Analytics and Technology Have Led Us to the Doorstep of Assessment’s “Golden Era”

As I continue to attend conferences and hear awesome speeches about analytics such as the one by Josh Bersin, I am thoroughly convinced that talent acquisition (testing and assessment included) are at the beginning of a new era. The coming decades will represent not just a new era for testing and assessment, but rather its “golden era.” I began talking about this trend almost a decade ago, and I continue to watch for signs of the major transition that is currently underway.

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The past decade saw Internet technology freeing employment testing from the shackles of paper-based administration and reporting. As it got easier to do tests, there was a big increase in the use of pre-employment testing and the testing industry changed. A byproduct of this shift was a veritable cornucopia of data that has better allowed us to understand the factors that predict performance in almost every job and industry.

The coming decades will be all about the ability to use data and technology to gain incredible new levels of insight around people and their relation to the workplace — and to use this insight to realize new levels of efficiency and effectiveness.

Here are some of the things that will provide unprecedented ability to understand the relationship between people and jobs both in the near and the not to distant future.

Closed loop analytics: Businesses can now set up their hiring technology system to continually collect data related to job and/organizational performance (i.e., sales revenue, customer service scores, absenteeism, store or unit-level performance, etc.) and stream it back into a platform that will compare it to pre-hire data in order to determine the impact of hiring tools on real, live performance outcomes … in real time!

Such “closed loop” analytics systems go well beyond the classic concept of “validating” tests and assessments, instead providing a better understanding of the impact of a wide range of data collected in as part of the hiring process on real outcomes. This allows for a dynamic picture of the impact of pre-hire assessment on real business outcomes as opposed to the more common and less-effective static snapshot of these relationships. Companies such as Furst Person and Evolv are leading the way with the first real closed loop analytics systems on the market.

Tighter integration between talent acquisition and talent management: We are moving toward technology backbones that integrate data feeds from “pre-hire to retire.” Different systems like performance management, talent acquisition systems, and HR management systems will be better integrated, allowing for deeper insight into all sorts of decisions related to talent. It’ll soon be easier to measure the impact of long-term individual and group performance on key business outcomes. When it comes to testing this means that the impact of pre-hire assessment data on post hire talent management will become much more clear, allowing it to add more value.

This trend is just really beginning in earnest right now as companies are starting to understand that pre-hire assessment data has value for post-hire activities. For decades pre-hire assessment data has been essentially thrown away once an applicant bas been hired. We now know that this data has tremendous value in helping to effectively onboard new hires and manage their performance. Beyond this, pre-hire assessment data is also a key part of a long-term view regarding the impact talent has on organizational performance.  Companies such as DDISHL, Taleo, and Kenexa are all helping their clients to gain long-term insight into the impact their talent acquisition process has on their talent management process. We are in a relative state of infancy with regard to comprehensive data systems that will provide the levels of insight I am talking about, but rest assured it will be a reality in the decades to come (see Oracle’s purchase of Taleo, and SAP’s purchase of SuccessFactors).

Expansion of the data used to make predictions about job applicants: The overall look and feel of assessment tools will continue to move away from the current standard of boring radio buttons. I’m not just talking about slick simulations either. While there is no doubt that assessments will become more engaging, I’m actually referring to the use of real-world data that does not look like an assessment to be used to support prediction about job performance (social networking activities, purchasing behaviors, etc.).

In some cases this could get somewhat sinister (genetic information that will provide insight into applicants who represent health care risks, for example. While in others (such as the impact of a social media profiles on job performance) it may actually have some merit. I am not saying that we should start using Facebook as a predictive tool, rather that the data is out there and the exploration of its impact on job performance is just getting started.

The ability to collect data and analyze an inconceivable amount of data from myriad sources in order to make employment predictions has placed us on a collision course with a new set of legal and ethical issues related to what information will be allowed into the equation when it comes to pre-hire predictions. The battles that shape these new standards will help testing by drawing the line around acceptable methods and helping to clarify a more modern set of legal standards (the current ones were created in 1978!).

While this collision could be more than a decade away, it will result in a new set of rules for what can and cannot be used to make employment decisions, and hopefully we will arrive at a good formula for balancing rational ideas with empirical data. For instance, it makes sense that online profiles will become the new resume and that this transformation will see these profiles being used as a predictive tool. The information contained in profiles will be verifiable and transportable, allowing it to be easily shared and compared (almost like a digital passport or portfolio). Exactly how far companies will go in the use of unsolicited social networking profiles (as opposed to an applicant actively submitting a LinkedIn profile) remains to be seen.

