In Search of HR Intelligence:
Evidence-Based HR Analytics Practices
in Higii Performing Companies
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-à-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
T
he 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 hot-test 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, deci-sion 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 &C Rams-tad, 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 practicesThis article summarizes
the results of The HR
Analytics Project
conducted by the
Organizational Intelligence
Institute and Drexel
University.
beyond simple descriptive metrics and score-cards, and more importantly how such activities are being used to facilitate HR strat-egy, decision making, and execution. This article summarizes the results of The HR Analytics Project conducted by the Organiza-tional 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 perform-ing companies (i.e., high performperform-ing firms in terms of annual gross revenue) are conduct-ing a wider range of HR research and analytics practices in the context of human resource strategy and decision making. Sev-eral key areas related to HR research and analytics were explored, including: 1. The types of HR research and analytics
practices being performed in high perform-ing 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
associ-ated with the predictive analytics movement
Methodology
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 alterna-tives (e.g., Likert-type scale, yes/no, rank order), as well as several open-ended questions. Some of the items were adapted from a benchmark-ing study conducted in 2001 by the principal researcher on the topic of HR intelligence prac-tices (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 consor-da 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, workforce planning, and human capital strategy.
Participants
In total, 220 distinct companies completed the web-based survey representing 47 differ-ent 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 com-panies that participated, 195 were Fortune 1000 companies and 21 were global firms headquartered outside of the United States. Of significance, 39 participating compa-nies 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/orga-nizational surveys, assessments, evaluation, applied human capital and organizational behavior research).
Evolving Practices
The first research question focused on the types of HR research and analytics practices that are currently conducted in high per-forming companies. The survey asked par-ticipants to rate the importance of 18 HR research and analytics practices in terms of influencing HR strategy and decision-mak-ing (see Table 1).
Employee and organizational sur-veys received the highest impor-tance 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 collect-ing data and information about employee's thoughts, feelings, and behaviors. While a mainstay for years among HR research-ers and skilled OD practitionresearch-ers, employee and organizational
sur-veys appear to be evolving in importance with respect to HR research and analytics capabilities at high-performing companies.
Surveys are
still the most
important
HR research
and anaiytics
tooi at our
disposai!
Surveys in general are com-monly used for varied pur-poses in the context of human capital strategy and manage-ment (e.g., assessing training needs, evaluating programs and solutions, measuring employee perceptions and attitudes, conducting organi-zational research). The larger companies in the sample (e.g., Fortune 100), however, tend to construct and deliver strategically focused employee and organi-zational surveys that account for key fac-tors and variables that enable, inhibit, and
TABLE 1 . IMPORTANCE RATINGS OF HR RESEARCH AND ANALYTICS PRACTICES
HR Research & Analytics Practice
Empioyee and organizational surveys (e.g., employee opinion surveys, engagement surveys, organizationai cuiture/climate surveys, organizational health surveys, organizationai effective-ness surveys, organizational alignment surveys)
Employee/talent profiiing (i.e., tracking and modeling individual data on critical talent or high-potential employees)
HR metrics and indicators
Partnership or outsourced research Inciuding membership-based research consortia such as the Corporate Leadership Councii,The Conference Board, university of Southern Caiifornia's Center for Effective Organizations, Corneii's Center for Advanced Human Resource Studies, and the institute for Corporate Productivity (Í4CP) to name a few
HR scorecards and dashboards
Workforce forecasting (e.g., workforce suppiy/demand and segmentation analysis to forecast and plan when to staff up or cut back)
Ad hoc HRiS data mining and anaiysis HR benchmarking
Training and HR program evaiuation
Labor market, taient pool and site/location identification research
Talent supply chain (e.g., anaiytics to make decisions in reai time for optimizing immediate talent demands in terms of changing business conditions)
Advanced organizational behavior (OB) research and modeling (e.g., linkage studies, driver anaiysis, correlation and regression anaiysis, factor analysis, path analysis, causai modeiing, and structural equation modeling procedures)
Seiection research invoiving the use of validated personality instruments that measure various empioyee traits, states, characteristics, attributes, attitudes, beiiefs, and/or vaiues
Return-on-investment (ROi) studies
Qualitative research methods inciuding case studies, focus groups, and content or thematic analysis
360 degree or multi-rater feedback (e.g., 360 degree leadership and management assess-ments)
Literature review (e.g., a review and synthesis of existing or secondary data sources such articies and research reports including evidence-based and schoiarly/peer-reviewed journai articies)
Operations research and management science (e.g., optimization methods such as iinear programming; stochastic processes/Markov anaiysis; Bayesian statistics, computational modeiing, and simuiations)
Mean 4.15 3.64 3.63 3.60 3.57 3.55 3.50 3.27 3.27 3.23 3.23 3.13 3.07 3.05 3.01 2.93 2.86 2.33 N 220 215 218 213 211 215 218 215 220 215 172 208 210 212 212 218 214 148
Source: Falletta, S., Organizational Intelligence Institute, 2013
in some cases predict employee engagement and other important individual and orga-nizational 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 ana-lytics 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 cat-egories.
