Businesses, governments, universities, hospitals, law enforcement agencies, and other organizations are increasingly collecting and analyzing data to inform decision making. This “big data” movement has benefited from the contributions of a number of academic disciplines, including mathematics, statistics, and computer science. The technical advances involved in big data have grown exponentially in recent years, thus contributing to its growing use by organizations, the experience of which contributes to further refinements and so forth. This cycle of technical advancement is likely to continue into the foreseeable future.
As psychological researchers, we can sit back and watch the world embrace big data and admire the speed with which this paradigm shift has taken place (indeed, it has been impressive!). We can even reap its benefits as it applies to advancing our research and practice, as the focal article (Guzzo, Fink, King, Tonidandel, & Landis, Reference Guzzo, Fink, King, Tonidandel and Landis2015) suggests. Although I think this is a prudent course of action, I also think we can—and have a responsibility to, given its rising prominence in society—be a more active participant in the big data movement.
The effectiveness of big data is not defined by its technical capabilities. There are crucial psychological aspects that need to be considered as well. We need to acknowledge that people must interface with the data and their analysis in order to integrate them into decision making. A marketing analyst may have results from a multisource data exploration of which geographic area should be targeted for an advertising campaign. An oncologist may be given a report that estimates a patient's prognosis based on genetics, blood tests, biopsy results, and a cross-referenced search in a national database for other patients with similar attributes. An intelligence analyst may have the output from an algorithm that identifies with some probability the location of a known terrorist based on satellite imagery, communications intercepts, and local reports. In these illustrative cases of big data utilization, data analysis simply provides information to a decision maker; it is up to this individual to integrate the information into a decision.
How Do Individuals Integrate Big Data Into Their Decision Making?
The question of how individuals actually integrate big data into their decision making is complex, but it is an important question and one about which we already have a number of insights from various streams of psychological research. I highlight some of these streams below with the hope of providing a springboard to encourage research that expands these theoretical areas in the context of the big data movement.
Insights From the Decision Aid Neglect Literature
As results generated by big data can help individuals in their decision making, it can therefore be considered a decision support system or decision aid (Dawes, Faust, & Meehl, Reference Dawes, Faust and Meehl1989; Sieck & Arkes, Reference Sieck and Arkes2005). Researchers have discovered that professionals, including managers, physicians, mental health clinicians, university admissions committees, parole boards, and many others are highly reluctant to use decision aids for a number of reasons (Grove & Meehl, Reference Grove and Meehl1996; Highhouse, Reference Highhouse2008). First, they tend to be overconfident in their own intuition and thereby assign a low value to the output of a decision aid (Sieck & Arkes, Reference Sieck and Arkes2005). Some eschew decision aids due to the belief that they will miss important “one-off” information cues (Grove & Meehl, Reference Grove and Meehl1996), such as a computer algorithm not being able to account for unexpected or value-based information such as political or moral considerations. Individuals have also been found to underutilize decision aids because they perceive them as dehumanizing or unfair (Dawes et al., Reference Dawes, Faust and Meehl1989), because they may wonder, for example, why they went through so much education or training if their expertise was going to ostensibly be usurped by a statistical analysis. There are also social influences that deter individuals from relying on decision aids, as studies have found that people tend to pay more attention to advice if it is based on expert judgment as compared with statistical forecasting (Onkal, Goodwin, Thomson, Gӧnül, & Pollock, Reference Onkal, Goodwin, Thomson, Gӧnül and Pollock2009). In this vein, Arkes, Shaffer, and Medow (Reference Arkes, Shaffer and Medow2007) found that individuals perceive physicians as less competent, thorough, and professional if they use a decision aid as compared with not using one. Although it may seem obvious that organizational decision makers will embrace big data, this body of work suggests otherwise. However, we do not know whether or how the decision aid neglect phenomenon applies to big data, in part because of its complexity given modern technical advances and in part because of its increased acceptance and endorsement by organizations. Nevertheless, many of the reasons identified above are core psychological processes (e.g., confidence, concern for fairness, etc.) and are thus likely to persist to some degree.
