or function. A cycle starts with the beginning of the first step in a process and ends with the completion of the final step.
Dashboard: A display of data in a simple visual format. A visualization tool that provides graphic depictions of current KPIs. Data displayed may be real time or historical.
Data: Atomic facts, text, graphics, images, sound, analog or digital live-video segments that are in a form that can be processed by a computer. Data is the raw material of an information system supplied by data producers and is used by managers and analysts to create information.
Data Mart: A focused collection of operational data that is usually confined to a specific aspect or subject of a business such as customers, products, or suppliers. It is a more focused decision support data store than a data warehouse.
Data Mining: A class of analytical applications that help users search for hidden patterns in a data set. Data mining is a process of analyzing large amounts of data to identify data–content relationships. Data mining is one tool used in decision support special studies. This process is also known as data surfing or knowledge discovery.
Data Visualization: Presenting data and summary information using graphics, animation, and three-dimensional displays. Tools for visually displaying information and relationships often using dynamic and interactive graphics.
Data Warehouse: A very large database designed to support decision making in organizations. It is usually batch updated and structured for rapid online queries and managerial summaries. A data warehouse is a subject-oriented, integrated, time-variant, nonvolatile collection of data.
Data-Driven DSS: A category or type of DSS that emphasizes access to and manipulation of 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 decision support functionality. Data warehouse systems often provide additional functionality. Analytical processing provides the highest level of functionality and decision support linked to analysis of large collections of historical data. Some data-driven DSS use real-time data to assist in operational performance monitoring.
Decision Automation: This broad term refers to computerized systems that make decisions and have some capability to independently act upon them. Decision automation refers to using technologies including computer processing to make decisions and implement programmed decision processes.
Decision Making Support: Using information technology to assist in one or more steps in the process of gathering and evaluating information about a situation, identifying a need for a decision, identifying or in other ways defining relevant alternative courses of action, choosing the “best,” the “most appropriate,” or the “optimum” action, and then applying the solution and choice in the situation.
Decision Scientist: A professional trained in quantitative and decision aiding tools and techniques. Decision scientists are experts in management science and database methods and tools. A decision scientist analyzes and implements computational rules related to business and organization activity. A decision scientist applies quantitative and behavioral methods to problems.
Decision Support: A broad, general concept that prescribes using computerized systems and other tools to assist in individual, group, and organization decision making.
Decision Support System (DSS): A DSS is an interactive computer-based system or subsystem intended to help decision makers use communications technologies, data, documents, knowledge or models, to identify and solve problems, complete decision process tasks, and make decisions.
Document-Driven DSS: A computerized support system that integrates a variety of storage and processing technologies to provide complete document retrieval and analysis to assist in decision making.
Drill Down/Up: An analytical technique that lets a DSS user navigate among levels of data ranging from the most summarized (up) to the most detailed (down).
Enterprise Decision Management (EDM): Automating operational decisions using business rules software with predictive analytics.
Enterprise-Wide DSS: A DSS that is broadly useful in an organization. It is usually a data-driven DSS that supports a large group of managers in a networked client–server environment with a specialized data warehouse as part of the DSS architecture.
Exception Reporting: A reporting philosophy and approach that involves only identifying unanticipated, abnormal, or anomalous information. Reports are designed to display significant exceptions in results and data. The idea is to “flag” important information and bring it quickly to the attention of a decision maker. Exception reporting can be implemented in any type of DSS, but it is particularly useful in data-driven DSS.
Executive Information Systems (EIS): A computerized system intended to provide current and appropriate information to support decision making for executives. EIS offer strong reporting and drill-down capabilities.
Explicit Knowledge: Knowledge that can be codified, such as plans, customer preferences, specifications, manuals, instructions for assembling components, and can be stored in a document-driven or knowledge-driven DSS.
Information: Data that has been processed to create meaning. Information is intended to expand the knowledge of the person who receives it. Information is the output of decision support and information systems.
Inter-Organizational DSS: A DSS that serves a company’s organizational stakeholders including customers and suppliers.
Knowledge: A collection of specialized facts, procedures, and judgment rules. Knowledge refers to what one knows and understands. Knowledge is categorized as unstructured, structured, explicit, or implicit. What we know we know we call explicit knowledge. Knowledge that is unstructured and understood, but not clearly expressed, we call implicit knowledge.
Knowledge-Driven DSS: A type of DSS that can suggest or recommend actions to managers. These systems store and help users apply knowledge for a specific problem.
Knowledge Management (KM): Knowledge management promotes activities and processes to acquire, create, document, and share formal explicit knowledge and informal implicit knowledge. Knowledge management involves identifying a group of people who have a need to share knowledge, developing technological support that enables knowledge sharing, and creating a process for transferring and disseminating knowledge.
Knowledge Management System (KMS): KMS can store and manage information in a variety of electronic formats. The software may assist in knowledge capture, categorization, deployment, inquiry, discovery, or communication. Document-driven DSS and knowledge-driven DSS are Knowledge Management Systems.
Model-Driven DSS: A category or type of DSS that emphasizes access to and manipulation of algebraic, financial, optimization, or simulation models.
On-Line Analytical Processing (OLAP): OLAP is software for manipulating multidimensional data from a variety of sources that has been stored in a data warehouse. The software can create various views and representations of the data. OLAP software provides fast, consistent, interactive access to shared, multidimensional data.
Operational Business Intelligence: Operational BI provides time-sensitive, relevant information to operations managers and frontline, customer-facing employees to support daily work processes. These data-driven DSS differ from other DSS in terms of purpose, targeted users, data latency, data detail, and availability.
Predictive Analytics: A general term for using simple and complex models to predict what will happen to support decision making. A process of using a quantitative model and current real-time or historical data to generate a score that is predictive of future behavior. Statistical analysis of historical data identifies a predictive model to support a specific decision task.
Scenario Analysis: A scenario analysis involves changing parameters in a model and then examining the results. A tool that helps a user explore different scenarios by changing a range of input values.
Semistructured Decision Situation: Some factors related to a decision or choice are structured while others are unstructured. Only some factors are known and can be specified.
Sensitivity Analysis: An analysis that involves calculating a decision model multiple times with different inputs so a modeler can analyze the alternative results.
Specific Decision Support System: A computer-based system that has a narrow decision-making purpose. The system helps a person accomplish a particular decision-making task.
Structured Decision Situation: A routine or standardized decision where factors are identifiable and solution techniques are known and available. The structural elements in the situation, for example, alternatives, criteria, environmental variables, are known, defined, and understood. Results can be measured and the problem is amenable to quantitative analysis.
Unstructured Decision Situation: A complex set of factors and no standard solutions exist for resolving the situation. Some or all of the structural elements of the decision situation are undefined, ill-defined, or unknown. For example, goals may be poorly defined, alternatives may be incomplete or incomparable, choice criteria may be hard to measure or difficult to link to goals.
User Interface: A component of a computerized system that provides communication and interaction between a system and its user. This component is also called the dialogue component or human to computer interface. An interface is a set of commands or menus through which a user communicates with a program.
Virtual World: An immersive three-dimensional virtual space where one’s avatar interacts with a computer-simulated world. Some people only associate virtual worlds with games, but such environments can be used for decision support.
What-If Analysis: The capability of “asking” the software package what the effect will be of changing some of the input data or independent variables.