• 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 ex-pensive 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 re-search membership consortiums, such as The Mayflower Group (www.mayñower-group.org) and Information Technology Survey Group (www.itsg.org). In doing so, member companies can make indus-try and cross-indusindus-try 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 imme-diate talent demands in terms of changing business conditions) was rated the second most difficult to perform, which is consis-tent with previous research and observa-tions (Davenport, Harris, &C Shapiro, 2010). • Surprisingly, the literature review
re-ceived the second lowest importance rat-ing (2.82), while global firms (companies headquartered outside of the US) rated the importance of literature reviews
sig-nificantly higher than all other Fortune categories, thereby suggesting a greater interest in and orientation towards evi-dence-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 intelli-gence (i.e., expert systems and machine learning) to HR management decisions are likely to increase as advancements in skills, capabilities, and technology con-tinue (Sesil, 2014).
Organization and
Structure of HR
Research ôc 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 staff-ing 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 analy-ses found that the staffing level of this func-tion was higher in companies with higher gross revenues and a larger workforce. It important to note that these results merely refiect 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) do-ing some form of analytics work in context of human capital management. Further, these results do not suggest that the remain-ing participatremain-ing 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).
TABLE 2. MOST COMMON FUNCTION OR GROUP NAMES
HR Analytics HR Intelligence Workforce Analytics Talent Analytics HR Insights HR Reporting Employee Insights Global HR Insights HR Technology HRIS
Human Capital intelligence Talent Management & Analytics
Empicyee Surveys & Insights
N = 13 N = 7 N = 7 N = 6 N - 5 N = 5 N = 4 N = 3 N = 3 N = 3 N = 3 N = 3 N - 2
HR Quality & Analytics HR Research HR Strategy Organizational Insights People Analytics People Metrics Peopie Research Surveys & Assessments Workforce Intelligence Workforce Measurement Workforce Planning Workforce Research N = 2 N = 2 N - 2 N = 2 N = 2 N = 2 N = 2 N = 2 N = 2 N=2 N=2 N = 2
Nearly a third (31.4% N = 53) of all dedicat-ed HR research and analytics groups report directly to the Chief HR Officer (i.e., head of HR) suggesting that these functions are stra-tegically 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.
Source: Falletta, S., Organizational Intelligence Institute, 2013
While the function or group "names" vary, the nature and content of the practices and activities appear to be HR research and ana-lytics 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 tal-ent analytics (n = 6) respectively.
EXHIBIT 1. HR RESEARCH AND ANALYTICS ROLE IN FACILITATING HR STRATEGY
60°'
AND DECISION MAKING
50%
40% I l l s
30% 30% 20% 10% 0% • Overall (N=218) • Fortune 1-100 (N=39) • Fortune 101-500 (N=74) B Fortune 501-1000 (N=82) Global (N=21) Select $1 billion + (N=4)•-••
HR anaiytics plays no role in HR strategy formulation and decision making 6.4% 2.6% 9.6% 7.3% 0.0% 0.0%nil
• h ll||
JH. iBL
HR anaiytics is involved in impiementing/ executing HR strategy 30.3% 21.1% 32.9% 29.3% 38.1% 50.0% HR analytics provides input to the HR strategy and helps impiement it after it has beenformulated 49.5% 50.0% 50.7% 47.6% 57.1% 25.0%
•
1 i
lili
HR analytics piays a central role in formulation and implementation of HR strategy 13.8% 26.3% 6.8% 15.9% 4.8% 25.0%Source: Falletta, S., Organizational Intelligence Institute, 2013
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 frequen-cies of choice are reported in Exhibit 1. HR analytics is characterized as having input into HR strategy formulation but not play-ing 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 addi-tional informa-tion about the HR research and analytics role in infiuencing HR strategy formula-tion and decision-making specifical-ly, an overarching theme emerged in which broader HR research and ana-lytics practices were largely described as an exhaustive data gathering exercise (i.e., a data dump), whereby pre-con-ceived notions or and decisions drove
The role of HR
research and
analytics is
largely an enabier
and/or data feed
to the strategy
formuiation and
decision-making
process.