Sourcing will change: Social media and analytics combined will lead to new models for sourcing, making it much easier for people to find jobs (and vice versa). One thing that will definitely impact sourcing will be a paradigm shift in the concepts of jobs and careers. We will see a trend toward constant movement of individuals between opportunities as the workforce becomes increasingly able to match their interests, skills, and abilities with their ambitions in a much more fluid manner then has ever been possible. A cornerstone of this fluidity will be the ability to accurately measure an individual on constructs that are critical for job performance (i.e., cognitive abilities, personality, career interests, work motivators, etc.) and to break jobs down into these same components. The ability to fluidly match people to job openings based on credible and reliable data will depend on quality assessment tools that provide a standard language for employers and job seekers.

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Imagine a time when everyone browsing the web could view their “fit” with a given company simply by visiting their website for other reasons (product research, e-commerce, etc).

Companies such as Talent Technologies are beginning to create systems that are using sourcing and candidate communication to allow companies to fluidly source and communicate with candidates in ways previously not possible.

Companies like BurningGlass are starting to analyze millions of resumes and job postings in real time to begin gaining insight into the overall labor force and the skills needed to support career progressions, making it possible to predict what skills are in the most demand and what skills are needed for someone to move from one job to another.

Eventually assessments will enter into the equation and when mated with new ways for people to find jobs (and vice versa), they will provide a new language of opportunity for persons who do not wish to waste time working in jobs for which they are not suited or where they do not fit in.

The relationship between higher education and jobs will change drastically: As the price of education continues to skyrocket and the ranks of unemployed college grads swell, pressure to find new ways for individuals to demonstrate their suitability for the workforce will cause major shifts in the current system. One emerging trend that promises to be huge is the ability to self-educate using the Internet.

Companies like Coursera are making legitimate, free online education a reality and are on the forefront of a major paradigm shift in the academic system. Other companies like Pathbrite are allowing individuals a way to track and share their online learning.

As persons self-educate using the Internet, we will see a time when one can take tests that will provide certification of a specific skill set or competency without the formality of a degree. These tests will actually offer an improvement over the current system in some ways since a college diploma is not able to offer precise prediction of specific competencies or skills. Standardized, high-stakes certification exams will allow employers a much better level of insight into the match between an individual and a given job. It wont take long until the data collected from this process will provide the ability to predict both job and career performance (using the various things discussed above).

All of the factors I have discussed here are actually interrelated and the result of their evolution will be nothing less then a series of major shifts in the relationship between humans and their work. In one way or another, assessment (or the concepts it is based on) will be a big part of this evolution by providing increased ability to help individuals and organizations come together in order to provide a psychological contract that will benefit both parties.

Dr. Charles Handler is a thought leader, analyst, and practitioner in the talent assessment and human capital space. Throughout his career Dr. Handler has specialized in developing effective, legally defensible employee selection systems. 

Since 2001 Dr. Handler has served as the president and founder of Rocket-Hire, a vendor neutral consultancy dedicated to creating and driving innovation in talent assessment.  Dr. Handler has helped companies such as Intuit, Wells Fargo, KPMG, Scotia Bank, Hilton Worldwide, and Humana to design, implement, and measure impactful employee selection processes.

Through his prolific writing for media outlets such as, his work as a pre-hire assessment analyst for Bersin by Deloitte, and worldwide public speaking, Dr. Handler is a highly visible futurist and evangelist for the talent assessment space. Throughout his career, Dr. Handler has been on the forefront of innovation in the talent assessment space, applying his sound foundation in psychometrics to helping drive innovation in assessments through the use of gaming, social media, big data, and other advanced technologies.

Dr. Handler holds a M.S. and Ph.D. in Industrial/Organizational Psychology from Louisiana State University.







7 Comments on “Analytics and Technology Have Led Us to the Doorstep of Assessment’s “Golden Era”

  1. Charles, great article the coming together of forces to form the threshold to the Golden Era.

    Threshold is indeed the correct word. The hold back factors from saying we have made it through the door are missing skill sets and data access.

    Assessment is actually a form of measurement rigor for a business process called staffing. When set up with closed-loop analytics, the insights can drive decision making that improves the yield of the process. The greatest challenges to taking advantage of this form of analysis are HR and Recruiting practitioners that are hard pressed to access data, and organize it into meaningful clusters. Read More.