Insights From the Attribution Theory Literature
Although the above evidence indicates that individuals may underutilize big data in their decision making, other evidence from the psychological literature suggests a more nuanced perspective. Attribution theory (Weiner, Reference Weiner1986) is a widely cited framework for understanding how individuals infer causes about particular outcomes. This inference process is driven by a number of dimensions, most notably regarding whether the outcome is perceived to be due to causes that are internal (e.g., in the context of this article, one's intuitive judgment) versus external (e.g., the output of a statistical analysis). An individual with the opportunity to utilize a big data analysis for an important decision may actually reject it in order to “prove” that one's intuition or professional expertise is valid, consistent with the literature cited earlier, in order to receive credit or earn respect from others for a (hopefully) good outcome. On the contrary, individuals in this situation may instead prefer to rely heavily on the analysis in order to reserve it as a scapegoat for blame in case the decision outcome is bad. One may imagine the negative repercussions of going against the recommendation of the analysis. In support of this notion, Naquin and Kurtzberg (Reference Naquin and Kurtzberg2004) found that organizations are held less accountable for accidents when they were caused by technological failure as compared with human error. A number of moderators, such as personality, social status, and/or decision risk, may explain these two alternative possibilities—whether decision makers reject or leverage big data vis-à-vis setting the stage for a self-serving attribution of praise or blame, respectively, depending on the decision outcome.
Insights From the Intuition Literature
Analyses provided by big data purportedly allow people to make systematic and logical decisions, and therefore they are believed to help mitigate against biases and overcome the cognitive limitations of humans. On the surface, this appears to be a panacea for decision making in organizations, as we perhaps finally have a systematic method (and burgeoning norm) of quelling intuition in organizations (cf. Highhouse, Reference Highhouse2008; Tversky & Kahneman, Reference Tversky and Kahneman1974). However, the intuition literature may suggest otherwise. Researchers have documented that some experts are remarkably effective in using their intuition to make decisions and solve problems very quickly (Zsambok & Klein, Reference Zsambok and Klein2014). Intuition has been identified as a crucial process involved in creativity (Gore & Sadler-Smith, Reference Gore and Sadler-Smith2011) and moral judgment (Haidt, Reference Haidt2001). In addition, some studies suggest that organizational leaders benefit from using their intuition (Khatri & Ng, Reference Khatri and Ng2000; Sadler-Smith, Reference Sadler-Smith2004). A stream of research on “thin slice” judgments has found that, in some contexts, people can form very fast and accurate impressions of others based on a small amount of information (Ambady, Reference Ambady2010). For instance, Curhan and Pentland (Reference Curhan and Pentland2007) found that negotiation outcomes can be accurately predicted based only on the first 5 minutes of parties’ conversational dynamics. Thus, it seems that intuition may be a key driver of decision-making effectiveness in some contexts, and therefore it should perhaps be a part of the conversation of how big data informs decision making. For instance, it may be the case that analyses derived from big data can provide a useful reference against which one's intuitive judgment can be compared (see Erez & Grant, Reference Erez and Grant2014, p. 105).
Conclusion
I should reiterate that the above literatures are not the only sources of insight for sparking research on how we can get a better psychological understanding of how individuals integrate big data into their decision making. A number of other perspectives may be fruitful as well. For instance, we should consider investigating how norms of big data utilization affect the motivational and reward systems in organizations (cf. Gagné & Deci, Reference Gagné and Deci2005). As organizations are increasingly relying on teams to accomplish work, we also need to better understand how analyses generated by big data are shared and processed among individuals (cf. De Dreu, Nijstad, & van Knippenberg, Reference De Dreu, Nijstad and van Knippenberg2008). Another potential avenue of research is regarding how the growing emphasis on big data influences the power dynamics within organizations (cf. Pfeffer, Reference Pfeffer1981).
The big data movement is here, and it will continue to grow, with or without advances from psychological research. A critical piece we can—and indeed, need—to offer is a better understanding of how results from big data are integrated into decision making. As I hope to have illustrated, this is a complex issue; but, we already have a head start in identifying ways to contribute to the big data movement and ultimately to help increase the effectiveness of organizations and individuals.
