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.
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).
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.
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.
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.
Proceedings of the Human Factors and Ergonomics Society 2019: A Human Factors Engineering Education Perspective on Data Science, Machine Learning and Automation
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