The Good and The Bad Sides of Predictive Analytics
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For centuries people have been captivated by the idea of predicting the future. Crystal ball gazers and fortune tellers all promised to do this. They played on our biases, weaknesses, and gullibility and counted on us attributing chance occurrences to their predictive powers.
But the rise of predictive analytics gives us the ability to reduce uncertainty by applying statistics and determining the probabilities that future patterns will emerge in the behavior of people and systems.
This is becoming an essential element for recruiters in selecting candidates who are most likely to succeed and helping those not selected learn more about themselves and where they might best use their skills.
Because people are largely creatures of habit and tend to repeat behaviors, our online activities, combined with today’s artificial intelligence and statistical knowledge, tell a lot about what we are likely to do. We can give odds, based on science, about who will be the better choice among many candidates. To do this requires access to mountains of data about what our candidates do, when they do it, how often they do it, and where there do it.
Smartphones and tablets give analysts the ability to track location, social media likes, retweets, what and when we buy, what people search for, and whom they communicate with.
These analysts are then able to make reliable predictions on our future behavior. We often dismiss this peripheral data as meaningless. After all, why is where we go or who we communicate with important? This data is often called “data exhaust” by analysts, and in and of itself, it has no real meaning or value. However, when aggregated, correlated, combined, and then analyzed, this data becomes not only relevant but commercially valuable.
I am a strong advocate of collecting and using data to make better decisions about candidates and employees. The more we know about a candidate’s preferences, history, accomplishments, and skills, the better we will be able to offer them satisfying and productive work.
But there are inherent downsides to collecting and analyzing data. Like anything, data can be used for both good and not-so-good reasons. Many recruiters are wary of collecting data and worry that we invade a candidate’s privacy. To avoid this, we must be transparent about what we are doing, what we are collecting, and why we are collecting it.
Most websites now collect data as we browse. They do this through algorithms and cookies. Although we are often given the choice of accepting cookies (these are the bits of code that allow us to be tracked), many of us feel the benefit of what we get for accepting the cookie is greater than the intrusion on our privacy.
Ethical recruiting requires us to let candidates know what we are collecting, why we are collecting it, and, perhaps as important, what we will do with it after using it.
The value in using data is obvious when we look at our current practices.
Mistaken Beliefs and Assumptions
Recruiters and hiring managers are guilty of using their own beliefs and assumptions to influence their decisions. For example, a hiring manager may only want candidates from elite schools because he believes they are better. Or a recruiter instantly rejects a candidate who did not have a 4,0 GPA. These are examples of poor decision-making based not on data but on beliefs or assumptions.
Analyzing data from all employees and looking for patterns that show the traits, skills, or behaviors that led to good performance is far more useful.
One-Sided Learning
If the data we collect and analyze is only used for our benefit, we are not practicing ethical recruiting. It would be far better to share what we have learned with a candidate so that they could make better decisions on what to apply for, what career might be better, or how to improve a lacking skill set.
Biases that impede truth
All humans have biases and many that tend to impact human resource professionals and recruiters.
The selection and hiring of people is fraught with bias and subjectivity. Psychologists have assembled long lists of these biases, including our tendency to reject new evidence that contradicts something we believe is true. Or the tendency to search for and remember information in a way that confirms our preconceptions.
For example, if we believe that people with high GPAs, for example, are better workers, then we will seek evidence to prove that and dismiss any that contradicts it.
When making decisions, recruiters often rely too heavily on one trait or piece of information -often the first piece of information acquired or the information obtained from a trusted source. For example, if someone recommends a candidate, that recommendation may outweigh any facts that contradict or suggest that the person is not so good.
Many recruiters and hiring managers also suffer from the “hothand effect” which is the fallacious belief that a person who has experienced success doing something has a greater chance of further success in additional attempts.
Analytics can help dispel many of these, but only if the results of the analysis are believed and acted on. There are many instances where our biases were unconsciously built into the algorithms that analyze our data.
Analytics can offer insight and help make sense of mountains of data that have been beyond our reach. Analytics can help us make choices that are based on facts. They can provide us with insights and reduce uncertainty. But, as with everything, there are dangers. We need to troll the waters of data with care, ethics, and human judgment.
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Thanks for taking on a very difficult topic.
While reading, my cerebrum was flooded with warnings! What came to mind was predictive analytics like China's heinous Social Credit System and specious algocracy - algorithmic prediction, personnel by algorithms, algorithmic selection, algocratic management, and a new algorithmic talent order. Granted, you did give fair warning, but it may not have been strong enough.
Simply put, humans are not equipped nor do the protocols exists to embrace predictive analytics for for wicked problems like talent. Human behavior is irreducibly complex. Talent & hiring managers are perpetually facing incomplete, contradictory, and changing requirements that are often difficult to recognize and impossible to predict.
Put another way, talent is non-deterministic, it has no stopping point. Predictive analytics have been a boon to discrete enterprise problems like consolidated fulfillment and replenishment, for example, but just aren't ready for social complexity prime time.