HR Business Partners should become HR Analytics 'Translators' - interview with Luk Smeyers
Question: Luk, you have recently attended HR Tech World Congress in Paris. What are some of the things that stood out?
Question: Could you tell us more about the role of such a coordinator?
Question: So what do you think makes a “good HR analytics translator”?
Question: What is the biggest challenge these translators face?
Question: You mentioned HR’s fear of analytics. Could you elaborate?
Question: So this is where the translator comes in?
How HR analytics avoids being a management fad - Thomas Rasmussen and Dave Ulrich
Far better an approximate answer to the right question, which is often vague, than an exact answer to the wrong question, which can always be made precise.
John W. Tukey, mathematician, 1962
Half a century later, Tukey's point is as relevant as ever. It helps explain why HR (human resources) analytics risks becoming a management fad, instead of providing powerful insights for general managers and HR leaders making key decisions about talent, incentive structures, organization design, allocation of training budget, etc. to support value creation and the business strategy. Management fads exist. Some fads become institutionalized within companies (e.g., MBO, matrix management, core competence); other fads fade (e.g., time management, zero-defects, T-groups). They are shiny new ideas that get attention but do not endure (e.g., learning organization, Japanese management, one minute manager, re-engineering). That HR analytics is one of the latest emerging fads is a paradox in itself. The promise of analytics is great: replace fads with evidence-based initiatives, data-based decision making, bridge management academia and practice, prioritize impact of HR investments, bring rigor to HR and supplement HR intuition with objectivity. Large parts of HR analytics, however, are not new and people have talked about HR metrics, utility analysis, HR scorecards, HR ROI (return on investment), personnel economics, and evidence-based management for years without a large noticeable step-change in the business impact of HR. So far the published evidence supporting the alleged value of HR analytics is actually quite slim – it is currently based more on belief than evidence, and most often published by consultants with a commercial interest in the HR analytics market, while organizations rarely share the same success stories of business impact, but typically share cases with turnover prediction (even if turnover is not an issue) or projects with a similar narrow HR focus. Rigorous analyses of loads of data on the wrong questions often have little practical value. Yet HR analytics tops most conferences this year (greatly helped by the many HR technology and consulting firms who see a major future business opportunity in selling data and statistics capabilities to a function that is short on both), and is also the dream of many management academics of how what they do finally becomes the center of the HR profession. We predict HR analytics in its current form will continue to fail to add real value to companies. We agree with those who argue that HR analytics is being taken over by other functions that are more mature in their analytics journey (in particular finance, IT, and marketing) and that this will happen sooner rather than later, but also that this is actually a good thing: HR analytics needs to evolve and transcend HR (as other functions’ analytics will need to transcend their own functional boundaries), and will only become relevant when it takes an “outside in” approach, and is taken out of HR and integrated in existing end-to-end business analytics. In this paper we highlight what is contributing to HR analytics in its existing form becoming a management fad, what can help HR analytics deliver value by being part of end-to-end analytics, and illustrate this with two cases.
What contributes to making HR analytics a management fad?
HR analytics in its current form has the risk of being a fad that fades. Here is a list of analytic pitfalls that will contribute to make it a fad:
Lack of analytics about analytics. One colleague made a vehement case that HR work required more analytics and that rigorous analytics was the wave of the future for HR. We asked him a simple question, “what is your data that suggests that analytics is critical for the future?” Some who are enamored with analytics are not using analytics to justify analytics. They are analytical hypocrites who call for analytics, but do not use analytics to justify the use of analytics.
