Assessments Were Big Data Before You Heard of Big Data
The corporate world has embraced Big Data with a vengeance since the 2000s. Modern business success now demands spotting useful trends within ever-increasing volumes of data that seemingly weren’t possible even a decade before.
The very definition of Big Data would, at first glance, imply that old methods of data collection and analysis are now hopelessly out of date. Yet psychometric assessments, which were pioneered in the early 20th century and incorporated by businesses decades ago, became Big Data before the term was even invented.
Though we can now squeeze a mind-boggling amount of data out of nearly any topic, few areas of study seem to have quite as many variables as the human mind. Psychologists have made giant strides toward understanding human behavior, but there’s still many ways even the best-known people can surprise us. That lifelong friend you know as calm and collected might freak out completely when stuck in an elevator.
Big Data has given mankind’s desire to find answers to difficult questions an even greater sense of urgency, and that includes cracking human personality. Corporations, which are always looking into new methods to get ahead of their competitors, have increasingly turned to psychometric assessments that measure personality, agility, cognitive ability and other traits to find and develop high-quality workers.
These assessments have evolved since the early days of psychology asscientists and statisticians grappled with different ways to try to quantify both subtle and wide-ranging differences in personality from person to person.
One of the earliest, simplest and best-known is the original Myers-Briggs Type Indicator, which categorizes people as one 16 different personality types.
Personality typing has grown from those humble beginnings to increasingly complex assessment studies. Methods and definitions vary, but these assessments can measure dozens of different traits, frequently on a spectrum rather than a binary choice. Often, these traits are considered in combination with one another to generate hundreds, thousands or millions of possible results.
As granular and complicated as individual results can become, companies don’t usually have one employee. Each of the many positions that need to be filled could attract a huge array of personality types. What’s more, specific jobs call for specific personality traits, such as self-motivation and assertiveness for a sales position – though what specific traits aren’t always obvious at first glance. Finally, individual corporate cultures can also fit some personalities better than others, and different worldwide cultures can value different traits. Put it all together, and the issue of determining what kind of personality best fits what kind of job at what kind of company at what place in the world involves a nigh-infinite number of possible outcomes.
Since the sample size has grown so large, Hogan’s researchers have recently turned to machine learning to sniff out useful trends – but the huge pool of completed assessments also ensures the results are accurate and meaningful. As the sample size continues to grow and more sophisticated analysis techniques become available, researchers can keep drilling down further and remain accurate even as job requirements, corporate cultures, and leadership demands shift over time.
As complex as psychometric assessments have been for decades, the most talented assessment developers are poised to deliver even more data with even more impact in the coming years. These early pioneers of Big Data are getting even bigger.