after-the-fact, HR strategies 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 cur-rent 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 ex-ample, a powerful and/or narcissistic leader would probably prefer to make decisions based upon his or her opinions and
intu-ition rather than relying on the good facts and figures (i.e., evidence). Similarly, Sesil explains in his recent book. Applying
Ad-vanced Analytics to HR Management Deci-sions (2014) that those in positions of
pow-er might have fragile egos and be primarily concerned with advancing their own agen-da rather than dealing with actual facts. Indeed, further work is needed in terms of
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 de-cisions is a recipe for organizational analysis paralysis. Likewise, making critical HR deci-sions solely based on prior experience, intu-ition, gut feelings, and/or management fad du
jour could have disastrous effects. In short, we
EXHIBIT 2. THE HR INTELLIGENCE VALUE CHAIN
intuition intelligence
Human capital decisionsare \arge\y based on prior experiences, opinions, gut feelings, current trends and/or fads.
0 1 2 3 4 5 6 7 8 9 10
data information analytics
Human capital decisions are based on insightfui HR analytics that are largely predictive and supported by a synthesis of the best available scientific evidence
(i.e. evidence-based HR).
Source; Falletta, S,, Organizational Intelligence Institute, 2013
elevating the status and legitimacy of HR analytics and its infiuence 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). Daven-port, Harris, & Morison (2010) in their book
Analytics at Work: Smart Decisions, Better
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, refiecdng degrees of HR 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
Intelli-TABLE 3. HR INTELLIGENCE CAPABILITIES BY HR PRACTICES, PROGRAMS, AND PROCESSES (TOP 12)
Highest rated HR practices in terms of HR inteliigence capabilities 1, Employee & organizational surveys
2, Employee engagement & retention 3, Compensation
4, HR strategy 5, Workforce planning
6. Competency & talent assessments 7. Benefits
8. Performance appraisal & management 9. Reduction in force & downsizing 10, HR legal & compliance 11, Succession planning 12, Recruitment Mean 6,59 6,05 5,90 5,62 5,54 5,35 5,34 5,29 5,14 5,11 5,09 5,03 N 214 212 215 215 215 214 215 214 206 212 215 214
TABLE 4. HR INTELLIGENCE CAPABILITIES BY HR PRACTICES, PROGRAMS, AND PROCESSES (BOTTOM 12)
Lowest rated HR practices in terms of HR intelligence capabilities 1. Knowledge management
2. Organization design 3. Organizational learning 4. Employee on-boarding 5. Career development 6. Diversity & inciusion 7. Change management 8. Selection
9. Advancement & promotions 10. Organization deveiopment 11.Training and development
12. Management & leadership development
Mean 3.48 3.86 3.92 3.95 4.07 4.53 4.58 4.76 4.81 4.83 4.88 4.99 N 213 212 213 214 215 211 212 214 215 213 215 211
gence Value Ghain was adapted from HR In-telligence Hierarchy — which included three levels namely — Data, Information, and telligence (Falletta, 2008). While the HR In-telligence Value Ghain is by no means a vali-dated scale in terms of measurement validity and reliability, it does provide a practical framework with which to estimate and gauge HR intelligence capabihties as a first step in conducting applied research on the topic. The ratings of these 24 HR activities are re-ported in Table 3 and Table 4 respectively. Employee and organizational surveys re-ceived the highest "HR intelligence" ratings (mean score of 6.59 on the 11-point scale)
Source: Falietta, S., Organizational Intelligence Institute, 2013
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 intelli-gence 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 low-est "HR intelligence" ratings (mean score of 3.48 on the 11 point scale) in terms of HR intelligence capabilities and level of so-phistication. Organization design (3.86),
TABLE 5. EFFECTIVENESS RATINGS OF CORE HR INTELLIGENCE ACTIVITIES
Core HR Intelligence Activity
Performing value-added HR research and analytics that enables strategy formulation, decision-making, execution, and organizational learning. Gathering external or competitive data and information on other best-in-class companies/organizations
Gathering internai data and information to better understand your people, taient and workforce in the context of the business
Linking multiple data and information sources to predict, modei and forecast individual, group and organizational behavior and performance outcomes
Anaiyzing and transforming data and information into knowledge, insight and foresight
Communicating and reporting insightfui and usefui research findings and inteiligence result Mean 3.42 3.56 3.73 2.71 3.28 3.42 N 214 218 218 218 217 217 Source: Falletta, S., Organizational Intelligence Institute, 2013; Falletta, S., HR Intelligence, 2008
organizational learning (3.92), employee on-boarding, (3.94), and career develop-ment (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 knowl-edge management and organizational learn-ing were in the bottom five. Definitional problems persist and many companies still struggle to effectively implement these evolv-ing 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 execu-tives 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 inteUigence is much more of an analytical aspiration at this point for many companies. The route to building HR intelligence capability that can improve hu-man capital decision making will depend on the level of HR analytical maturity as well as the extent to which a given company em-braces evidence-based HR.
