There is a dawning awareness that data and information, as a commodity in and of itself, has little value to an organization unless it is transformed into meaningful intelligence. The sheer volume of Big Data that organizations can and do amass is overwhelming. What is needed is the type of alchemy that transforms data and information into analytics and intelligence vis-a-vis an evidence-based approach. In the context of human capital management, HR intelligence, as derived from HR research and analytics practices, is a fast emerging mandate for organizations seeking strategic competitive advantage.
Advancing HR Analytics
The topic of HR intelligence or what is more popularly and perhaps narrowly referred to as human capital, talent, people, and/or HR analytics is one of the hottest trends in the context of HR strategy and decision making. Several notable thought-leaders have called for the HR profession to adopt an evidence-based management, decision science, HR intelligence, and predictive analytics approach to understanding and managing human capital in order to improve individual and organizational performance (Pfeffer & Sutton, 2006; Boudreau & Ramstad, 2007; Falletta, 2008; Fitz-enz, 2010 respectively). With the exception of a handful of high-profile case studies (e.g., Google, IBM, and Morgan Stanley), little is known about the extent to which Fortune 1000 and select global companies are performing broader HR research and analytics practices beyond simple descriptive metrics and scorecards, and more importantly how such activities are being used to facilitate HR strategy, decision making, and execution.
This article summarizes the results of The HR Analytics Project conducted by the Organizational Intelligence Institute and Drexel University. The HR Analytics Project is the largest study to date on the topic of HR research and analytics in terms of the number of participating companies representing the Fortune 1000 and select global firms.
The purpose of the study was to gain insight into the extent to which these high performing companies (i.e., high performing firms in terms of annual gross revenue) are conducting a wider range of HR research and analytics practices in the context of human resource strategy and decision making. Several key areas related to HR research and analytics were explored, including:
1. The types of HR research and analytics practices being performed in high performing companies
2. Organization and structured of HR research and analytics
3. HR research and analytics role in HR strategy, decision-making, and execution
4. The meaning of "HR intelligence"
5. The emerging ethical implications associated with the predictive analytics movement
Over 3,000 HR professionals representing the entire Fortune 1000 as well as select global firms were invited to participate in the survey. The survey included 29 core items with a number of secondary items and various response alternatives (e.g., Likert-type scale, yes/no, rank order), as well as several open-ended questions. Some of the items were adapted from a benchmarking study conducted in 2001 by the principal researcher on the topic of HR intelligence practices (Falletta, 2008) while other variables were adapted and used from a survey instrument developed by senior research scientists at the University of Southern California's Center for Effective Organization (Levenson, Lawler, & Boudreau, 2005; Levenson, 2011). In addition, a targeted, snowball sampling approach was used to promote and generate interest in the project through several notable membership consortia such as The Mayflower Group, Information Technology Survey Group (ITSG), and Attrition and Retention Consortium (ARC), as well as a number of Linkedin groups dedicated to HR metrics and analytics, HR intelligence, employee engagement surveys, work, force planning, and human capital strategy.
In total, 220 distinct companies completed the web-based survey representing 47 different industries. No duplicate responses were received (i.e., all recipients of the invitation to participate in the survey forwarded the survey URL to the best individual or group responsible for HR research and analytics within their company). Of the 220 companies that participated, 195 were Fortune 1000 companies and 21 were global firms headquartered outside of the United States. Of significance, 39 participating companies were Fortune 100 firms. In terms of respondent characteristics, 87% (n = 187) were senior HR leaders and specialists who regularly perform broader HR research and analytics work (e.g., metrics, employee/organizational surveys, assessments, evaluation, applied human capital and organizational behavior research).
The first research question focused on the types of HR research and analytics practices that are currently conducted in high performing companies. The survey asked participants to rate the importance of 18 HR research and analytics practices in terms of influencing HR strategy and decision-making (see Table 1).
Employee and organizational surveys received the highest importance ratings in the study, (overall mean rating of 4.15), which isn't too surprising given that surveys are one of the most prevalent and widely used methods for collecting data and information about employee's thoughts, feelings, and behaviors. While a mainstay for years among HR researchers and skilled OD practitioners, employee and organizational surveys appear to be evolving in importance with respect to HR research and analytics capabilities at high-performing companies.
Surveys in general are commonly used for varied purposes in the context of human capital strategy and management (e.g., assessing training needs, evaluating programs and solutions, measuring employee perceptions and attitudes, conducting organizational research). The larger companies in the sample (e.g., Fortune 100), however, tend to construct and deliver strategically focused employee and organizational surveys that account for key factors and variables that enable, inhibit, and in some cases predict employee engagement and other important individual and organizational outcomes (Falletta, 2008b). For many, the annual, company-wide employee survey serves as the primary data feed for HR strategy formulation and human capital decision making.
