Let’s talk about the future of predicting job success and why the world’s biggest evangelist for pre-hire assessments thinks tests are in danger of becoming extinct (and is OK with it).
There are a number of emerging trends in hiring right now that center around the currency of the new millennium: data. The impact of our ability to collect, organize, and interpret data is rapidly changing all areas of the economy. Should employment be any different? There are three ways in which data is slowly killing the employment test as we know it.
The Impact of Publicly Available “Free-range” Data
People born in the past decade or so, along with all persons to come, will begin accumulating a personal digital fingerprint that will be associated with them from cradle to the grave. While there may still be some things that can be kept private, most everyone’s every move, preference, and connection will become publicly available to anyone who is interested.
The ideas behind using publicly available data to predict job performance are gaining big time attention these days as evidenced by these recent Business Insider and New York Times articles.
We are even starting to see research that suggests we can gauge an individual’s job success from social media data such as one’s Facebook usage.
Theoretically any data that exists out there can be thrown into the hopper, and its impact on job performance related variables examined. We I/O psychologists refer to this strategy as “dustbowl empiricism” because it lacks any real guiding theory, instead relying on cold, hard numbers as the only truth. This purely empirical strategy was popular in the 1950s and 1960s when various life history factors were used to build predictive employment tests or “weighted application blanks,” and is in fact still used today to some extent.
While this strategy does work, there is something unsettling about leaving out some underlying theory or framework and failing to rely on predictive factors that make rational sense. For instance, if we could show shoe size correlated with job success but had no reason why, should we feel comfortable hiring based on this factor? We are now revisiting this decades-old debate but within an entirely new framework that involves literally billions of new possibilities.
The Rise of Structured, Sanctioned, Verifiable, Shareable Personal Data
Also gaining in popularity at present are many types of data that people directly create and groom for specific purposes related to employment. Consider if you will the LinkedIn profile, which is now becoming the defacto resume. It is fluid and fully deconstructible for use in hiring situations. It has all the info needed by an employer to gauge your ability to contribute to their cause.
My friends at DDI just shared some really cool research with me in which they were able to find a relationship between LinkedIn profile elements and job success. In this purely exploratory study of 587 people across 11 organizations, the researchers examined the impact of LinkedIn profile elements such as number of connections, position progressions, and employment gaps on performance. They were able to show that a variety of rationally hypothesized factors (i.e., # of positions held) impacted key variables such as turnover. They also found evidence of less-rational relationships such as the fact that those who gave more recommendations had higher job success levels than those who received them. This study showed a direct connection between profile elements and performance and as such I consider it to be a groundbreaking study.
Beyond LinkedIn, badging/credentialing represents an emerging concept in which persons can essentially gain digital merit badges that carry with them an assumption of competence or skill in a certain area. Currently, a great example is the Klout score, which can be used to help evaluate someone’s level of engagement in social media and is often cited as a key piece of data for those being evaluated for marketing and advertising jobs.
We are also seeing virtual badges that are awarded based on the completion of coursework or mastery of a subject that is verified or validated by a third party that is able to officially sanction it. Imagine if you will that these verified pieces of evidence can then be attached to a public data profile and that they can be easily reviewed via a predictive algorithm used by an employer without the individual even applying for a given job.
Combine this with the emerging trend of free online learning and coursework and the connectivity of social media and you have just hit on the future of sourcing and screening tools. It will not be long until anyone, active job seeker or not, is being notified of jobs for which they realistically are a good fit, without lifting a finger. In a few years a recruiter could be wearing the Google Glass with an app that will allow them to compare people they meet in person throughout their day to open requisitions in real time.
An Increase in the Quality and Quantity of Job Performance Data and Company Information
One of the most significant barriers to proving the value of hiring practices is slowly and steadily being torn down. I’m talking about the difficulty of collecting quality job performance data required to support the proper evaluation of pre-hire data sources such as tests.
Ask any I/O psychologist and they will tell you that getting companies to provide access to the data needed to do predictive analytics is like pulling teeth. In many cases it is just not available, and in others the company simply does not care about collecting or maintaining it. We are often forced to use low-quality job performance data as part of our evaluation program. You can’t predict things accurately if you don’t have good data on both sides of the equation. We I/O psychologists refer to this difficulty as “the criterion problem,” and it has held us back for decades.
Change is coming when it comes to performance data. Workforce analytics is here to stay and at its core resides data of all shapes and sizes. We are now in love with data and most major companies are tracking everything. This allows us new proxies for the data we have not been able to collect and also provides a quantum leap in the ability to explore data relationships to look for meaningful patterns. The shift in the perceived value of data is perceptible and it is going to have a huge impact on the ability of analysts like myself to begin showing companies the direct value of their employment practices. In some cases these practices may be tests, but we should not constrain ourselves to the notion that tests are the only predictors of value. Once the post-hire data stream becomes more available, we should be prepared to find all kinds of new predictors of value.
I am not quitting my day job as a testing guy just yet. But things are changing fast and those of us in the testing business are going to be tested ourselves. The data we begin to see may make it hard for us to argue with the impact of digitized personal data on job success. The truth lies somewhere in the middle, and the best formula will include both unstructured data and digitally shareable credentials that are verified via accepted sanctioning bodies. This digital fingerprint will be used (along with other personal data) to help match people with jobs and to quantify the direct impact of hiring on an organization’s strategic objectives. Yes, tests may be involved somewhere in the data stream, but they will cease to be the only focus when it comes to predicting job success.