The third and final group of survey items in the Core HR Analytics Capabilities &c Pro-cesses section of the survey asked partici-pants to rate their effectiveness on a 5-point scale (1 = very ineffective, to 5 = very effec-tive) 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 orga-nizational behavior performance outcomes
was relatively low. For many participating companies, this particular activity is still a very challenging and emerging core capabil-ity. As described earlier, respondents rated "advanced OB research and modeling" as the most timing-consuming as well as most dif-ficult to HR research and analytics practice to perform.
I
OBSERVATIONS & INSIGHTS -WHO SHOULD OR CAN DO ANALYTICS?
Driving a proactive HR research and anaiytics agenda is a critically important capability in terms of enabling strategic human capital decisions.Therefore, HR researchers and analysts should bring their own "HR intel-ligence" and expertise to the table. Many of the respondents in this study hold advanced degrees in the social, behavioral, and organi-zational 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 research-er 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
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 in-dicated that they have a function or group dedicated to HR research and analytics. This might suggest that HR research and analyt-ics activities and its prioritization are large-ly 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 cus-tomer driven (60.7%, n = 215). In short, this demonstrates a relatively balanced approach in terms of determining the actual HR re-search and analytics agenda.
EXHIBIT 3. THE HR INTELLIGENCE CYCLE
1 : determine stakeholder requirements
« tactical 7: enable strategy + decision making imitator+ improver+ innovator * iconoclast 6: connmunicate intelligence results
descriptive * prédictive * prescriptive
2: define HR research + analytics agenda
3: identify data sources
puMc-» private
4: gather data 5: transform data
meta-aiulytics
Source; Falietta, S., Organizationai intaiiigence Institute, 2013
The Meaning of HR
Intelligence
The forth research question explored the meaning of "HR intelligence" by those who perform HR research and analytics. Respon-dents were asked to rank in order seven items in terms of how accurately they describe what HR research and analytics means. The
rank-ings of these items are reported in Table 6. The rank order is presented in ordinal fash-ion (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.
TABLE 6. THE MEANING OF HR RESEARCH AND ANALYTICS (RANK ORDER)
The Meaning of HR Research and Analytics (Rank Order)
Making better human capital decisions by 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., 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 statistical analyses and predictive modeling procedures to identify key drivers (i.e., factors and variables) and cause and ef-fect relationships that enable and inhibit important business outcomes Using advanced statistical analyses, predictive modeling procedures, and human capital investment analysis to forecast and extrapolate 'what-if scenarios for decision making
Standard tracking, reporting, and benchmarking of HR metrics
Ad-hoc querying, drill-down, and reporting of HR metrics and indicators through some type of a HRIS and HR scorecard/dashboard reporting tool
Operations research and management science methods for HR optimization (i.e., what's the best that can happen if we do XVZ or what is the optimal solution for a specific human capital problem?)
Rank Order 1 2 3 4 5 6 7 Mean Rank(N) 2.63 (N = 219) 2.66 (N = 219) 3.47 (N = 219 4.37 (N = 219) 4.67 ( N - 2 1 9 ) 4.92 (N = 219) 5.90 (N = 219)
What Is HR Intelligence?
In the spirit competitive or business intel-ligence, HR intelligence is defined as "a proactive and systematic process for gath-ering, 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 sys-tematic, HR intelligence must be positioned as an ongoing cycle involving seven steps (see Exhibit 3).