In terms of the type of HR research and analytics practices, a closer examination of the data gleaned the following observations and insights.
* Fortune 100 and large global firms rated "employee and organizational surveys" as slightly more important (4.33 and 4.24 respectively) as compared to the overall mean rating (4.15) and other Fortune categories.
* High-performing companies in terms of size and gross revenue tend to invest a significant amount of resources and time on employee and organizational survey initiatives. Over a third of all respondents (36.4%, n = 80) reported employee and organizational surveys as the most expensive or costly to perform and the third most time-consuming HR research and analytics practice.
* The larger companies, such as Bank of America, Dell, Eli Lilly, Ford, Google, Intel, Microsoft, Nike, IBM, Target, and SAP, benchmark and compare their survey results through employee research membership consortiums, such as The Mayflower Group (www.mayflowergroup.org) and Information Technology Survey Group (www.itsg.org). In doing so, member companies can make industry and cross-industry comparisons by job family, similar groups, business units, and/or functions.
* Respondents rated advanced OB research and modeling as the most time-consuming and most difficult to perform. Whereas, talent supply chain (e.g., analytics to make decisions in real time for optimizing immediate talent demands in terms of changing business conditions) was rated the second most difficult to perform, which is consistent with previous research and observations (Davenport, Harris, & Shapiro, 2010).
* Surprisingly, the literature review received the second lowest importance rating (2.82), while global firms (companies headquartered outside of the US) rated the importance of literature reviews significantly higher than all other Fortune categories, thereby suggesting a greater interest in and orientation towards evidence-based HR in terms of HR strategy and decision making.
* Operations research and management science received the lowest rating (2.33) in terms of facilitating HR strategy and decision making--although interest in optimization methods as well as the emerging application of artificial intelligence (i.e., expert systems and machine learning) to HR management decisions are likely to increase as advancements in skills, capabilities, and technology continue (Sesil, 2014).
Organization and Structure of HR Research & Analytics
The second research question explored how HR research and analytics activities and groups are organized and structured within high-performing companies. Over three-quarters of all participating companies (76.8%, n = 169) indicated that they have an individual or function dedicated to HR research and analytics. In terms of staffing levels for the HR research and analytics function, 62% of the companies reported staffing levels of five or less people in the group, and 92% reported 12 or less people assigned to this function. Additional analyses found that the staffing level of this function was higher in companies with higher gross revenues and a larger workforce.
It important to note that these results merely reflect the staffing levels within dedicated HR research and analytics groups. It is quite likely that overall staffing levels of those who perform HR research and analytics work may be underreported since many large firms typically decentralize and embed HR professionals through the organization (e.g., HR business partners, OD consultants). There also may be those outside of the HR function (e.g., IT or Finance specialists) doing some form of analytics work in context of human capital management. Further, these results do not suggest that the remaining participating companies (those without a dedicated function or group; 23.2%, N = 51) are not engaged in HR research and analytics practices. It is clear that all of the participating companies are performing HR research and analytics work at some level (as evidenced in Table 1).
Nearly a third (31.4% N = 53) of all dedicated HR research and analytics groups report directly to the Chief HR Officer (i.e., head of HR) suggesting that these functions are strategically positioned in terms of organizational structure, whereas, the mean and mode were only two levels down from the top, indicating a substantial degree of organizational status being accorded to this function.
While the function or group "names" vary, the nature and content of the practices and activities appear to be HR research and analytics related. Table 2 lists the most common functional or group names. HR analytics was the most common function or group name (N = 13), followed by HR intelligence (n = 7), workforce analytics (N = 7), and talent analytics (n = 6) respectively.
Role in HR Strategy & Decision Making
The third research question addressed the extent to which HR research and analytics facilitate HR strategy, decision-making, and execution.
The response alternatives and their frequencies of choice are reported in Exhibit 1.
HR analytics is characterized as having input into HR strategy formulation but not playing a central role in its formulation in about half (49.5%) of the companies in the study. A central role in HR strategy was reported for less than 15% of the companies, whereas in nearly 37% of the sample, HR analytics is characterized as playing little or no role in HR strategy formulation.
When asked to elaborate or provide additional information about the HR research and analytics role in influencing HR strategy formulation and decision-making specifically, an overarching theme emerged in which broader HR research and analytics practices were largely described as an exhaustive data gathering exercise (i.e., a data dump), whereby preconceived notions or after-the-fact, HR strategies and decisions drove the actual data requirements.
