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What Have You Done for Me Lately? Entrance to the C-suite Requires Data on the Economic Impact of Job Performance

by Aug 6, 2013, 12:58 am ET

If I hear one more speech about how HR needs to be strategic, I may lose it. Of course, we all want and need to make a difference, get noticed, and help our companies be successful. I’m not trying to suggest that we avoid the idea of thinking strategically, but there is only one true way to be strategic when in front of business leadership: show them the money!

Until we HR, talent acquisition, and talent management professionals have the tools and know-how needed to directly quantify the economic impact of our efforts to hire people who will perform effectively, it will be hard  for us to be truly strategic and triumph in our quest to be taken seriously by the C suite. While there is no doubt that the tools used to predict which applicants will be the best performers provide an important contribution, the only way to gain insight into the value of these tools is to tie their effectiveness directly to financial metrics of job performance. Unfortunately, this has proven to be a major challenge.

Below is a short list of the major roadblocks to HR (specifically talent acquisition and talent management) being truly strategic when it comes to measuring the economic impact of the hiring process. Since the value of the hiring process hinges on the ability to show the impact on the bottom line through the people who are contributing to it, these issues all center around problems related to translating insight about workplace performance into financial metrics. This is a data problem and thus it is no surprise that all of these roadblocks are related to the data used to measure job performance. These issues include:

A lack of value placed on job performance data. When it comes to the impact of the hiring process, job performance data is absolutely essential. Unfortunately, businesses don’t value this type of data or champion the need to collect and manage it. Both my practice and my research clearly show that even those companies that do use assessments rarely make any effort to evaluate their effectiveness. Companies simply don’t place enough value on the importance of this data. Ask any industrial-organizational psychologist who has done test validation and they will tell you that getting the data we need to show the impact of a hiring process is like pulling teeth. Even when firms have sponsored us and paid us handsomely, they still fall flat when we ask them for access to the data we need to do our jobs. Without data, we have absolutely n0 ability to prove anything, and we end up relying on the “trust me, I’m a scientist” angle.

Performance can be hard to measure. To be fair, measuring job performance is not always an easy thing to do. In some areas, such as sales organizations or call centers, key performance indicators are pretty clear cut.  But once you move to the rung of white collar and managerial jobs, performance is more complex and does not lend itself to easy data collection or even definitions. The field of I/O psychology has lived this problem for quite some time and in what is now a classic article for us geeky types, Austin and Villanova suggest that the inability to find quality measures of job performance and “think outside the box” about the issue has created a “criterion problem” that is holding us back from our potential as assets to both business and science. Beyond this, some data, such as subjective ratings of performance made by supervisors, cannot be trusted to tell the whole truth about performance because it may be biased by personal or political motives.

Not enough data is available. Anyone, I/O psychologist or otherwise, who speaks the language of statistics, population sampling, and normal distributions knows that to make credible inferences from data, one needs large samples. Insight based on small numbers of data points simply cannot be trusted. Unfortunately, there are often just not enough people in a given job to provide real insight about the relation between hiring and job performance. Usually the result is either simply ignoring the issue and trying to pass a study with a useless sample size (under 100), or just not doing the study at all (my preference).

Inability to speak the right language. We psychologists and HR folks are famously lousy at communicating with business in practical language. We often end up mired in administrative details or become sidetracked on issues that are of no practical significance to business leaders. The eyeballs of C-suite suits glaze over easily when faced with endless droning about esoteric statistical analyses. Credibility and action with these folks hinges on a few key pieces of high-level info that has dollar signs next to it and which can be processed in five minutes or less. Without the ability to have an economically based discussion based on data that can enlighten business leaders of the value of the hiring process, we remain doomed to failure.

Failure to share information within (and across) industries. Data is something that organizations are most often not willing to share with others in their industry. This presents limitations because it precludes any real high-level insight as to what job performance means within a given industry as a whole. While most companies have their own specific definitions of what performance means, within industries there are major themes around performance. The ability to benchmark using data around these themes presents an opportunity for higher levels of analysis and insight. This drives the ability to better understand the economics of job performance.

So the $64 million question then becomes, “What can we do to connect the data collected in the hiring process and that ultimately drives hiring decisions, directly to financial metrics of job performance that can clearly demonstrate the economic impact of these decisions?”

There is no easy answer to this question, and in fact the solution requires several things to happen. The good news is that all of the right pieces are coming together at present to provide us with the tools to take it to the next level. All it will take is the right mindset and a real interest in using data to provide insight. The critical pieces required to provide the answer include:

A change in mindset. Movement forward requires businesses to create a data-centric culture that values the collection, grooming, and analysis of data. The craze of big data is helping this cause greatly. As time goes on, almost every company will have a more data-focused mindset. This alone is a big key to creating a sea change for the whole organization, C-suite included.

