What’s new in talent assessment? Nothing and everything.
Looking back on the year that was in HR tech and reflecting on this year’s awesome HR technology trade show, talent assessment remains a small, but significant, cog in the massive HR technology machine.
Despite a general lack of true innovation, the talent assessment industry is thriving. Outside investment continues to pour in — creating salad days for providers.
While the cash is flowing freely, talent assessment is still lagging behind other key functional areas of HR technology. For example, when it comes to mobile access to assessments, the talent assessment industry is pitching an epic fail. Today’s job applicants are still forced to endure time-consuming, non-mobile based assessments loaded with obtuse and confusing questions.
Skating on thin ice
Assessment providers need only to look around them at the way technology is changing consumer expectations to realize that they are skating on thin ice. Staying dialed in to the trends and walking the trades how floor at HR Tech 2015 make it clear that today’s hottest technologies (e.g., artificial intelligence) and the continued consumerization of enterprise HR technologies are working together to slowly hammer nails into the coffin of the traditional employment test.
While the singularity may still be decades or even centuries away, numerous technological advancements are already blowing our minds. The following technologies are impacting the future employment testing industry:
- Machine learning;
- Artificial intelligence;
- Natural language processing;
- Sentiment analysis;
- Biometrics (voice and retinal analysis);
- Neural networks;
- Predictive analytics;
- Virtual Reality (VR) heads-up display;
- Data mining; and,
These technologies are poised to shake up the employment testing industry for one simple reason: No one likes taking tests!
For decades there has been no suitable alternative to the insight provided by employment testing, and job applicants have had no power to express their dislike for being tested. But, the winds of change are blowing harder with each passing day. The collective power of job applicants and the emergence of new technologies are causing an inversion in the status quo of the employment testing industry.
Shifting from an “active” to a “passive” model
The status quo in pre-hire assessment is an “active” model in which job applicants are pushed employment tests that deliver a discrete set of questions designed to measure knowledge, traits, attitudes, skills, and abilities. For these tests to do their job, they must be created using proper psychometric techniques and applicants must complete the questions served to them in one testing session.
Moving forward, the real disruption to the testing industry will be a shift from an “active” model to a more “passive” approach. In the passive model, data needed to measure human characteristics will be collected from candidates via their ongoing interactions with various technologies (e.g., mobile devices, wearables, laptops, etc).
“Passive” assessments will not look like tests at all and will have a very low impact on job applicants’ time and attention.
The insurance industry is a great example of the shift to using passive data as a key ingredient in predictive decision making.
The insurance industry is built on the use of predictive analytics. While insurance companies still actively collect data from motorists, almost every auto insurance company now offers motorists the opportunity to use in-car devices that capture data about driving habits. Data silently collected from drivers who have installed tracking devices is used to optimize the prediction of how likely a motorist will be to get in an accident and rates are adjusted accordingly.
A quick look at two key trends shows why the future of predicting human performance in the workplace will inevitably move to a more passive model of data collection.
Trend 1: Digital breadcrumbs
This app helps users learn a new language via ongoing interaction with their mobile device. Learning sessions occur whenever the user has a few moments available to interact with the app. Duolingo allows the data associated with learning a language to be collected over a protracted period of time using individual questions delivered in small batches.
Duolingo’s more passive, on-demand approach to data collection and analysis is a glimpse into the future of employment testing. In the world of HR technology, there are a number of new companies pushing the market towards passive data collection, including:
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- Voice sense — Uses voice analytics from mobile devices to provide personality profiles.
- Sunstone Analytics (part of CEB/SHL) — Uses AI to create a success profile based on resumes of successful employees.
- Entelo & Gild — Both search the open web and use AI to evaluate personal data and forward employers candidates with relevant skills and abilities.
- IBM Watson — Uses AI to analyze written content and social media posts and infer personality traits.
Soon there will be even more companies using advanced technologies to passively collect data for use in employment decisions.
Trend 2: Big changes to the performance review process
Believe it or not, performance reviews have a lot to do with the future of employment testing.
One of the biggest limitations to being good at using a test to predict future performance is the absence of quality job performance data. It is impossible to understand the impact of predictive data used in the hiring process without data about the job performance of those hired.
Without data about actual job performance, our insight into the impact of our decisions is limited.
There are a few reasons why good performance data is hard to come by.
While it is not likely that most companies will ditch their performance appraisal programs, there is a lot of room for improvement. It’s only a matter of time until annual performance reviews begin to experience a shift from a highly structured active model towards a more passive one.
The annual review will soon be a thing of the past, replaced by a more passive model of ongoing collection and analysis of data offering a much more dynamic and data rich view of employee performance. Companies include:
- Leader amp — Provides on-going interaction with a virtual coach over mobile devices.
- Glint — App for performance data capture — dedicated to eliminating the annual performance review.
- Tiny Pulse — A highly agile, engagement driven employee data collection platform.
- Zugata — A platform that supports self awareness and on-going skill development.
Data post-hire will drive pre-hire predictors
The ability to capture and organize huge amounts of performance data in a more passive manner will have a big impact on the future landscape of predictive hiring tools. Injecting richer, more varied data on the post-hire side will drive the continued evolution of the pre-hire predictors.
Technology is providing the tools needed to make employment tests obsolete. We psychologists have spent almost 100 years perfecting the craft of measuring human traits, abilities, and attitudes. We know our tests work, but we also know that we must be willing to adapt our models to keep up with technology or face becoming practitioners of a lost art.
The need for collaboration and an interdisciplinary approach is critical for the value of measurement-based employment tests to live on.