Decision intelligence is an engineering discipline that augments data science with theory from social science, decision theory, and managerial science. Its application provides a framework for best practices in organizational decision-making and processes for applying machine learning at scale. The basic idea is that decisions are based on our understanding of how actions lead to outcomes. Decision intelligence is a discipline for analyzing this chain of cause-and-effect, and decision modeling is a visual language for representing these chains.
Decision Intelligence is based on the recognition that, in many organizations, decision making could be improved if a more structured approach were used. Decision intelligence seeks to overcome a decision making "complexity ceiling", which is characterized by a mismatch between the sophistication of organizational decision making practices and the complexity of situations in which those decisions must be made. As such, it seeks to solve some of the issues identified around complexity theory and organizations.
In this sense, decision intelligence represents a practical application of the field of complex systems, which helps organizations to navigate the complex systems in which they find themselves. Decision intelligence can also be thought of as a framework that brings advanced analytics and machine learning techniques to the desktop of the non-expert decision maker, as well as incorporating, and then extending, data science to overcome the problems articulated in Black swan theory.
Decision intelligence proponents believe that many organizations continue to make poor decisions. In response, decision intelligence seeks to unify a number of decision making best practices, described in more detail below.
Decision intelligence builds on the insight that it is possible to design the decision itself, using principles previously used for designing more tangible objects like bridges and buildings.
The use of a visual design language representing decisions is an important element of decision intelligence, since it provides an intuitive common language readily understood by all decision participants. A visual metaphor improves the ability to reason about complex systems as well as to enhance collaboration.
In addition to visual decision design, there are other two aspects of engineering disciplines that aid mass adoption. These are: 1) the creation of a shared language of design elements and 2) the use of a common methodology or process, as illustrated in the diagram above.
A list of decision intelligence videos and related resources can be found at.
The need for a unified methodology of decision making is driven by a number of factors that organizations face as they make difficult decisions in a complex internal and external environment.
Recognition of the broad-based inability of current methods to solve decision making issues in practice comes from several sources, including government sources and industries such as telecommunications, media, the Automotive industry , and pharmaceuticals.
The car is becoming an expression of identity, values, and personal control in ways that move far beyond traditional segmentation and branding. For example, fuel efficiency will be only one consideration for a socially responsible vehicle (SRV). What percent of the parts are recyclable? What is the vehicle's total carbon footprint? Are there child labor inputs? Toxic paints, glues, or plastics? How transparent is the supply chain? Is the seller accountable for recycling? What methods are used? Are fair labor practices employed?— Shoshana Zuboff, The GM Solution: Life Boats, Not Life Support. Business Week, November 18, 2008
We live in a dynamic world in which the pace, scope, and complexity of change are increasing. The continued march of globalization, the growing number of independent actors, and advancing technology have increased global connectivity, interdependence and complexity, creating greater uncertainties, systemic risk and a less predictable future. These changes have led to reduced warning times and compressed decision cycles.— Director of National Intelligence, Vision 2015: A Globally Networked and Integrated Intelligence Enterprise Also see this Vision 2015 summary
Unlike other decision making tools and methodologies, decision intelligence seeks to bring to bear a number of engineering practices to the process of creating a decision. These include requirements analysis, specification, scenario planning, quality assurance, security, and the use of design principles as described above. During the decision execution phase, outputs produced during the design phase can be used in a number of ways; monitoring approaches like business dashboards and assumption based planning are used to track the outcome of a decision and to trigger replanning as appropriate (one view of how some of these elements combine is shown in the diagram at the start of this article).
Decision intelligence has the potential to improve the quality of decisions made, the ability to make them more quickly, the ability to align organizational resources more effectively around a change in decisions, and lowers the risks associated with decisions. Furthermore, a designed decision can be reused and modified as new information is obtained. 
Although many elements of decision intelligence, such as Sensitivity analysis and analytics, are mature disciplines, they are not in wide use by decision makers. Decision intelligence seeks to create a visual language that serves to facilitate communication between them and quantitative experts, allowing broader utilization of these and other numerical and technical approaches.