New technologies. We are presently experiencing the emergence of a multi-disciplinary approach to collecting and understanding performance data that is being driven by the makers of technology, (as opposed to end users such as corporations) and business folks who see problems within their industries and want to use technology to solve them. The result is a host of new technology products that will provide the C-suite with the insight needed to view investments in human capital in the same terms as its non-carbon-based capital (i.e., infrastructure). Some interesting new technologies that will help drive insight into the economic impact of job performance include:

  • Streaming validation. Although I have been talking about this term for more than 10 years now, it is still likely to be unfamiliar to 90 percent of those reading this article. It is time that we all become familiar with it because it is a major shift in our industry that is finally becoming reality. Streaming validation means simply the configuration of a technology system that collects both real-time predictor data from the hiring process and also provides a steady, automated stream of performance data. It is the steady combination of these two streams of data that provide us with a foundation on which to build economically based models of job performance. The leading I/O firms are already setting up these types of systems and working hard to get clients on board.
  • Dashboards. One of the most valuable additions to a data-centric mindset is the addition of dashboards that overlay databases and allow users to view real-time summaries of their data and the impact it is having on all sorts of things. The big data and analytics revolution is driving widespread adoption of dashboards across almost every industry, including HR. When combined with a streaming data approach, dashboards provide business leaders with simple tools that allow for directly relevant information. In relation to hiring and job performance, dashboards provide an essential tool for tracking the value of hiring as expressed using a company’s KPIs.
  • Collective intelligence-based rating data. This basically means crowdsourcing … a relatively new phenomenon that is changing the way we evaluate all sorts of things. When applied to the business of hiring, it means the ability to have multiple people share opinions or make ratings to support a higher level of insight about something or someone. Crowdsourcing is poised to provide a new source for data about individuals (reference ratings), company culture, and even specific managers within a company.  The interesting thing is that it will allow for collective information that may be unsanctioned by the company itself (think of Glassdoor), but which represents an important reality that is very valuable to many folks on many levels.
  • Credentialing. The ability to provide digital merit badges, the integrity of which are sanctioned by a credible entity using a credible process, is going to represent a very important source of data. Credentials will provide believable proof that a person has some specific skill, ability, or experience. The real value of them is that they will provide data that can be shared and understood by algorithms and artificial intelligence. It will provide data useful both for predicting who can perform a given job well and for understanding the economic impact of a verified skillset.
  • Longitudinal personal data. If you are a Google user, check out your Google Dashboard to get an idea of the fact that every single thing you do using the Internet is tracked by someone somewhere and rolls up to a unique digital picture of who you are and what you have done. Imagine a similar work-related tool that is provided by someone like LinkedIn or even Facebook and which captures work-related data. Now imagine that your progress in training and development programs is part of this data and is tracked and compared to other data such that the value of the increase in skills you have gained via training could be quantified. We have only just begun to scratch the surface when it comes to ways that a lifetime of personal data can be linked to economic terms, but it’s going to happen.

But cool technologies and products alone will not provide the leverage to pry open the door to the luxury skyboxes of the C suite. To make it happen we as practitioners need to partner with technology providers and figure out how to use what they are creating to support changes in the mindset around the value of data and its ability to translate job performance data into financial terms.

Sharing data and collaboration within (and across) industries. Making  analytics about the financial impact of work performance really take off will really require some form of sharing of data to provide the ability for industries to benchmark vs. a set of standardized performance metrics.  My utopian, populist dream as a scientist centers around businesses coming together to develop a standardized language of performance that has relevant metrics within specific industries and and verticals.  I am starting to see this as the true doorway to the next level when it comes to tracking and understanding the true value of human capital. Imagine a set of generalized, quantifiable, standardized performance metrics that are shared by all organizations.  This would allow for each company to cultivate their own data and then contribute anonymously to a cloud system that would store it and support benchmarking. 

Thinking outside the box. We need to begin leaving behind our traditional notions of what performance data looks like and take an expanded view of what data may help provide clarity.  Since data is a byproduct of almost everything one does these days, there is ample opportunity to mine data from all areas of an organization and to look for patterns that may not be intuitive but which end up telling an important story.

The answer to the question of how performance can be tied to economic metrics has many facets and is not yet entirely clear. But the common threads are data and technology. Data is information that can be used to drive system-based prediction and which will serve to “close the loop” and demonstrate the ROI of all types of things. It is happening in almost every industry, and it is going to be a big part of the movement that will help catapult we HR people from our (perceived) role as backroom administrative hacks, right to the boardroom table on the penthouse floor.

The sooner you and your firm’s HR leadership embrace the role of data and technology systems in quantifying performance and begin to really care about your data and align it with technology, the sooner you will have leverage to pry open the golden door and help push your company’s stock ticker into overdrive.

This article is provided for informational purposes only and is not intended to offer specific legal advice. You should consult your legal counsel regarding any threatened or pending litigation.

  • http://www.HRMC.com Larry Cummings

    The lack of effort in measuring job performance data is not as troublesome as the total disregard for #TalentAcquisition KPI’s

    “Since we promote people internally based on their performance, we should hire new people the same way.” Some performance gets measured but unless it can be matched up with hiring metrics there is no ROI (Return On Information).

    - Handler says it better.