Data Analytics in Human Factors¶

Status: emerging
Last updated: 2026-05-31
Sources: 9781119636113.Ch51.Pdf
Tags: [data-analytics, big-data, human-factors, machine-learning, network-analysis, systems-thinking, methods]

Summary¶

Data analytics is the use, manipulation, cleaning, processing, and analysis of data to reach conclusions, an activity that is not new to human factors but is being transformed by Big Data and advanced software (Holman et al., 2021). The chapter frames a shift from a "deterministic microscope," which examines individual units of data in detail, to a "data analytical macroscope," which examines global patterns across very large datasets. It positions data analytics as the means by which human factors can engage with the Fourth Industrial Revolution.

Body¶

Context¶

Holman et al. (2021), in their handbook chapter on data analytics in human factors, examine the use, manipulation, cleaning, processing, and analysis of data to reach conclusions, and how Big Data and advanced software are transforming it. Their central framing is a shift from a "deterministic microscope" examining individual units of data to a "data analytical macroscope" examining global patterns across very large datasets. Within this knowledge base the article is the methods-at-scale strand of human factors: it provides the analytical lens through which the empirical foundations of Human Factors Ergonomics Discipline move to systems-level questions, complements the model-building of Human Performance Modeling and the brain-data focus of Neuroergonomics, and supplies the data-driven backdrop for Automation Autonomy And Ai.

Key Points¶

Data analytics is both old and new to human factors. Taken at face value there is nothing new about it, since data has been used to reach conclusions since the discipline's inception; what is new is the growing use of the term to describe something different, which the chapter sets out to define precisely (PDF pp. 1–2, orig. pp. 1351–1352).

Big Data is a distinct phenomenon requiring definition. Holman et al. define it as extremely large data sets analysed computationally to reveal patterns, trends, and associations, especially relating to human behaviour and interactions, with definitions sharing an emphasis on the global scale of the data rather than the individual units within it. Distinguishing Big Data from ordinary data matters because the analytical methods and the human-factors questions they enable differ from traditional small-data approaches (PDF p. 2, orig. p. 1352).

The central metaphor is a shift in scale of analysis: from the deterministic microscope, which examines individual units of data in fine detail, to the data analytical macroscope, which examines global patterns across large datasets. This positions data analytics as a change in the level at which human-factors phenomena are studied, not merely a change in tooling (PDF pp. 2–3, orig. pp. 1352–1353).

Holman et al. argue that data analytics will let human factors step into the Fourth Industrial Revolution and beyond, driving HF methods with Big Data and advanced software (PDF p. 3, orig. p. 1353), including network approaches such as dynamic EAST networks (PDF pp. 30–31, orig. pp. 1380–1381).

Conclusion¶

Holman et al. (2021) conclude that data analytics extends the empirical foundations of human factors to large-scale, systems-level questions. The macroscope is a forward-looking methodological capability that complements rather than replaces the microscope, letting the discipline engage with Big Data and the Fourth Industrial Revolution.

References¶

Holman, M., Walker, G., Bedinger, M., Visser-Quinn, A., McClymont, K., Beevers, L. & Lansdown, T. (2021) 'Data Analytics in Human Factors', in Salvendy, G. & Karwowski, W. (eds.) Handbook of Human Factors and Ergonomics. 5th edn. Hoboken, NJ: John Wiley & Sons. holman2021dataanalytics

Open Questions¶

When does the macroscope view of Big Data lose information that the deterministic microscope would have captured?
How should human-factors practitioners validate conclusions drawn from Big Data analytics against traditional experimental methods?