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While "data based decision-making" is a more common term, "data-informed decision-making" is the preferred term, since decisions should not be based solely on quantitative data. [1] [3] Data-driven decision-making is commonly used in the context of business growth and entrepreneurship. [4] [5] Many educators have access to data system for ...
Data-driven models encompass a wide range of techniques and methodologies that aim to intelligently process and analyse large datasets. Examples include fuzzy logic, fuzzy and rough sets for handling uncertainty, [3] neural networks for approximating functions, [4] global optimization and evolutionary computing, [5] statistical learning theory, [6] and Bayesian methods. [7]
Decision intelligence recognizes that many aspects of decision-making are based on intangible elements, including opportunity costs, employee morale, intellectual capital, brand recognition and other forms of business value that are not captured in traditional quantitative or financial models.
Improved decision-making: By providing a single version of the truth, MDM aims to have business leaders make informed, data-driven decisions, and improve overall business performance. Operational efficiency : With consistent and accurate data, operational processes such as reporting, inventory management, and customer service become more efficient.
However, data has staged a comeback with the popularisation of the term big data, which refers to the collection and analyses of massive sets of data. While big data is a recent phenomenon, the requirement for data to aid decision-making traces back to the early 1970s with the emergence of decision support systems (DSS).
A decision support system (DSS) is an information system that supports business or organizational decision-making activities. DSSs serve the management, operations and planning levels of an organization (usually mid and higher management) and help people make decisions about problems that may be rapidly changing and not easily specified in advance—i.e., unstructured and semi-structured ...
Automated decision-making involves using data as input to be analyzed within a process, model, or algorithm or for learning and generating new models. [7] ADM systems may use and connect a wide range of data types and sources depending on the goals and contexts of the system, for example, sensor data for self-driving cars and robotics, identity data for security systems, demographic and ...
A case is similar to the business entity concept in many respects. Wang and Kumar [7] proposed the document-driven workflow systems which is designed based on data dependencies without the need for explicit control flows. Muller et al. [8] also introduced the framework for the data-driven modelling of large process structures, namely COREPRO ...