In short, HR analytics has a long way to go. More often than not, data and analytics are used to support decisions that have already been made rather than to question the current path of HR strategy and planning within large companies.
According to Pfeffer and Sutton, in their book Hard Facts, Dangerous Half Truths, and Total Nonsense (2006), the idea of using data to make decisions changes the power dynamics in a company. For example, a powerful and/or narcissistic leader would probably prefer to make decisions based upon his or her opinions and intuition rather than relying on the good facts and figures (i.e., evidence). Similarly, Sesil explains in his recent book, Applying Advanced Analytics to HR Management Decisions (2014) that those in positions of power might have fragile egos and be primarily concerned with advancing their own agenda rather than dealing with actual facts. Indeed, further work is needed in terms of elevating the status and legitimacy of HR analytics and its influence on HR strategy and decision making.
The beauty of advanced analytics, according to Sesil, is that it "does not care who it annoys" (2014, pg 11).
While speaking truth to power can be risky (and a little fun), we need to recognize that HR analytics is both an art and science. That is, we shouldn't abandon our intuition and well-seasoned expertise (Sesil, 2014). Davenport, Harris, & Morison (2010) in their book Analytics at Work: Smart Decisions, Better Results, describe the limitations of analytics and the role of quantitative and qualitative data. For example, a purely analytical and dispassionate approach to human capital decisions is a recipe for organizational analysis paralysis. Likewise, making critical HR decisions solely based on prior experience, intuition, gut feelings, and/or management fad du jour could have disastrous effects. In short, we need to balance the art and the science of HR analytics while adopting an evidence-based HR orientation and raising the bar in terms of advanced analytics literacy (Bassi, 2011).
Core HR Intelligence Capabilities and Processes
The second group of survey items included 24 HR practices and processes that were rated on an 11-point scale of HR Intelligence, reflecting degrees of FIR research and analytics capabilities (i.e., level of sophistication) in terms of human capital decision-making (refer to Exhibit 2).
For the purposes of this study, the HR Intelligence Value Chain was adapted from HR Intelligence Hierarchy--which included three levels namely--Data, Information, and Intelligence (Falletta, 2008). While the HR Intelligence Value Chain is by no means a validated scale in terms of measurement validity and reliability, it does provide a practical framework with which to estimate and gauge HR intelligence capabilities as a first step in conducting applied research on the topic.
The ratings of these 24 HR activities are reported in Table 3 and Table 4 respectively.
Employee and organizational surveys received the highest "HR intelligence" ratings (mean score of 6.59 on the 11-point scale) and was the only HR practice on the cusp of what could be considered "analytics" (7 and 8 on the scale) in terms of HR intelligence capabilities and level of sophistication. Employee engagement and retention (6.05), compensation (5.90), HR strategy (5.62), and workforce planning (5.54) rounded out the top five. As expected, the larger Fortune 100 firms were slightly ahead of the curve in terms of their HR intelligence rating across all of the HR practices.
Knowledge management received the lowest "HR intelligence" ratings (mean score of 3.48 on the 11 point scale) in terms of HR intelligence capabilities and level of sophistication. Organization design (3.86), organizational learning (3.92), employee on-boarding, (3.94), and career development (4.07) rounded out the bottom five. Again, the larger Fortune 100 firms were slightly ahead of the curve in terms of their HR intelligence capabilities across all of the HR practices.
It shouldn't be too surprising that knowledge management and organizational learning were in the bottom five. Definitional problems persist and many companies still struggle to effectively implement these evolving practices. Organization design has been around for years in OD circles and there are a number of excellent publications on the topic, yet internal HR or OD practitioners rarely get to play in this space. Senior executives typically sort out such matters on their own behind closed-doors--either as a senior leadership team or in consultation with one of the big Ivy-League consulting firms.
Lastly, it should be noted that no HR practice was rated at the "intelligence" level (9 to 10) for any of the Fortune categories--thereby suggesting that HR intelligence is much more of an analytical aspiration at this point for many companies. The route to building HR intelligence capability that can improve human capital decision making will depend on the level of HR analytical maturity as well as the extent to which a given company embraces evidence-based HR.
The third and final group of survey items in the Core HR Analytics Capabilities & Processes section of the survey asked participants to rate their effectiveness on a 5-point scale (1 = very ineffective, to 5 = very effective) on six core activities associated with HR research and analytics work (see Table 5). These six statements were derived from a previous study conducted in 2001 which asked participants to describe what "HR intelligence" (i.e., broader HR research and analytics activities) meant to them (Falletta, 2008).
The mean rating for linking multiple data and information sources to predict, model, and forecast individual, group, and organizational behavior performance outcomes was relatively low. For many participating companies, this particular activity is still a very challenging and emerging core capability. As described earlier, respondents rated "advanced OB research and modeling" as the most timing-consuming as well as most difficult to HR research and analytics practice to perform.
Who Determines the HR Research and Analytics Agenda?
Respondents were asked to indicate whether the company conducts a formal HR research and analytics agenda process. Interestingly, only 39.5% (N = 87) of participants reported having a formal HR research and analytics agenda process despite the fact that 76.8% (n = 169) of all participating companies indicated that they have a function or group dedicated to HR research and analytics. This might suggest that HR research and analytics activities and its prioritization are largely reactive and stakeholder and customer driven rather than proactive and research and analyst driven. However, on average, nearly 40% of all HR research and analytics work was identified as "proactive" (39.3%, n = 215) and determined by the HR research or analytics team (40.3%, n = 215), while approximately 60% of all HR research and analytics work was identified as "reactive" (59.7%, n = 215) and stakeholder or customer driven (60.7%, n = 215). In short, this demonstrates a relatively balanced approach in terms of determining the actual HR research and analytics agenda.
The Meaning of HR Intelligence
The forth research question explored the meaning of "HR intelligence" by those who perform HR research and analytics. Respondents were asked to rank in order seven items in terms of how accurately they describe what HR research and analytics means. The rankings of these items are reported in Table 6. The rank order is presented in ordinal fashion (i.e., 1, 2, 3, 4, 5, 6, and 7) for the sake of simplicity and includes the actual mean rank. The overall mean rank was 4.09. While there are certainly a diversity of views, the first two (Rank 1 and 2) emerged as significantly more descriptive than the others as to the central activities of HR research and analytics.
What Is HR Intelligence?
In the spirit competitive or business intelligence, HR intelligence is defined as "a proactive and systematic process for gathering, analyzing, communicating and using insightful HR research and analytics results to help organizations achieve their strategic objectives" (Falletta, 2008, pg. 21). In order to effectively build robust HR intelligence capabilities that are both proactive and systematic, HR intelligence must be positioned as an ongoing cycle involving seven steps (see Exhibit 3).
Robust HR intelligence capabilities extend beyond HR metrics. HR intelligence enables human capital decisions that are based on insightful HR analytics which are largely predictive and supported by a synthesis of the best available scientific evidence (i.e., evidence-based HR) (see Exhibit 2). The key differentiator between HR analytics and HR intelligence is that the latter is supported by empirical and theoretical research (i.e., scholarly evidence that resides outside of your organization).
Lastly, merely mining and modeling your internal employee data is tantamount to a theory free, correlation fishing expedition unless such data and insights can be analyzed and supported in relation to other sources of internal and external data. Only then can you make valid and reliable predictive assertions and prescriptive recommendations.
"Don't Be Evil"
All professions, like HR, are built around norms, values, and ethical principles about how professionals and organizations are to conduct themselves. In this study, an attempt was made to investigate ethical judgments associated with HR research and predictive analytics. Ethical questions have begun to arise about the potential abuses of HR analytics with respect to technological advancements and mining and modeling "Big Data" (Bassi, 2011).
Twenty-one practices were selected and included in the survey--some of which have had a long history of controversy--from intelligence (IQ) and personality testing to forced-ranking in performance appraisals to employee performance monitoring and surveillance technologies. These practices have always incited spirited debates among academicians and practitioners with respect to the appropriateness of using such methods and tools for human capital decisions.
Pre-coding employee survey demographic variables have raised a few questions in recent years (Saari & Scherbaum, 2011). A handful of emerging and unconventional practices, such as Google's elaborate survey that explores a job applicant or employee's attitudes, preferences, and values on seemingly innocuous aspects of their personal life (e.g., "what magazines do you subscribe to?" and "what pets do you have?") (Hansell, 2007), as well as identifying a job applicant's "hometown" as a relatively accurate predictor of attrition (Ganguly, 2007), are dubious at best. More recently, private data and information obtained from social media websites (e.g., Facebook), whereby employers ask a candidate or employee to furnish his/her user-ID and password, have garnered national attention.
The fifth and final research question in this study attempted to gain insight into the ethical implications associated with the HR research and predictive analytics movement. Respondents were asked to rate 21 workforce data collection and HR analytics practices on a five-point scale of appropriateness ranging from absolutely inappropriate to absolutely appropriate. The appropriateness ratings of these 21 practices are reported in Table 7.
There were five practices that had mean ratings which were both significantly higher than the overall mean (2.80) and fell into the appropriate scale interval. These are listed below from highest-rated downward.
