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Human Factors Engineering Human Factors Engineering

What Is Human Factors Engineering?

Psychologists and industry professionals use human factors engineering to design equipment and workflows to maximize safety and efficiency.

Their work is especially important in dangerous or stressful industries, like healthcare, oil and gas, or aviation. However, other professionals, from managers in commercial offices to mobile app designers, use human factors to motivate and guide employees and to appeal to customers.

This field is also known as engineering psychology.

Where Does Human Factors Engineering Data Come From?

This data comes not just from psychological studies but also from industry reports. Investigative reports on disasters, in particular, are good sources of human engineering data, even when not authored by trained psychologists.

Competitor analysis also provides insights into appealing, safe, and easy-to-use design that companies can replicate (or avoid).

What Types of Columns/Attributes Should I Expect When Working with This Data?

The main attributes of human factors engineering data concern the user, the environment, and the equipment under use. However, separating these factors from each other is difficult, for obvious reasons. Many researchers therefore rely on in-field usability testing before declaring something safe for use.

Additionally, expect to find data tailored to specific industries. Some equipment is intended only for trained users, some environments are inherently more stressful than others, and so on.

What Is Human Factors Engineering Data Used For?

Human factors attempts to account for user, equipment, and environmental factors to improve safety and efficiency in all industries. It offers guidelines for management, machine construction, and situational awareness so anyone from designers, managers, marketers, and in-store salespeople can use the insights generated from this field of applied psychology to their work.

How Should I Test the Quality of This Data?

The best data quality test is comprehensiveness and relevancy. While human factors engineering inherently covers a wide range of factors, the data scientists should still take care to use as much data relevant to their industry or use case as possible.

Also of note: human factors engineers do not have extensive data science training. Therefore, any workflow or software system that requires machine learning programming should account for the skills gap.

Interesting Case Studies and Blogs to Look Into

Proceedings of the Human Factors and Ergonomics Society 2019: A Human Factors Engineering Education Perspective on Data Science, Machine Learning and Automation
Science Direct: Development of NASA-TLX (Task Load Index): Results of Empirical and Theoretical Research

Tangible Examples of Impact

The Human Factors and Ergonomics Society (HFES) applauds Federal Aviation Administration (FAA) Administrator Steve Dickson’s order that returns the Boeing 737 MAX to service following the fatal Lion Air Flight 610 and Ethiopian Airlines Flight 302 crashes last year and the aircraft’s subsequent grounding… Automation confusion and loss of situation awareness are common challenges brought on by the inherent brittleness of automation, lack of automation display transparency, and inadequate automated system training.

Newswise: Human Factors and Ergonomics Society (HFES) Applauds Boeing and FAA for Resolving 737 MAX Human Factors Design Flaws

Relevant datasets

BuzzSumo Crisis Alerting

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Crisis Alerting provides you early alerts during a crisis to make wise moves.

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BrandWatch Crisis Management

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Crisis Management enables brands to have all the tools and data to keep them safe and sound suring a crisis.

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IBISWorld Industry Reports

Industry Research Reports is an online library of reports offering actionable insights, comprehensive data and in-depth analysis on thousands of global industries.

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