 | This articleneeds additional citations forverification. Please helpimprove this article byadding citations to reliable sources. Unsourced material may be challenged and removed. Find sources: "Decision intelligence" – news ·newspapers ·books ·scholar ·JSTOR(June 2019) (Learn how and when to remove this message) |
Decision intelligence is an engineering discipline that augmentsdata science with theory fromsocial science,decision theory, andmanagerial science. Its application provides a framework forbest practices in organizationaldecision-making and processes for applying computational technologies such asmachine learning, natural language processing, reasoning, and semantics 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 ofcause and effect, anddecision modeling is a visual language for representing these chains.[1]
A related field,decision engineering, also investigates the improvement of decision-making processes but is not always as closely tied to data science.[Note]
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 aroundcomplexity theory and organizations.
In this sense, decision intelligence represents a practical application of the field ofcomplex 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 advancedanalytics andmachine 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 inblack swan theory.[citation needed]
Decision intelligence proponents believe that many organizations continue to make poor decisions.[2] In response, decision intelligence seeks to unify a number of decision-makingbest practices, described in more detail below.
Decision intelligence builds on the insight that it is possible todesign the decision itself, using principles previously used for designing more tangible objects like bridges and buildings.
The use of avisual design language representing decisions (see§ Visual decision design) is an important element of decision intelligence, since it provides an intuitive common language readily understood by all decision participants. Avisual metaphor improves the ability to reason about complex systems[3] as well as to enhancecollaboration.
In addition to visual decision design, there are other two aspects of engineering disciplines that aid mass adoption. These are:
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 astelecommunications,media, theautomotive industry, andpharmaceuticals.
Examples:
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, July 2008
Unlike otherdecision making tools and methodologies, decision intelligence seeks to bring to bear a number ofengineering practices to the process of creating a decision. These includerequirements analysis,specification,scenario planning,quality assurance,security, and the use ofdesign 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 likebusiness dashboards andassumption 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.[4]
Although many elements of decision intelligence, such assensitivity analysis andanalytics, are mature disciplines, they are not in wide use by decision makers.[5][failed verification] 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 acausal loop diagram), data flow (as in adata 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, includemachine learning andanalytics algorithms (includingartificial neural networks), as well as more traditionalregression analysis. Results fromoperations research and many other quantitative approaches have a similar role to play.
When data is not available (or is toonoisy, uncertain, or incomplete), these dependency links can take on the form of rules as might be found in anexpert system orrule-based system, and so can be obtained throughknowledge 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 usingmachine learning. In this respect, decision intelligence can be seen as a "multi-link" extension toartificial intelligence, 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. In this way, decision intelligence unifiescomplex systems,machine learning, anddecision analysis.
| This sectionneeds additional citations forverification. Please helpimprove this article byadding citations to reliable sources in this section. Unsourced material may be challenged and removed.(January 2012) (Learn how and when to remove this message) |
Despite decades of development ofdecision support system and methodologies (likedecision analysis), these are still less popular thanspreadsheets 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 oncognitive bias and decision-making,situational awareness,critical andcreative thinking, collaboration and organizational design, with engineering technologies.
Decision intelligence is considered an improvement upon current organizational decision-making practices, which include the use ofspreadsheets, 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 commonblueprint 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, usinglogic to support an argument, the necessity ofcritical thinking to evaluate a decision, and understanding the impacts ofbias—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 2018, Google's processes and training programs in applied data science were renamed to "decision intelligence"[6] 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.[7]
Modern decision intelligence is highly interdisciplinary and academically inclusive. Research centering on decisions, defined broadly as biological and nonbiologicalaction 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 likemind mapping,conceptual graphs, andsemantic networks.
The basic idea is that a visual metaphor enhancesintuitive thinking,inductive reasoning, andpattern recognition—important cognitive skills usually less accessible in a verbal or text discussion.Abusiness decision map can be seen as one approach to a formal decision language to support decision intelligence.[8]
Decision intelligence recognizes that many aspects of decision-making are based on intangible elements, includingopportunity 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.
^ Note the following semantic variations: