In Search of HR Intelligence: Evidence-Based HR Analytics Practices in Higii Performing Companies By Dr. Salvatore Falletta
28 I
PEOPLE & STRATEGY
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 hottest trends in the context of HR strategy and decision making. Several notable thoughtleaders 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 &C 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
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"
This article summarizes the results of The HR Analytics Project conducted by the Organizational Intelligence Institute and Drexel University. 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
5. The emerging ethical implications associated 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 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 consorda 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 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).
Evolving Practices 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). >VOLUME 36/ISSUE 4 — 2014
29
Employee and organizational surSurveys in general are comSurveys are veys received the highest impormonly used for varied purtance ratings in the study, (overall still the most poses in the context of human mean rating of 4.15), which isn't capital strategy and managetoo surprising given that surveys important ment (e.g., assessing training are one of the most prevalent and HR research needs, evaluating programs widely used methods for collectand solutions, measuring ing data and information about and anaiytics employee perceptions and employee's thoughts, feelings, attitudes, conducting organitooi at our and behaviors. While a mainstay zational research). The larger for years among HR research- disposai! companies in the sample ers and skilled OD practitioners, (e.g., Fortune 100), however, employee and organizational surtend to construct and deliver veys appear to be evolving in importance strategically focused employee and organiwith respect to HR research and analytics zational surveys that account for key faccapabilities at high-performing companies. tors and variables that enable, inhibit, and TABLE 1. IMPORTANCE RATINGS OF HR RESEARCH AND ANALYTICS PRACTICES Mean
N
4.15
220
3.64
215
HR metrics and indicators
3.63
218
Partnership or outsourced research Inciuding membership-based research consortia such as
3.60
213
HR scorecards and dashboards
3.57
211
Workforce forecasting (e.g., workforce suppiy/demand and segmentation analysis to forecast
3.55
215
Ad hoc HRiS data mining and anaiysis
3.50
218
HR Research & Analytics Practice Empioyee and organizational surveys (e.g., employee opinion surveys, engagement surveys, organizationai cuiture/climate surveys, organizational health surveys, organizationai effectiveness surveys, organizational alignment surveys) Employee/talent profiiing (i.e., tracking and modeling individual data on critical talent or highpotential employees)
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
and plan when to staff up or cut back)
HR benchmarking
3.27
215
Training and HR program evaiuation
3.27
220
Labor market, taient pool and site/location identification research
3.23
215
Talent supply chain (e.g., anaiytics to make decisions in reai time for optimizing immediate
3.23
172
3.13
208
3.07
210
Return-on-investment (ROi) studies
3.05
212
Qualitative research methods inciuding case studies, focus groups, and content or thematic
3.01
212
2.93
218
2.86
214
2.33
148
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,
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.mayñowergroup.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.
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
analysis 360 degree or multi-rater feedback (e.g., 360 degree leadership and management assessments) 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
• 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, &C Shapiro, 2010).
articies) Operations research and management science (e.g., optimization methods such as iinear programming; stochastic processes/Markov anaiysis; Bayesian statistics, computational modeiing, and simuiations) Source: Falletta, S., Organizational Intelligence Institute, 2013
30
PEOPLE & STRATEGY
• 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 sig-
nificantly 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.
TABLE 2. MOST COMMON FUNCTION OR GROUP NAMES
• 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 ôc Analytics The second research question explored how HR research and analytics activities and groups are organized and structured within high-performing companies. Over threequarters 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 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) 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).
HR Analytics
N = 13
HR Quality & Analytics
N=2
HR Intelligence
N=7
HR Research
N=2
Workforce Analytics
N=7
HR Strategy
N-2
Talent Analytics
N=6
Organizational Insights
N=2
HR Insights
N-5
People Analytics
N=2
HR Reporting
N= 5
People Metrics
N=2
Employee Insights
N=4
Peopie Research
N=2
Global HR Insights
N=3
Surveys & Assessments
N=2
HR Technology
N=3
Workforce Intelligence
N=2
HRIS
N=3
Workforce Measurement
N=2
Human Capital intelligence
N=3
Workforce Planning
N=2
Talent Management & Analytics
N=3
Workforce Research
N=2
Empicyee Surveys & Insights
N-2 Source: Falletta, S., Organizational Intelligence Institute, 2013
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.
EXHIBIT 1. HR RESEARCH AND ANALYTICS ROLE IN FACILITATING HR STRATEGY AND DECISION MAKING 60°' 50%
40%
Ills
nil
30%
• h ll||
30% 20% 10% 0%
•-•• HR anaiytics
JH. iBL
•
1i
lili
HR anaiytics
HR analytics
plays no role
is involved in
provides input to
piays a
in HR strategy
impiementing/
the HR strategy
central role in
formulation and
executing HR
and helps
formulation and
decision making
strategy
impiement it
implementation
after it has been
of HR strategy
HR analytics
formulated •
Overall (N=218)
6.4%
30.3%
49.5%
13.8%
•
Fortune 1-100 (N=39)
2.6%
21.1%
50.0%
26.3%
•
Fortune 101-500 (N=74)
9.6%
32.9%
50.7%
7.3%
29.3%
47.6%
Global (N=21)
0.0%
38.1%
57.1%
4.8%
Select $1 billion + (N=4)
0.0%
50.0%
25.0%
25.0%
B Fortune 501-1000 (N=82)
6.8% 15.9%
Source: Falletta, S., Organizational Intelligence Institute, 2013
VOLUME 36/ISSUE 4 — 2 0 1 4
31
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.