* Performance appraisal/evaluation ratings
* Pre-coding survey demographic data in general
* Pre-coding survey demographic data from "top talent" employees
* 360 degree feedback results for leadership development purposes
* Personality assessment results
Five of the practices had means that were both significantly lower than the overall mean and which fell into the inappropriate scale interval. These are listed below (ordered from lowest upward):
* An individual employee's prescription drug usage obtained legally
* Private data and information obtained from social media websites (e.g., Facebook and the like) whereby the employer asks a candidate or employee to furnish his/her user-ID and password
* A job applicant's "hometown" or where they were born and raised
* Surveillance video to monitor work patterns and behavior
* Tracking whether a new employee signed up for the company retirement program as an indicator of early turnover
It is noteworthy that 76% of the listed practices were considered neutral or inappropriate by the sample as a whole. Needless to say, much more research is needed on ethical issues associated with HR research and predictive analytics. This study attempted to explore ethical judgments on select practices pertaining to human capital decisions in the broadest sense. However, it is quite likely that individual ethical judgments will vary and depend on the type of human capital decision being made (e.g., hiring, job/work assignments, performance management, advancement/promotion, demotion, reduction-in-force efforts).
The results of the study suggest that the landscape for using data and information has shifted dramatically, and that leading companies are building strategic capabilities and competitive advantage through advanced HR analytics practices. As expected, the companies surveyed are performing a broad range of HR research and analytics practices that extend beyond simple metrics and scorecards. However, the profession still has a long way to go to play a more influential role in HR strategy development and decision making.
Another vexing challenge, that wasn't specifically addressed in this study, has to do with making sense of the disparate data sources from all of the HR research and analytics activities. Sure, numerous advancements and innovations have been made by leading edge software firms (e.g., Oracle, SAP, and Workday) that have incorporated workforce analytical capabilities within their suite of products. None of these SacS-based tools, however, can magically codify, analyze, and interpret all of the "Big Data" at our disposal. When it comes to a company's annual HR strategy and planning cycle, much of work is still done manually by expert HR researchers, analysts, and data scientists.
Lastly, our success hinges upon our collective ability to harness the power of advanced analytics, ethically and responsibility, while raising the bar to be more evidence-based as we recommend and implement HR policies, programs, and practices. In sum, proactive HR intelligence arms strategists and decision-makers with pertinent knowledge and insight to make critical decisions pertaining to human capital.
OBSERVATIONS & INSIGHTS--WHO SHOULD OR CAN DO ANALYTICS?
Driving a proactive HR research and analytics agenda is a critically important capability in terms of enabling strategic human capital decisions. Therefore, HR researchers and analysts should bring their own "HR intelligence" and expertise to the table. Many of the respondents in this study hold advanced degrees in the social, behavioral, and organizational sciences and are arguably in the best position to design and interpret robust HR research and analytics results. While an HRIS, IT, and/or financial analyst might possess the technological and statistical chops to mine and model data, it takes an applied researcher with the right disciplinary background to accurately interpret the data and identify any predictive insights in the context of individual, group, and organizational behavior.
Source: Falletta, S., Organizational Intelligence Institute, 2013
OBSERVATIONS & INSIGHTS FIRST, DO NO HARM
One disturbing trend I've experienced firsthand involves HR professionals having difficulty distinguishing between the law and ethics. For example, during a recent conference in which I was invited to speak on HR intelligence, I shared a few questionable HR analytics practices, including the one about an applicant's hometown being used as a relatively accurate predictor of attrition. Afterwards, a well-known and highly respected HR metrics consultant stood-up and said, "I have no problem with it as long as it's legal and doesn't involve a protected group." While sharing the same examples during a recent presentation, I've received mixed reactions, surprisingly, from a few very experienced and competent industrial and organizational psychologists who seem to be grappling with their company's workforce data collection and HR analytics practices --in terms of their own underlying values and professional code of conduct (i.e., APA's Ethical Principles of Psychologists and Code of Conduct and in particular the general principle--First, Do No Harm). Clearly, further discussion and debate are needed about ethics in general and the application of HR analytics in particular (Bassi, 2011).
All of this begs the question: should HR professionals and line managers make human capital decisions based on an applicant's hometown? What about an employee's pet preferences or favorite ice cream flavor? I suppose dog lovers from small towns are more loyal and committed than cat people born and raised in the urban jungle, and just maybe--butter pecan employees have a higher EQ and make better leaders than plain ole vanilla folks. Irrespective to any predictive utility, how appropriate is it to use such data and information for human capital decisions?