Robust HR intelligence capabihties extend beyond HR metrics. HR intelligence en-ables 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 sup-ported 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 as-sociated with HR research and predictive an-alytics. 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 in-cluded in the survey — some of which have had a long history of controversy — from
TABLE 7. APPROPRIATENESS OF SELECT WORKFORCE DATA COLLECTION AND HR PRACTICES
Workforce Data Coiiection and HR Anaiytics Practices Performance appraisai/evaluation ratings
Pre-coding seemingiy harmiess demographic data for an organizationai or empioyee engagement survey project (e.g. identifying, linking, and retain-ing employee information in advance such as business unit, iocation, grade or band level on each survey respondent)
Pre-coding "top taient" employees (e.g., high performers, high potentiais) empioyee demographic data for an organizational or employee engagement survey project (e.g., identifying, iinking, and retaining employee information in advance such as performance appraisai rating, promotion readiness status, and other high-potentiai attributes on each survey respondent)
The use of 360 degree feedback results designed soieiy for the leadership development purposes (e.g., research has shown that ieadership quaiity/ effectiveness as measured by the 360 degree instrument predicts actuai employee turnover)
Personality assessment results (e.g., Hogan's Big-Five personaiity, 16PF)
The reiative rank of empioyees derived from forced ranking process as part of a company's performance appraisal/evaluation system (i.e., a perfor-mance management approach that assesses employee perforperfor-mance relative to peers rather than against predetermined goals)
The use of emotionai intelligence (EQ) test scores
Pre-coding diversity related demographic data for organizationai or empioyee engagement survey project (e.g., identifying, linking, and retaining empioyee information in advance such as gender, age, ethnicity, and marital status on each survey respondent)
The use of Myers-Briggs typologies
The use of inteiiigence (iQ) test scores (e.g., Wechsier's Aduit Intelligence Scale or the Stanford-Binet inteiiigenceTest)
The use of gênerai surveys that explore a job applicant or employee's attitudes, preferences, values and behavior which include seemingiy 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 websites (e.g., Facebook and the iike)
The use of standardized academic achievement test scores (e.g., SAT, GMAT, GRE)
The use of electronic performance monitoring technologies (e.g., tracking the number of computer key strokes an employee performs each day or the amount of daiiy code a computer programmer generates)
Conducting email analysis to identify workgroups/teams who aiways copy (cc) or biind copy (bcc) their boss as a possibie indicator of trust issues Tracking whether a new empioyee signed up for the company retirement program as an indicator of eariy turnover
The use of surveiilance video to monitor work patterns and behavior
An individuai employee's personal data and information obtained from a company-sponsored "Weiiness" website or empioyee services portal A job applicant's "hometown" or where they were born and raised
Private data and information obtained from social media websites (e.g., Facebook and the like) whereby the empioyer asks a candidate or employee to furnish his/her user-id and password
An individual employee's prescription drug usage obtained legally
Mean 4.47 3.81 3.75 3.71 3.64 3.26 3.16 3.08 3.06 3.05 2.79 2.69 2.67 2.53 2.42 2.24 2.16 1.81 1.57 1.48 1.44 N 215 217 217 217 217 217 216 217 212 215 217 213 217 214 215 215 215 216 217 215 215
Source: Falletta, S., Organizational Intelligence Institute, 2013
intelligence (IQ) and personality testing to forced-ranking in performance appraisals to employee performance monitoring and sur-veillance technologies. These practices have always incited spirited debates among aca-demicians 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 &c 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 seem-ingly innocuous aspects of their personal life (e.g., "what magazines do you subscribe to?" and "what pets do you have?") (Han-sell, 2007), as well as identifying a job ap-plicant's "hometown" as a relatively accu-rate 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 ethi-cal implications associated with the HR re-search and predictive analytics movement. Respondents were asked to rate 21 work-force data collection and HR analytics prac-tices 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 high-er than the ovhigh-erall mean (2.80) and fell into the appropriate scale interval. These are listed below from highest-rated down-ward.
