Augmenting Foresight Methodologies with Data
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WFRXXX10.1177/1946756720905639World Futures ReviewBoysen 905639research-article2020 Review World Futures Review 1 –10 Mine the Gap: Augmenting © The Author(s) 2020 Article reuse guidelines: sagepub.com/journals-permissions Foresight Methodologies https://doi.org/10.1177/1946756720905639DOI: 10.1177/1946756720905639 with Data Analytics journals.sagepub.com/home/wfr Anne Boysen1 Abstract The explosion of Big Data and analytic tools in recent years has brought new opportunities to the field of foresight. Big Data and improved analytics capabilities can expand the knowledge base and act as a corrective to our cognitive biases. Moreover, several data mining and machine learning techniques that increase performance for businesses can be applied in foresight to help researchers discover patterns that may be early signals of change and correct our misperception of patterns where they don’t exist. This article discusses the opportunities and limitations of various data mining and machine learning techniques in foresight. Keywords foresight, future studies, data analytics, machine learning, artificial intelligence, methodology Without data you’re just another person with an techniques to discover novel patterns directly opinion. in primary data. While this article discusses —W. Edwards Deming applications of analytics of both primary and secondary sources, it will specifically make a The explosion of Big Data and analytic tools in case for the former. recent years has brought new opportunities to the field of Foresight. Information gathering Can Big Data and Analytics and processing that once took weeks and Help Fight Cognitive Bias? months can now be accomplished in much shorter time and with fewer resources. With The centrality of data and empirical deduction the increased data access and analytics capa- has waxed and waned in philosophy and aca- bilities comes not only speed and accuracy, but demic research. More than half a century ago also better opportunities to study data directly Karl Popper popularized the Hypothetico- without interpreting intermediaries, such as Deductive method which has become widely journalists, publishers, and research institu- adopted in social sciences as a means to fight tions. Walls that once existed between fore- positivistic assumptions or theories without sight professionals and raw data are crumbling, corroborating evidence. In his seminal work or at least becoming more penetrable, as both The Logic of Scientific Discovery, Popper the access to and the analytic capabilities of Big Data become ever more available. 1University of Houston, TX, USA While text mining tools that automate envi- Corresponding Author: ronmental scanning are gaining more atten- Anne Boysen, University of Houston, 4800 Calhoun Rd, tion, little has been written about applying Houston, TX 77004, USA. statistics, data mining, and machine learning Email: [email protected] 2 World Futures Review 00(0) (2002, 18–24) warns that “it must be possible about in the mid-twentieth century are still for an empirical scientific system to be refuted valid, and a strong foundation in data can help by experience” and that “a subjective experi- us build a solid base of empirical evidence that ence, or a feeling of conviction, can never jus- helps our perception of reality with empirically tify a scientific statement.” Later schools of deduced logic rather than subjectively induced thought have posited that there were more assumptions (“Confirmation Bias and the solid barriers between the researcher and the Power of Disconfirming Evidence”, 2017). An objective truth than merely collecting and ana- empirical approach could be our best defense lyzing data. Social privilege, language, and against biases amplified by social influence culture influence the interpretation of reality and echo chambers (Mounk 2018). and how we perceive and emphasize the data Discovering interconnection, correlations, we have access to. and causal effects in the systems we want to Discourses in Foresight have followed a understand helps us understand current similar trajectory, alternating between empiri- dynamics or locate early warnings of change. cal deduction and forecasting to more critical If we want to contemplate future state t2 or t3, approaches that question metanarratives and we should first obtain fine-tuned information values (Inayatullah 2009). After all, data from about the current state t0. While comprehen- the future on which to make falsifying state- sive data analysis may have fewer direct ments do not exist. The various courses of applications for fleshing out complete sce- events a futurist must consider will often depart narios, our assumptions around data, casual from current reality in both kind and magni- connections, and change should be informed tude, making empirical data from the present by rigorous data gathering and analysis.1 If less useful when envisioning alternative we employ analytics customized to the type futures. Questioning common default assump- of insights we want to unearth, we reduce the tions is often seen as a more viable approach chances that our research is biased or mis- than predicting the future (Dator 2009). aligned with our research objectives. By If our default assumptions color our selec- doing our own primary data analysis, we can tion of data sources, it might seem as if data ensure a more future-oriented problem focus analytic approaches fail to correct cognitive and also reduce the chance of sponsor bias, bias. After all we can decide to include some which can be hard to detect when we rely on sources and not others. But while the selection secondary reports and desk research (Sarniak, of data sources is left to human judgment, we 2015). cannot necessarily infer causal relationships Of course, a mere focus on data is not within the data. Unlike authored information enough to remove cognitive bias from research often curated by personalized algorithms, raw since the interpretation of objective data is data lack narrative and incentives to focus on bound by the epistemologies of the researcher. some elements at the expense of others. It is Thus, an emphasis on comprehensive data reasonable to assume that we are less likely to approaches should not be used to trivialize fall for selection bias in our overall analysis if critical discourses around ontology and para- we can better prevent personal interests to digms. For example, Inayatullah does not influence our analysis. argue that critical analysis of the metaphors In a time when “post truth” has entered our that surround data should in any case sacrifice dictionary (“Word of the Year” 2016) and anti- the data collection effort. In fact, he suggests scientific factions weaponize warped readings that the Causal Layered Analysis (CLA) of post-modernism to advance relativistic framework should not be used at the expense agendas, efforts to rebuild intersubjective con- of data orientation (Inayatullah 2014). It might sensus around verifiable facts might be more therefore be more appropriate to view data important than ever (Kuntz 2012; London centric and more post-structural approaches as School of Economics and Political Science synergistic rather than two epistemologies 2017). The subjective pitfalls Popper warned where one precludes the other. Rather data Boysen 3 should be seen in the context of meanings, and a process of analysis, interpretation, and meanings in the context of available data. prospection render an outcome that can be used Since machine-analyzed data are intrinsically in strategy formation (Voros 2003). Early in the more value-neutral and comprehensive, we information gathering process a foresight prac- should expect a more robust foundation for the titioner must identify early signals and trends. critical analysis it enables. A student in my Conway (2006) distinguishes between trend Data Analytics class in the Foresight program spotting and trend analysis, pointing out that at the University of Houston obtained granular analysis considers the existing themes and pat- knowledge around what makes a person likely terns in society. Merriam-Webster defines to consider opportunities in the emerging gig “trend” as a prevailing tendency or inclination, economy. Building a model that considered a a general movement. While a trend reflects cur- complex combination of demographic and atti- rent events that, based on the frequency of its tudinal variables, she was able to sketch out mention in blogs, twitter, news stories, and so the combination of traits of people who will be on, can be gauged as either increasing or more or less likely to thrive in the gig econ- decreasing, an emerging issue is a latent issue omy. This knowledge would be useful to sce- that has not yet reached mainstream attention. nario exercises focused on the future of work, Richard Lum writes that a trend is a job automation, or industrial reorganization. In its absence, our scenarios might be informed historical change up until the present, then an by anecdotal case examples or loosely appli- emerging issue is a possible new technology, a cable secondary findings. potential public policy issue, or a new concept or idea that, while perhaps fringe thinking today, could mature and develop into a critical Environmental Scanning mainstream issue in the future or become a through Direct Observation major trend in its own right. (Lum 2016) in Big Data Dator (2018, 7) describes emerging issues as Futurists strive to obtain somewhat similar the “far left tail of the ‘S’ curve of growth,