When I got off my soapbox, a quick-witted colleague and old friend said to me that the "genie is already out of the bottle and it will probably take Federal legislation to sort it out." Meanwhile, if HR professionals are willing to proactively address such ethical quandaries and challenge questionable HR analytics practices regardless of any real or perceived predictive value--there is indeed a bright future for HR analytics.
Bassi, L. (2011). Raging debate in HR analytics. People & Strategy, 34(2), 14-18.
Boudreau J. & Ramstad, P. (2007). Beyond HR: The New Science of Human Capital, Boston, MA: Harvard Business School Press.
Davenport, T., Harris, J., & Morison, R. (2010). Analytics at Work: Smarter Decisions, Better Results, Boston, MA: Harvard Business School Press.
Davenport, T., Harris, J., & Shapiro, J. (2010). Competing on talent analytics. Harvard Business Review, 52-58.
Falletta, S. (2008). HR intelligence: Advancing people research and analytics. International HR Information Management Journal. 7 (3), 21-31.
Falletta, S. (2008b). Organizational intelligence surveys. Training & Development, 52-58.
Fitz-enz, J. (2010). The New HR Analytics: Predicting Economic Value of Your Company's Human Capital Investments. New York, NY: AMACOM.
Ganguly, D. (2007, February 23). Taming the beast: Psychometric profiling, demo graphic regression models, and predictive algorithms. The Economic Times.
Hansell, S., (2007, January 3rd). Google's answer to filling jobs is an algorithm. The New York Times Online.
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Pfeffer, J. & Sutton, R. I. (2006). Hard Facts, Dangerous Half-Truths, & Total Nonsense: Profiting from Evidence-Based Management. Boston, MA: Harvard Business School Press.
Saari, L. & Scherbaum, C. (2011). Identified employee surveys: Potential promise, perils, and professional practice guidelines. Industrial and Organizational Psychology, 4(4), 435-448.
Sesil. J. C. (2014). Applying advanced analytics to HR management decisions: Methods for selection, developing incentives, and improving collaboration. Saddle River, NJ: Pearson.
Dr. Salvatore Falletta is EVP and Managing Director for the Organizational Intelligence Institute (www.oi-institute. com)--a Skyline Group company. Dr. Falletta also is Associate Professor and Program Director for Human Resource Development at Drexel University.
Prior to Organizational Intelligence Institute and Drexel, he was President and CEO of Leadersphere, served as a Vice President and Chief HR Officer at a Fortune 1000 firm based in the Silicon Valley, and has held senior management positions in human resources at several global companies, including Nortel Networks, Alltel, Intel, SAP AG, and Sun Microsystems respectively.
Dr. Falletta is an accomplished speaker, researcher, and author and is currently writing a book on HR Intelligence, Strategy, and Decision Making. He can be reached at firstname.lastname@example.org.
TABLE 1. IMPORTANCE RATINGS OF HR RESEARCH AND ANALYTICS PRACTICES HR Research & Analytics Practice Mean N Employee and organizational surveys (e.g., employee 4.15 220 opinion surveys, engagement surveys, organizational culture/climate surveys, organizational health surveys, organizational effective ness surveys, organizational alignment surveys) Employee/talent profiling (i.e., tracking and modeling 3.64 215 individual data on critical talent or high-potential employees) HR metrics and indicators 3.63 218 Partnership or outsourced research including 3.60 213 membership-based research consortia such as the Corporate Leadership Council, The Conference Board, University of Southern California's Center for Effective Organizations, Cornell's Center forAdvanced Human Resource Studies, and the Institute for Corporate Productivity (i4CP) to name a few HR scorecards and dashboards 3.57 211 Workforce forecasting (e.g., workforce supply/demand 3.55 215 and segmentation analysis to forecast and plan when to staff up or cut back) Ad hoc HRIS data mining and analysis 3.50 218 HR benchmarking 3.27 215 Training and HR program evaluation 3.27 220 Labor market, talent pool and site/location 3.23 215 identification research Talent supply chain (e.g., analytics to make decisions 3.23 172 in real time for optimizing immediate talent demands in terms of changing business conditions) Advanced organizational behavior (OB) research and 3.13 208 modeling (e.g., linkage studies, driver analysis, correlation and regression analysis, factor analysis, path analysis, causal modeling, and structural equation modeling procedures) Selection research involving the use of validated 3.07 210 personality instruments that measure various employee traits, states, characteristics, attributes, attitudes, beliefs, and/or values Return-on-investment (ROI) studies 3.05 212 Qualitative