By Brad Minor, M.Ed. Candidate in Human Resource Development, Peabody College of Vanderbilt University
What does it mean to be analytical? Dr. Fitz-enz (2009) notes three types of analytics in his article, Predicting People: From Metrics to Analytics: descriptive, prescriptive, and causation (also known as cause-and-effect reporting). Descriptive analytics “reveals and describes relationships and differences between … groups,” while prescriptive analytics “relates what you know currently to what you want to know about the future” (Fitz-enz, 2009, p.5). Cause-and-effect reporting is the most valuable, and probably the deepest, level of analysis (Fitz-enz, 2009). According to Sharon Spence (2010), the Human Capital Business Leader at Mercer New Zealand, “Facts about cause-and-effect relationships add the greatest human capital intelligence because they allow you to predict the impact that decisions about human capital can have on performance or other outcomes.”
Why Should We Bother to Predict Anything?
Why not just continue reporting our past and present measures? According to Fitz-enz (2009), “Predictive analytics combines information on what has happened in the past, what is happening now, and what’s likely to happen in the future to give you a complete picture of your situation” (p.8).
In 2007, The Hackett Group reported (as cited in Fitz-enz, 2010) that corporate earnings can grow 15% just by improving a company’s talent management function; they also “found a strong correlation between improved financial performance and top-quartile performance in four key talent-management areas”: strategic workforce planning, staffing services, overall organizational effectiveness, and organizational design and measurement (Fitz-enz, 2010, p. 55). Let’s consider each of these for a moment. Strategic workforce planning involves forecasting future employment levels based on projected growth and change. Successful staffing services – particularly recruitment, which we will revisit momentarily – require valid predictive measures of employee success. Organizational effectiveness includes performance management, which involves predictions about what will motivate people and how their individual competencies might be developed. Organizational design and measurement involves picking the right measures (asking the right questions) and designing organizations in a way that is projected to be most efficient and effective in both the present and future. Clearly, predictive analytics are the key to optimal organizational planning and bottom-line results.
The Use of Predictive Analytics in Recruitment
The use of predictive analytics in recruitment warrants further discussion, because it is one of the most vital components of any organization’s success. Companies need to select high-performing individuals in order to maximize productivity and results, so the selection and use of instruments and processes that identify such talent is critical. It is important that these measures are valid predictors of success on the job, not only for the sake of performance, but also because this documented validity is important in employment litigation. The best predictors of success on the job, according to a study by Schmidt and Hunter (as cited in Pfeffer & Sutton, 2006) are “measures of general mental ability,” such as IQ scores which, across studies, appear to have a correlation to performance of somewhere around (or just under) 0.4, with intelligence accounting “for no more than 16% of the variation in performance”; other successful predictors that they note include work sample tests, job tryouts, structured interviews, and conscientiousness. Measures of general intelligence should be used with caution, however, because scores are only valid predictors of success on the job up to a certain threshold; differences beyond this threshold are somewhat meaningless (Effron & Ort, 2010).
Person-job and person-team fit should also be taken into account during recruitment. According to Presser (2006), in reference to person-team fit, “fit is key to increasing human capital.” She is a champion of “the metric of human capital synergy,” which she defines as the “measurement of the effect that happens when we put the right people together” (Presser, 2006).
As the selection example indicates, being analytical can yield great benefits. Davenport, Harris, and Morison (2010) note seven important benefits that come from being analytical:
- Help manage and steer the business in turbulent times
- Know what’s really working
- Leverage previous investments in IT and information to get more insight, faster execution, and more business value in many business processes
- Cut costs and improve efficiency
- Manage risk
- Anticipate changes in market conditions
- Have a basis for improving decisions over time (p. 3)
Marketing materials for SPSS, a software product sold by IBM, also list a number of benefits brought about by analytics:
- Get a higher return on your data investment
- Find hidden meaning in your data
- Look forward, not backward
- Deliver intelligence in real time
- See your assumptions in action
- Mitigate risk
- Discover unexpected opportunities
- Guarantee your organization’s competitive advantage (as cited in Fitz-enz, 2009, p.8)
According to Pfeffer & Sutton (2006), it is important to keep in mind that systems are often more important than people, but because the two are so tightly interwoven in the workplace, human resource professionals and human capital analysts should also pay close attention to people-related systems when engaging in the analysis of human capital. Many systems in the workplace affect people, so the interaction between people and systems should be considered when diagnosing problems or analyzing extant data. Systemic issues might not always be part of our measurements, but they may be part of a problem’s root cause. Analytics related to this interaction might be a good topic for further study, research, and development, because it is difficult to find existing analytics that address this.
Effron, M., Ort, M. (2010). One Page Talent Management: Eliminating Complexity, Adding Value. Boston, MA: Harvard Business Press.
Fitz-Enz, J., (2010). The New HR Analytics. New York: AMACOM.
Fitz-Enz, J. (2009, Autumn). Predicting people: from metrics to analytics. Employment Relations Today, 36(3), 1-11.
Fitz-Enz, J. (2009, August). Predictive leadership. Leadership Excellence, 26(8), 20.
Pfeffer, J., & Sutton, R. (2006). Hard Facts, Dangerous Half-Truths and Total Nonsense. Boston: Harvard Business School Press. (Amazon Kindle e-book)
Presser, J. (2006). Approaching a metric of human capital synergy. Retrieved 11 21, 2010, from shrm.org: http://www.shrm.org/Research/Articles/Articles/Pages/CMS_015839.aspx