• Performance appraisal/evaluation rat-ings
• Pre-coding survey demographic data in general
• Pre-coding survey demographic data from "top talent" employees
• 360 degree feedback results for leader-ship 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 (or-dered from lowest upward):
• An individual employee's prescription drug usage obtained legally
• Private data and information obtained from social media websites (e.g.. Face-book and the like) whereby the em-ployer 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 turn-over
It is noteworthy that 76% of the listed prac-tices were considered neutral or inappropri-ate by the sample as a whole. Needless to say, much more research is needed on ethi-cal 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, ad-vancement/promotion, demotion, reduction-in-force efforts).
i
OBSERVATIONS & INSIGHTS -FIRST DO NO HARM
One disturbing trend I've experienced first-iiand involves HR professionals iiaving dif-ficuity distinguishing between the iaw and ethics. For example, during a recent confer-ence in which i was invited to speak on HR intelligence, i shared a few questionable HR anaiytics practices, including the one about an applicant's hometown being used as a relatively accurate predictor of attrition. Af-terwards, a weii-known and highiy respected HR metrics consuitant stood-up and said, "I have no problem with it as long as it's le-gal and doesn't involve a protected group." While sharing the same exampies during a recent presentation, I've received mixed reactions, surprisingiy, from a few very ex-perienced and competent industrial and organizationai psychologists who seem to be grappling with their company's workforce data collection and HR anaiytics practices 1 — 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 gen- ™ erai principle - First, Do No Harm). Cleariy, m further discussion and debate are needed about ethics in general and the application of HR anaiytics in particular (Bassi, 2011). Ali of this begs the question: should HR i professionais and iine managers make human capital decisions based on an ap-piicant's hometown? What about an em-Ä ployee's pet preferences or favorite ice cream flavor? i suppose dog iovers from small towns are more loyal and commit-ted than cat peopie born and raised in ^ the urban jungie, and just maybe - butter • pecan employees have a higher EQ and ' make better leaders than piain oie vaniiia foiks. Irrespective to any predictive utiiity, how appropriate is it to use such data and information for human capital decisions? When I got off my soapbox, a quick-witted coiieague and oid friend said to me that the "genie is aiready out of the bottie and it will probably take Federal legislation to sort it out." Meanwhile, if HR profes-sionais are willing to proactlveiy address such ethicai quandaries and challenge questionable HR anaiytics practices re-gardless of any real or perceived predic-tive vaiue - there is indeed a bright future for HR analytics.
Final Thoughts
The results of the study suggest that the landscape for using data and information has shifted dramatically, and that leading companies are building strategic capabili-ties and competitive advantage through ad-vanced HR analytics practices. As expect-ed, 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 specifi-cally addressed in this study, has to do with making sense of the disparate data sources from all of the HR research and analyt-ics 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 SaaS-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 collec-tive 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 hu-man capital. i ^ S
References
Bassi, L. (2011). Raging debate in HR ana-lytics. People & Strategy, 34(2), 14-18. Boudreau J. &c 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 Deci-sions, Better Results, Boston, MA: Harvard Business School Press.
Davenport, T , Harris, J., & Shapiro, J. (2010). Competing on talent analytics. Har-vard Business Review, 52-58.
Falletta, S. (2008). HR intelligence: Advanc-ing people research and analytics. Interna-tional HR Information Management Jour-nal. 7 (3), 21-31.
Falletta, S. (2008b). Organizational intel-ligence surveys. Training & Development, 52-58.
Fitz-enz, J. (2010). The New HR Analytics: Predicting Economic Value of Your Compa-ny'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.
Levenson, A. (2011). Using targeted analyt-ics to improve talent decisions. People & Strategy, 34(2), 34-43.
Levenson, A., Lawler, E., & Boudreau, J. (2005). Survey on HR Analytics and HR Transformation: Feedback Report. Genter for Effective Organizations, University of Southern California.
Pfeffer, J. &: Sutton, R. I. (2006). Hard Facts, Dangerous Half-Truths, & Total Nonsense: Profiting from Evidence-Based Manage-ment. Boston, MA: Harvard Business School Press.
Saari, L. & Scherbaum, G. (2011). Identified employee surveys: Potential promise, perils, and professional practice guidelines. Indus-trial and Organizational Psychology, 4(4), 435-448.
Sesil. J. G. (2014). Applying advanced ana-lytics to HR management decisions: Meth-ods for selection, developing incentives, and improving collaboration. Saddle River, NJ: Pearson.
Dr. Salvatore Falletta is EVP and Man-aging 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 GEO of Leadersphere, served as a Vice President and Ghief HR Officer at a Fortune 1000 firm based in the Silicon Valley, and has held senior management positions in human resources at sev-eral 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 [email protected].