research methods including case studies, 3.01 212 focus groups, and content or thematic analysis 360 degree or multi-rater feedback (e.g., 360 degree 2.93 218 leadership and management assessments) Literature review (e.g., a review and synthesis of 2.86 214 existing or secondary data sources such articles and research reports including evidence-based and scholarly/peer-reviewed journal articles) Operations research and management science (e.g., 2.33 148 optimization methods such as linear programming; stochastic processes/ Markov analysis; Bayesian statistics, computational modeling, and simulations) Source: Falletta, S., Organizational Intelligence Institute, 2013 TABLE 2. MOST COMMON FUNCTION OR GROUP NAMES HR Analytics N = 13 HR Intelligence N = 7 Workforce Analytics N = 7 Talent Analytics N = 6 HR Insights N = 5 HR Reporting N = 5 Employee Insights N = 4 Global HR Insights N = 3 HR Technology N = 3 HRIS N = 3 Human Capital Intelligence N = 3 Talent Management & Analytics N = 3 Employee Surveys & Insights N = 2 HR Quality & Analytics N = 2 HR Research N = 2 HR Strategy N = 2 Organizational Insights N = 2 People Analytics N = 2 People Metrics N = 2 People Research N = 2 Surveys & Assessments N = 2 Workforce Intelligence N = 2 Workforce Measurement N = 2 Workforce Planning N = 2 Workforce Research N = 2 Source: Falletta, S., Organizational Intelligence Institute, 2013 TABLE 3. HR INTELLIGENCE CAPABILITIES BY HR PRACTICES, PROGRAMS, AND PROCESSES (TOP 12) Highest rated HR practices in terms of HR intelligence Mean N capabilities 1. Employee & organizational surveys 6.59 214 2. Employee engagement & retention 6.05 212 3. Compensation 5.90 215 4. HR strategy 5.62 215 5. Workforce planning 5.54 215 6. Competency & talent assessments 5.35 214 7. Benefits 5.34 215 8. Performance appraisal & management 5.29 214 9. Reduction in force & downsizing 5.14 206 10. HR legal & compliance 5.11 212 11. Succession planning 5.09 215 12. Recruitment 5.03 214 Source: Falletta, S., Organizational Intelligence Institute, 2013 TABLE 4. HR INTELLIGENCE CAPABILITIES BY HR PRACTICES, PROGRAMS, AND PROCESSES (BOTTOM 12) Lowest rated HR practices in terms of HR intelligence Mean N capabilities 1. Knowledge management 3.48 213 2. Organization design 3.86 212 3. Organizational learning 3.92 213 4. Employee on-boarding 3.95 214 5. Career development 4.07 215 6. Diversity & inclusion 4.53 211 7. Change management 4.58 212 8. Selection 4.76 214 9. Advancement & promotions 4.81 215 10. Organization development 4.83 213 11. Training and development 4.88 215 12. Management & leadership development 4.99 211 Source: Falletta, S., Organizational Intelligence Institute, 2013 TABLE 5. EFFECTIVENESS RATINGS OF CORE HR INTELLIGENCE ACTIVITIES Core HR Intelligence Activity Mean N Performing value-added HR research and analytics that 3.42 214 enables strategy formulation, decision-making, execution, and organizational learning. Gathering external or competitive data and information 3.56 218 on other best-in- class companies/organizations Gathering internal data and information to better 3.73 218 understand your people, talent and workforce in the context of the business Linking multiple data and information sources to 2.71 218 predict, model and forecast individual, group and organizational behavior and performance outcomes Analyzing and transforming data and information into 3.28 217 knowledge, insight and foresight Communicating and reporting insightful and useful 3.42 217 research findings and intelligence result Source: Falletta, S., Organizational Intelligence Institute, 2013; Falletta, S., HR Intelligence, 2008 TABLE 6. THE MEANING OF HR RESEARCH AND ANALYTICS (RANK ORDER) The Meaning of HR Research and Analytics Rank Mean Rank (N) (Rank Order) Order Making better human capital decisions by 1 2.63 (N = 219) using the best available scientific evidence and organizational facts with respect to "evidence-based HR" (i.e., get ting beyond myths, misconceptions, and "plug and play" HR solutions, fads, and trends) Moving beyond "descriptive" HR metrics (i.e., 2 2.66 (N = 219) lagging indicators--something that has already occurred) to "predictive" HR metrics (i.e., leading indicators--some thing that may occur in the future) Segmenting the workforce and using 3 3.47 (N = 219 statistical analyses and predictive modeling procedures to identify key drivers (i.e., factors and variables) and cause and effect relationships that enable and inhibit important business outcomes Using advanced statistical analyses, 4 4.37 (N = 219) predictive modeling procedures, and human capital investment analysis to forecast and extrapolate 'what-if scenarios for decision making Standard tracking, reporting, and 5 4.67 (N = 219) benchmarking of HR metrics Ad-hoc querying, drill-down, and reporting