ition 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
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 The role of HR HR research and analytics role in research and infiuencing HR strategy formulaanalytics is tion and decisionlargely an enabier making specificalan overarching and/or data feed ly, theme emerged in which broader HR to the strategy research and anaformuiation and lytics practices were described as decision-making largely an exhaustive data process. gathering exercise (i.e., a data dump), whereby pre-conceived 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 intu32
PEOPLE & STRATEGY
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
EXHIBIT 2. THE HR INTELLIGENCE VALUE CHAIN
intuition Human capital decisions are \arge\y based on prior experiences, opinions, gut feelings, current trends and/or fads.
intelligence 01
2 3 4 5 6 7 8 9 data
information
analytics
10
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). Davenport, 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
Mean
N
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 capabilities
Mean
N
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 & inciusion
4.53
211
7. Change management
4.58
212
8. Selection
4.76
214
9. Advancement & promotions
4.81
215
10. Organization deveiopment
4.83
213
11.Training and development
4.88
215
12. Management & leadership development
4.99
211
Source: Falietta, S., Organizational Intelligence Institute, 2013
gence Value Ghain was adapted from HR Intelligence Hierarchy — which included three levels namely — Data, Information, and Intelligence (Falletta, 2008). While the HR Intelligence Value Ghain 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 capabihties 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),
TABLE 5. EFFECTIVENESS RATINGS OF CORE HR INTELLIGENCE ACTIVITIES Core HR Intelligence Activity
Mean
N
Performing value-added HR research and analytics that enables strategy formulation, decision-making, execution, and organizational learning.
3.42
214
Gathering external or competitive data and information on other best-inclass companies/organizations
3.56
218
Gathering internai data and information to better understand your people, taient and workforce in the context of the business
3.73
218
Linking multiple data and information sources to predict, modei and forecast individual, group and organizational behavior and performance outcomes
2.71
218
Anaiyzing and transforming data and information into knowledge, insight and foresight
3.28
217
3.42
217
Communicating and reporting insightfui and usefui research findings and inteiligence result
Source: Falletta, S., Organizational Intelligence Institute, 2013; Falletta, S., HR Intelligence, 2008
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 inteUigence 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 &c 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. VOLUME 36/iSSUE 4 — 2014
33
EXHIBIT 3. THE HR INTELLIGENCE CYCLE
OBSERVATIONS & INSIGHTS -WHO SHOULD OR CAN DO ANALYTICS?
1 : determine stakeholder requirements « tactical
Driving a proactive HR research and anaiytics agenda is a critically important capability in
2: define HR research + analytics agenda
terms of enabling strategic human capital decisions.Therefore, HR researchers and
7: enable strategy + decision making
analysts should bring their own "HR intel-
imitator+ improver+ innovator * iconoclast
ligence" and expertise to the table. Many of
3: identify data sources
the respondents in this study hold advanced
puMc-» private
degrees in the social, behavioral, and organi-
6: connmunicate intelligence results
zational sciences and are arguably in the best position to design and interpret robust HR
4: gather data
descriptive * prédictive * prescriptive
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
5: transform data
I
meta-aiulytics
Source; Falietta, S., Organizationai intaiiigence Institute, 2013
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 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. 34
PEOPLE & STRATEGY
The Meaning of HR Intelligence
ings 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 The forth research question explored the simplicity and includes the actual mean rank. meaning of "HR intelligence" by those who The overall mean rank was 4.09. While there perform HR research and analytics. Respon- are certainly a diversity of views, thefirsttwo dents were asked to rank in order seven items (Rank 1 and 2) emerged as significantly more in terms of how accurately they describe what descriptive than the others as to the central HR research and analytics means. The rank- 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)
Rank Order
Mean Rank(N)
Making better human capital decisions by using the best available scientific
1
2.63 (N = 219)
2
2.66 (N = 219)
3
3.47 (N = 219
4
4.37 (N = 219)
Standard tracking, reporting, and benchmarking of HR metrics
5
4.67 ( N - 2 1 9 )
Ad-hoc querying, drill-down, and reporting of HR metrics and indicators through
6
4.92 (N = 219)
7
5.90 (N = 219)
evidence and organizational facts with respect to "evidence-based HR" (i.e., getting 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 - something 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 effect 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
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?) Source: Falletta, S., Organizational Intelligence institute, 2013
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 capabihties 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
TABLE 7. APPROPRIATENESS OF SELECT WORKFORCE DATA COLLECTION AND HR PRACTICES Workforce Data Coiiection and HR Anaiytics Practices
Mean
N
Performance appraisai/evaluation ratings
4.47
215
Pre-coding seemingiy harmiess demographic data for an organizationai or empioyee engagement survey project (e.g. identifying, linking, and retain-
3.81
217
3.75
217
3.71
217
Personality assessment results (e.g., Hogan's Big-Five personaiity, 16PF)
3.64
217
The reiative rank of empioyees derived from forced ranking process as part of a company's performance appraisal/evaluation system (i.e., a perfor-
3.26
217
The use of emotionai intelligence (EQ) test scores
3.16
216
Pre-coding diversity related demographic data for organizationai or empioyee engagement survey project (e.g., identifying, linking, and retaining
3.08
217
The use of Myers-Briggs typologies
3.06
212
The use of inteiiigence (iQ) test scores (e.g., Wechsier's Aduit Intelligence Scale or the Stanford-Binet inteiiigenceTest)
3.05
215
The use of gênerai surveys that explore a job applicant or employee's attitudes, preferences, values and behavior which include seemingiy innocuous
2.79
217
Public data and information obtained from social media websites (e.g., Facebook and the iike)
2.69
213
The use of standardized academic achievement test scores (e.g., SAT, GMAT, GRE)
2.67
217
The use of electronic performance monitoring technologies (e.g., tracking the number of computer key strokes an employee performs each day or the
2.53
214
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)
mance management approach that assesses employee performance relative to peers rather than against predetermined goals)
empioyee information in advance such as gender, age, ethnicity, and marital status on each survey respondent)
and irrelevant items/questions pertaining to their personal life (e.g., "what magazines do you subscribe to?" and "what pets do you have?")