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Businesses, governments, universities, hospitals, law enforcement agencies, and other organizations are increasingly collecting and analyzing data to inform decision making. This “big data” movement has benefited from the contributions of a number of academic disciplines, including mathematics, statistics, and computer science. The technical advances involved in big data have grown exponentially in recent years, thus contributing to its growing use by organizations, the experience of which contributes to further refinements and so forth. This cycle of technical advancement is likely to continue into the foreseeable future.
As psychological researchers, we can sit back and watch the world embrace big data and admire the speed with which this paradigm shift has taken place (indeed, it has been impressive!). We can even reap its benefits as it applies to advancing our research and practice, as the focal article (Guzzo, Fink, King, Tonidandel, & Landis, Reference Guzzo, Fink, King, Tonidandel and Landis2015) suggests. Although I think this is a prudent course of action, I also think we can—and have a responsibility to, given its rising prominence in society—be a more active participant in the big data movement.
The effectiveness of big data is not defined by its technical capabilities. There are crucial psychological aspects that need to be considered as well. We need to acknowledge that people must interface with the data and their analysis in order to integrate them into decision making. A marketing analyst may have results from a multisource data exploration of which geographic area should be targeted for an advertising campaign. An oncologist may be given a report that estimates a patient's prognosis based on genetics, blood tests, biopsy results, and a cross-referenced search in a national database for other patients with similar attributes. An intelligence analyst may have the output from an algorithm that identifies with some probability the location of a known terrorist based on satellite imagery, communications intercepts, and local reports. In these illustrative cases of big data utilization, data analysis simply provides information to a decision maker; it is up to this individual to integrate the information into a decision.
How Do Individuals Integrate Big Data Into Their Decision Making?
The question of how individuals actually integrate big data into their decision making is complex, but it is an important question and one about which we already have a number of insights from various streams of psychological research. I highlight some of these streams below with the hope of providing a springboard to encourage research that expands these theoretical areas in the context of the big data movement.
Insights From the Decision Aid Neglect Literature
As results generated by big data can help individuals in their decision making, it can therefore be considered a decision support system or decision aid (Dawes, Faust, & Meehl, Reference Dawes, Faust and Meehl1989; Sieck & Arkes, Reference Sieck and Arkes2005). Researchers have discovered that professionals, including managers, physicians, mental health clinicians, university admissions committees, parole boards, and many others are highly reluctant to use decision aids for a number of reasons (Grove & Meehl, Reference Grove and Meehl1996; Highhouse, Reference Highhouse2008). First, they tend to be overconfident in their own intuition and thereby assign a low value to the output of a decision aid (Sieck & Arkes, Reference Sieck and Arkes2005). Some eschew decision aids due to the belief that they will miss important “one-off” information cues (Grove & Meehl, Reference Grove and Meehl1996), such as a computer algorithm not being able to account for unexpected or value-based information such as political or moral considerations. Individuals have also been found to underutilize decision aids because they perceive them as dehumanizing or unfair (Dawes et al., Reference Dawes, Faust and Meehl1989), because they may wonder, for example, why they went through so much education or training if their expertise was going to ostensibly be usurped by a statistical analysis. There are also social influences that deter individuals from relying on decision aids, as studies have found that people tend to pay more attention to advice if it is based on expert judgment as compared with statistical forecasting (Onkal, Goodwin, Thomson, Gӧnül, & Pollock, Reference Onkal, Goodwin, Thomson, Gӧnül and Pollock2009). In this vein, Arkes, Shaffer, and Medow (Reference Arkes, Shaffer and Medow2007) found that individuals perceive physicians as less competent, thorough, and professional if they use a decision aid as compared with not using one. Although it may seem obvious that organizational decision makers will embrace big data, this body of work suggests otherwise. However, we do not know whether or how the decision aid neglect phenomenon applies to big data, in part because of its complexity given modern technical advances and in part because of its increased acceptance and endorsement by organizations. Nevertheless, many of the reasons identified above are core psychological processes (e.g., confidence, concern for fairness, etc.) and are thus likely to persist to some degree.