of 6 4.92 (N = 219) HR metrics and indicators through some type of a HRIS and HR scorecard/dashboard reporting tool Operations research and management science 7 5.90 (N = 219) methods for HR optimization (i.e., what's the best that can happen if we do XYZ or what is the optimal solution for a specific human capital problem?) Source: Falletta, S., Organizational Intelligence Institute, 2013 TABLE 7. APPROPRIATENESS OF SELECT WORKFORCE DATA COLLECTION AND HR PRACTICES Workforce Data Collection and HR Analytics Practices Mean N Performance appraisal/evaluation ratings 4.47 215 Pre-coding seemingly harmless demographic data for an 3.81 217 organizational or employee engagement survey project (e.g. identifying, linking, and retaining employee information in advance such as business unit, location, grade or band level on each survey respondent) Pre-coding "top talent" employees (e.g., high 3.75 217 performers, high potentials) employee demographic data for an organizational or employee engagement survey project (e.g., identifying, linking, and retaining employee information in advance such as performance appraisal rating, promotion readiness status, and other high-potential attributes on each survey respondent) The use of 360 degree feedback results designed solely 3.71 217 for the leadership development purposes (e.g., research has shown that leadership quality/ effectiveness as measured by the 360 degree instrument predicts actual employee turnover) Personality assessment results (e.g., Hogan's Big-Five 3.64 217 personality, 16PF) The relative rank of employees derived from forced 3.26 217 ranking process as part of a company's performance appraisal/evaluation system (i.e., a performance management approach that assesses employee performance relative to peers rather than against predetermined goals) The use of emotional intelligence (EQ) test scores 3.16 216 Pre-coding diversity related demographic data for 3.08 217 organizational or employee engagement survey project (e.g., identifying, linking, and retaining employee information in advance such as gender, age, ethnicity, and marital status on each survey respondent) The use of Myers-Briggs typologies 3.06 212 The use of intelligence (IQ) test scores (e.g., 3.05 215 Wechsler's Adult Intelligence Scale or the Stanford-Binet Intelligence Test) The use of general surveys that explore a job 2.79 217 applicant or employee's attitudes, preferences, values and behavior which include seemingly innocuous and irrelevant items/questions pertaining to their personal life (e.g., "what magazines do you subscribe to?" and "what pets do you have?") Public data and information obtained from social media 2.69 213 websites (e.g., Facebook and the like) The use of standardized academic achievement test 2.67 217 scores (e.g., SAT, GMAT, GRE) The use of electronic performance monitoring 2.53 214 technologies (e.g., tracking the number of computer key strokes an employee performs each day or the 2.42 215 amount of daily code a computer programmer generates) Conducting email analysis to identify workgroups/teams who always copy (cc) or blind copy (bcc) their boss as a possible indicator of trust issues Tracking whether a new employee signed up for the 2.24 215 company retirement program as an indicator of early turnover The use of surveillance video to monitor work patterns 2.16 215 and behavior An individual employee's personal data and information 1.81 216 obtained from a company-sponsored "Wellness" website or employee services portal A job applicant's "hometown" or where they were born 1.57 217 and raised Private data and information obtained from social 1.48 215 media websites (e.g., Facebook and the like) whereby the employer asks a candidate or employee to furnish his/her user-id and password An individual employee's prescription drug usage 11.44 215 obtained legally Source: Falletta, S., Organizational Intelligence Institute, 2013 EXHIBIT 1. HR RESEARCH AND ANALYTICS ROLE IN FACILITATING HR STRATEGY AND DECISION MAKING HR analytics HR analytics plays no role is involved in in HR strategy implementing/ formulation and executing HR decision making strategy Overall (N = 218) 6.4% 30.3% Fortune 1-100 (N = 39) 2.6% 21.1% Fortune 101-500 (N = 74) 9.6% 32.9% Fortune 501-1000 (N = 82) 7.3% 29.3% Global (N = 21) 0.0% 38.1% Select $1 billion + (N = 4) 0.0% 50.0% HR analytics HR analytics provides input to plays a the HR strategy central role in and helps formulation and implement it implementation after it has been of HR strategy formulated Overall (N = 218) 49.5% 13.8% Fortune 1-100 (N = 39) 50.0% 26.3% Fortune 101-500 (N = 74) 50.7% 6.8% Fortune 501-1000 (N = 82) 47.6% 15.9% Global (N = 21) 57.1% 4.8% Select $1 billion + (N = 4) 25.0% 25.0% Source: Falletta, S., Organizational Intelligence Institute, 2013