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
2.42
215
Tracking whether a new empioyee signed up for the company retirement program as an indicator of eariy turnover
2.24
215
The use of surveiilance video to monitor work patterns and behavior
2.16
215
An individuai employee's personal data and information obtained from a company-sponsored "Weiiness" website or empioyee services portal
1.81
216
A job applicant's "hometown" or where they were born and raised
1.57
217
Private data and information obtained from social media websites (e.g., Facebook and the like) whereby the empioyer asks a candidate or employee
1.48
215
1.44
215
to furnish his/her user-id and password An individual employee's prescription drug usage obtained legally
Source: Falletta, S., Organizational Intelligence Institute, 2013
VOLUME 3 6 / I S S U E 4 — 2 0 1 4
35
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 &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 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. scale interval. These are listed below (ordered from lowest upward): The fifth and final research question in this study attempted to gain insight into the ethi• An individual employee's prescription cal implications associated with the HR redrug usage obtained legally search and predictive analytics movement. • Private data and information obtained Respondents were asked to rate 21 workfrom social media websites (e.g.. Faceforce data collection and HR analytics pracbook and the like) whereby the emtices on a five-point scale of appropriateness ployer asks a candidate or employee to ranging from absolutely inappropriate to furnish his/her user-ID and password absolutely appropriate. The appropriateness • A job applicant's "hometown" or ratings of these 21 practices are reported in where they were born and raised Table 7. • Surveillance video to monitor work patterns and behavior There were five practices that had mean • Tracking whether a new employee ratings which were both significantly highsigned up for the company retirement er than the overall mean (2.80) and fell program as an indicator of early turninto the appropriate scale interval. These over are listed below from highest-rated downward. It is noteworthy that 76% of the listed practices were considered neutral or inappropri• Performance appraisal/evaluation rat- ate by the sample as a whole. Needless to ings say, much more research is needed on ethi• Pre-coding survey demographic data cal issues associated with HR research and in general predictive analytics. This study attempted to • Pre-coding survey demographic data explore ethical judgments on select practices from "top talent" employees pertaining to human capital decisions in the • 360 degree feedback results for leader- broadest sense. However, it is quite likely ship development purposes that individual ethical judgments will vary • Personality assessment results and depend on the type of human capital decision being made (e.g., hiring, job/work Five of the practices had means that were assignments, performance management, adboth significantly lower than the overall vancement/promotion, demotion, reductionmean and which fell into the inappropriate in-force efforts). PEOPLE & STRATEGY
OBSERVATIONS & INSIGHTS FIRST DO NO HARM One disturbing trend I've experienced firstiiand involves HR professionals iiaving difficuity distinguishing between the iaw and ethics. For example, during a recent conference 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. Afterwards, a weii-known and highiy respected HR metrics consuitant 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 exampies during a recent presentation, I've received mixed reactions, surprisingiy, from a few very experienced 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).
i
Ali of this begs the question: should HR i professionais and iine managers make human capital decisions based on an appiicant'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 committed 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 professionais are willing to proactlveiy address such ethicai quandaries and challenge questionable HR anaiytics practices regardless of any real or perceived predictive vaiue - there is indeed a bright future for HR analytics.
I
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 analytics 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.
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 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 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 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 decisionmakers with pertinent knowledge and insight to make critical decisions pertaining to human capital. i ^ S
References Bassi, L. (2011). Raging debate in HR analytics. 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 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-
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, G. (2011). Identified employee surveys: Potential promise, perils, and professional practice guidelines. Industrial and Organizational Psychology, 4(4), 435-448. Sesil. J. G. (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 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 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
[email protected].
VOLUME 36/ISSUE 4 — 2014
37
Copyright of People & Strategy is the property of HR People & Strategy and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use.