  2. Charles, A clear and compelling argument, as always. The case to be made for assessments and for predictive science is powerful and yet not nearly enough people and organizations are yet buying in. Legacy systems and tech-is-the-solution mentality play a part in that resistance. But like I am convinced that we are entering an era when this will change. We are and will increasingly be a player.

  3. Thanks, Dr. Handler. I very much like the increasing availability of tools which allow for a more complete and objective analysis and assessment of people and the jobs they apply for/work in. However, this assumes that the people involved on both sides are “rational actors” which research in behavioral economics and cognitive science shows to not be the case- we are largely influenced by innate cognitive biases- a hiring manager doesn’t want to hire a person that careful analysis shows to be the best person for the job- s/he want to hire the person s/he THINKS is the best person for the job. In order to have verifiable objectivity to be most effective, it would be necessary to minimize the subjective factors (aka “real people”) involved, and the “subjective factors” are not going to be pleased by that….Last night I heard a great talk on “big data” and recruiting and met the founder of a company that is using the Big 5 Personality Test” to determine a given candidate’s fit in a group.. If you combine your assessments with this company’s service (or something like it) you could have objective means of clearly finding the best qualified and best fitting applicants without much need for human decision making. How many hiring managers would go for that, even if it clearly is superior to the old-fashioned way? Until you can convince substantial numbers of decision makers that algorithmic data analysis is more effective than their own decision-making and that they won’t lose power by using it, I don’t see any “Golden Age”.


    Keith “Where’s Our 32 Hour Work Week” Halperin

  4. @ Keith

    Thanks for the insight. Dont think for a moment I am advocating robotic employee selection. I continually preach the idea that human judgment is essential and should not be removed. Rather it should be supported by good tools to help those making decisions to make informed decisions. I will say that the present situation at the top of the funnel is dismal. There is tons of noise in the way people FIND jobs and technology and assessment can have a huge value here. By simply putting people who have the basic qualifications into the conversation, we provide a statistically better chance of making a good hire even if the down the funnel hiring is being done by a chimp. Add a good set of dialed in predictive assessments and the chimp has roller skates and a beany. Seriously, though. I sincerely hope there is not a time when humans do not have a major role in hiring but technology can help us be better at our jobs, thats its value and recruiting and hiring is no exception.

  5. Excellent summary, Charles. I agree that we are entering a new era. The credibility of more scientific hiring decisions can be seen in the long term success of a company such as Google, for example, where these objective recruitment processes have helped them hire thousands of very suitable employees in a relatively short period of time.

    The lagging, legal and ethical issues, you mention are ones that will be important ones for the recruitment industry to address, effectively and transparently. We don’t need greater legislative involvement in hiring right now.

  6. Charles, great article, and I agree with you that the golden era is upon us. Employers who don’t utilize pre-employment assessments are typically inundated with candidates, and as you noted in a comment above, there is “tons of noise.” Using science and technology to narrow the funnel of applicants is smart, efficient, economical and makes a ton of business sense – and enables a company to focus on candidates who (as you mentioned) are most likely to work out. Seems like a better gamble than some of the more traditional methods of screening applicants (and probably better than some non-traditional methods I’ve heard of – like if a candidate has a hotmail address, they’re rejected – I’m sure that’s very encouraging to jobseekers). Helping companies to focus on talent metrics can only help to get more businesses into this new era.

  7. Thanks, Dr. Handler. As the saying goes: “There’s the rub.”
    1) If decision-algorithms will be able to effectively do all types of decison-making better than people, which (if any) should be left to us, and why?
    2) If decision-algorithms will NOT be able to effectively do all types of decison-making better than people,which types should be left to them and which to us, and why?
    3) The assumption here is that who is objectively the best and who the hiring managers/interviewers THINK is the best are the same, and I think that’s frequently NOT the case.
    We are inherently limited in our decision-making by our cognitive biases (that’s the basis of Behavioral Recruiting), and a realistic tool set would take note of this vital fact.

    @ Ross: Google and scientific hiring decisions? Not when I was there, unless by “scientific” you mean having a high GPA from an elite school (totally unrelated to job performance except for some attorneys), and being youthful and perky, aka “Googly” .

    @ Paula: Golden Age? There’re hella powerful vested interests in doing things the old-fashioned, inefficient ways- lots of folks to lose money (low-level contingency agencies, and “recruiting snake-oil peddlers” in particular) and face/power (hiring managers and their +1s, +2s, etc.). They won’t magically “see the light” and bring in the Golden Age.



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