Insights From the Attribution Theory Literature
Although the above evidence indicates that individuals may underutilize big data in their decision making, other evidence from the psychological literature suggests a more nuanced perspective. Attribution theory (Weiner, Reference Weiner1986) is a widely cited framework for understanding how individuals infer causes about particular outcomes. This inference process is driven by a number of dimensions, most notably regarding whether the outcome is perceived to be due to causes that are internal (e.g., in the context of this article, one's intuitive judgment) versus external (e.g., the output of a statistical analysis). An individual with the opportunity to utilize a big data analysis for an important decision may actually reject it in order to “prove” that one's intuition or professional expertise is valid, consistent with the literature cited earlier, in order to receive credit or earn respect from others for a (hopefully) good outcome. On the contrary, individuals in this situation may instead prefer to rely heavily on the analysis in order to reserve it as a scapegoat for blame in case the decision outcome is bad. One may imagine the negative repercussions of going against the recommendation of the analysis. In support of this notion, Naquin and Kurtzberg (Reference Naquin and Kurtzberg2004) found that organizations are held less accountable for accidents when they were caused by technological failure as compared with human error. A number of moderators, such as personality, social status, and/or decision risk, may explain these two alternative possibilities—whether decision makers reject or leverage big data vis-à-vis setting the stage for a self-serving attribution of praise or blame, respectively, depending on the decision outcome.
Insights From the Intuition Literature
Analyses provided by big data purportedly allow people to make systematic and logical decisions, and therefore they are believed to help mitigate against biases and overcome the cognitive limitations of humans. On the surface, this appears to be a panacea for decision making in organizations, as we perhaps finally have a systematic method (and burgeoning norm) of quelling intuition in organizations (cf. Highhouse, Reference Highhouse2008; Tversky & Kahneman, Reference Tversky and Kahneman1974). However, the intuition literature may suggest otherwise. Researchers have documented that some experts are remarkably effective in using their intuition to make decisions and solve problems very quickly (Zsambok & Klein, Reference Zsambok and Klein2014). Intuition has been identified as a crucial process involved in creativity (Gore & Sadler-Smith, Reference Gore and Sadler-Smith2011) and moral judgment (Haidt, Reference Haidt2001). In addition, some studies suggest that organizational leaders benefit from using their intuition (Khatri & Ng, Reference Khatri and Ng2000; Sadler-Smith, Reference Sadler-Smith2004). A stream of research on “thin slice” judgments has found that, in some contexts, people can form very fast and accurate impressions of others based on a small amount of information (Ambady, Reference Ambady2010). For instance, Curhan and Pentland (Reference Curhan and Pentland2007) found that negotiation outcomes can be accurately predicted based only on the first 5 minutes of parties’ conversational dynamics. Thus, it seems that intuition may be a key driver of decision-making effectiveness in some contexts, and therefore it should perhaps be a part of the conversation of how big data informs decision making. For instance, it may be the case that analyses derived from big data can provide a useful reference against which one's intuitive judgment can be compared (see Erez & Grant, Reference Erez and Grant2014, p. 105).
Conclusion
I should reiterate that the above literatures are not the only sources of insight for sparking research on how we can get a better psychological understanding of how individuals integrate big data into their decision making. A number of other perspectives may be fruitful as well. For instance, we should consider investigating how norms of big data utilization affect the motivational and reward systems in organizations (cf. Gagné & Deci, Reference Gagné and Deci2005). As organizations are increasingly relying on teams to accomplish work, we also need to better understand how analyses generated by big data are shared and processed among individuals (cf. De Dreu, Nijstad, & van Knippenberg, Reference De Dreu, Nijstad and van Knippenberg2008). Another potential avenue of research is regarding how the growing emphasis on big data influences the power dynamics within organizations (cf. Pfeffer, Reference Pfeffer1981).
The big data movement is here, and it will continue to grow, with or without advances from psychological research. A critical piece we can—and indeed, need—to offer is a better understanding of how results from big data are integrated into decision making. As I hope to have illustrated, this is a complex issue; but, we already have a head start in identifying ways to contribute to the big data movement and ultimately to help increase the effectiveness of organizations and individuals.