Michael Schrage Research Fellow, MIT Center for Digital Business wrote an article recently that described how an employee’s “professional relationships are being algorithmically evaluated to help assure they are really worth “investment on the part of their employers. What follows is an interesting discussion with most commenters against the use of statistics, analytics and predictions when working with employees.
I find it curious to observe the criticism when analytics and statistics are applied to employees, versus the open embrace of analytics when used to predict customer behavior. Aren’t customers and employees both people?
There’s no going back on the fact that numbers are the language of business. What’s fantastic is that employees also have numbers that help to describe and value their characteristics. (Notice I said “also” not “only”). To be valued as a business asset, employees also need to be described by numbers. Analytics, statistics and new technologies make it possible to include employee numbers as an asset (or not) in understanding a business’ portfolio.
Many employee-related numbers utilized in analytics models (attrition, time to hire, compensation etc.) highlight little that is strategic about their employee’s ability to execute on business strategy. This presents a major risk to a business’ ability to meet business goals and deliver shareholder value. From a risk assessment perspective, this is one of the largest risk factors and major a blind spot. It is also an opportunity with potential for large ROI.
Reading comments on Schrage’s article highlights the rampant fear around the topic of adding numbers to highlight employee characteristics. I understand it of course. Employees often feel (and are) undervalued by businesses. Lack of understanding of analytics as a discipline can lead employees to be concerned that analytics will lead them to being even more undervalued.
Like the healthcare system, the global system of recruiting, hiring, managing and aligning talent to goals is terribly broken. Yet, fear of the unknown means that businesses are repeating mistakes while hoping for a better outcome. Really, this is the definition of insanity.
In the knowledge economy, as ‘potential’ becomes more important as a predictor to success than hard skills, the “current system of hiring” becomes even more difficult without statistical models to help identify candidates and the employees predicted to perform.
I think we as a discipline can help non-analytics professionals understand that numbers and analytics are critical, particularly when predicting employee performance. We can do much to help non-analytics leaders understand that analytics makes it possible to get in front of the problem (during the hiring process) to help predict where candidates are likely to excel (and where they are likely not to). Isn’t it better to set someone up for success instead of failure?
The analytics community can help those outside our discipline to understand that numbers make it possible for employees to be found and valued inside of our businesses. Numbers allow a company to find the natural innovators, or complex problem solvers, or out-of-the-box-thinkers and include them on projects (even if they’re not in the department where you would expect). Numbers are rational and logical – they should be less scary than a manager or group or company that could hate you for no reason. Numbers give insight into errors in judgment. Today’s interviewing, resumes, job offers and soft performance measures keep results of good or bad decisions in the dark.
The analytics community has successfully embraced the use of analytics with customers (people) to affect business performance. It seems like a small step for us to help the business community embrace the use of analytics with employees (also people) as well.
Originally published by the International Institute for Analytics.
Greta Roberts is a Faculty Member of the IIA and CEO of Talent Analytics, Corp. Follow her on twitter @GretaRoberts.