In particular, dependency links in a decision model represent cause-and-effect (as in a causal loop diagram), data flow (as in a data flow diagram), or other relationships. As an example, one link might represent the connection between "mean time to repair a problem with telephone service" and "customer satisfaction", where a short repair time would presumably raise customer satisfaction. The functional form of these dependencies can be determined by a number of approaches. Numerical approaches, which analyze data to determine these functions, include machine learning and analytics algorithms (including artificial neural networks), as well as more traditional regression analysis. Results from operations research and many other quantitative approaches have a similar role to play.
When data is not available (or is too noisy, uncertain, or incomplete), these dependency links can take on the form of rules as might be found in an expert system or rule-based system, and so can be obtained through knowledge engineering.
In this way, a decision model represents a mechanism for combining multiple relationships, as well as symbolic and subsymbolic reasoning, into a complete solution to determining the outcome of a practical decision.
As described above, decision model dependency links can be modeled using machine learning. In this respect, decision intelligence can be seen as a "multi-link" extension to AI, which is most widely used for single-link analysis. From this point of view, machine learning can be viewed as answering the question "If I know/see/hear X, what can I conclude?", whereas decision intelligence answers: "If I take action X, what will be the outcome?". The latter question usually involves chains of events, sometimes including complex dynamics like feedback loops. From this point of view, Decision Intelligence unifies complex systems, machine learning, and decision analysis.
Despite decades of development of decision support system and methodologies (like decision analysis), these are still less popular than spreadsheets as primary tools for decision making. Decision intelligence seeks to bridge this gap, creating a critical mass of users of a common methodology and language for the core entities included in a decision, such as assumptions, external values, facts, data, and conclusions. If a pattern from previous industries holds, such a methodology will also facilitate technology adoption, by clarifying common maturity models and road maps that can be shared from one organization to another.
The decision intelligence approach is multidisciplinary, unifying findings on cognitive bias and decision making, situational awareness, critical and creative thinking, collaboration and organizational design, with engineering technologies.
Decision intelligence is considered an improvement upon current organizational decision making practices, which include the use of spreadsheets (difficult to QA, hard to collaborate and discuss), text (sequential in nature, so is not a good fit for how information flows through a decision structure), and verbal argument. The movement from these largely informal structures to one in which a decision is documented in a well understood, visual language, echoes the creation of common blueprint methodologies in construction, with promise of similar benefits.
Decision intelligence is both a very new and also a very old discipline. Many of its elements—such as the language of assessing assumptions, using logic to support an argument, the necessity of critical thinking to evaluate a decision, and understanding the impacts of bias—are ancient. Yet the realization that these elements can form a coherent whole that provides significant benefits to organizations by focusing on a common methodology is relatively new.
In 2013, Lorien Pratt and Mark Zangari, founders of Decision intelligence vendor Quantellia, chose to re-brand their offering from the name "Decision engineering" to "Decision intelligence" for marketing reasons. Their rationale was that the word "Engineering" connoted a discipline too technical for management professionals. Also, Quantellia's projects were increasingly integrating Artificial intelligence (AI), Business Intelligence (BI) and Collaborative intelligence (CI), so "DI" was a natural extension to this group.
In 2018, Google's processes and training programs in applied data science were renamed "Decision intelligence" to indicate the central role of actions and decisions in the application of data science. The extent to which the theoretical frameworks drew on the managerial and social sciences in addition to data science was an additional motivator for unifying decision intelligence into a field of study that is distinct from data science.
Modern decision intelligence is highly interdisciplinary and academically inclusive. Research centering on decisions, defined broadly as biological and nonbiological action selection, is considered part of the discipline. Decision intelligence is not an umbrella term for data science and social science, however, since it does not cover components unconcerned with decisions.
Because it makes visible the otherwise invisible reasoning structures used in complex decisions, the design aspect of decision intelligence draws from other conceptual representation technologies like mind mapping, conceptual graphs, and semantic networks.
The basic idea is that a visual metaphor enhances intuitive thinking, inductive reasoning, and pattern recognition—important cognitive skills usually less accessible in a verbal or text discussion. A business decision map can be seen as one approach to a formal decision language to support decision intelligence. See, e.g., Waring, 2010.
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. Value network analysis—most notably Value network maps—are therefore relevant here.