Mean/end inversion or data fetish. Some are enamored with analytics, thinking that more data (or “Big Data”) is always better. It is not about data, but about data for informed decision-making. For example, what separates distinguished academics like Daniel Kahnemann, widely known for his work on cognitive biases and how same can distort decision-making (see his bestseller Thinking, Fast and Slow), from less distinguished colleagues in academia is not having more or “bigger” data, but having the right data (including qualitative data or other data that is not readily available), asking the right questions, and interpreting the results and implications the right way. Analytics for the sake of analytics is not helpful. Analytics too often starts with data, when it should start with business challenges (hence all the analytics cases linking survey data to turnover because the data is readily available – while it does not yield new, insightful or value adding results). HR succeeds by adding value to business decisions – by informing how to make business decisions that intervene and create business success, not just by validating existing knowledge in practice. Think of the efficiency/effectiveness discussion in HR as an analogy: HR analytics is often preoccupied with “doing things right” with an “inside-out” HR perspective (e.g. do we use the right recruitment assessments? What is the ROI of our training programs? How efficient is our onboarding?), while it may create disproportionately more value when HR analytics applies an “outside-in” perspective and “does the right things” (How do we help transform the organization's culture so we can better deal with market consolidation and expected acquisitions the next 3–5 years? How can we grow critical technical talent faster, cheaper, better than the market to realize our growth strategy in a booming market and differentiate ourselves from the competition?).
Academic mindset in a business setting. Some companies, e.g. Google, Shell, Aramco, PepsiCo, HSBC, are currently using/implementing human capital analytics as a way to bring more theory and rigor to the practice of management. One leading company in fast moving consumer goods hired some well-trained theorists and researchers who set about to predict turnover, consistent with published studies in the academic literature. After enormous effort, they were able to explain more than 70 percent of the variance in retention of human capital. But, when they shared their results, a thoughtful observer said, “so how serious is the problem of regrettable losses in the company?” The researchers responded that the company had less than 2 percent regrettable losses for the key positions and top levels. The academics who went into industry led with theory about what they had studied, not with questions about business challenges facing this company. This company was facing challenges of global market penetration, product innovation in declining markets, an activist investor who wanted to force management changes, and a culture of working within silos rather than collaboration. But, the theory based academics started their human capital work with a theory they were testing (turnover of firm specific assets), not with a deep understanding of business challenges. So even though academia and the accumulated science is an enormous resource for management practice (and an underutilized resource too), not understanding the differences between academia and practice – or academia and actionable analytics – may actually undermine the value of HR analytics. Academics like to create assumptions that allow them to test null hypothesis and offer incremental insights on theory. Business leaders face complicated problems that require integrated solutions. Academics like precision; business leaders require practical “good-enough” solutions. Academics start with theory; business leaders start with real challenges. Academics like to reflect; business leaders have to act.
HR analytics run from an HR Center-of-Expertise (CoE). Recent evidence suggests that chief human resource officers with a clear business focus are still few and far-between (and hence receive a premium on pay). Practical experience tells us that HR CoE's with an “outside-in” approach and deep business understanding are even rarer. HR analytics CoE's will often use big data to discover insights that they will “push” out to the businesses. This is a bit like shooting a gun in the air and hoping a bird flies over. Dust bowl empiricism was popular with the advent of multivariate statistics when statisticians were seeking statistical relationships without a clear theory guiding their analyses, but when analytics are push, not pull, they risk the liabilities of dust bowl empiricism and rarely yield business value. Just as Kahnemann's distinguished work was more about his focus than amount of data, impactful HR analytics is more about strategic business focus than random patterns in big data.
A journalistic approach to HR analytics. Politics and power are real phenomena in any organization. The philosopher Foucault noted that “power is knowledge,” referring to the fact that power in part decides what knowledge creation will focus on or that “history is written by the victors.” HR analytics can be misused to maintain the status quo and drive a certain agenda, i.e. when you know what story you want to tell, and you then go look for data to support same (e.g., requests to “validate the effects of our training”). Just like academia suffers from publication bias, findings showing no effect or even value-destroying effects of HR processes or initiatives are sometimes not shared. In many cases, these require substantial energy devoted to stakeholder management (but are often among the most value-adding HR analytics findings). This is similar to the findings generated by various “think-tanks,” in which the particular focus and interpretation are guided by a particular framework with the purpose of advancing particular points of view. HR should aspire to the ideal expressed by the Scottish novelist Andrew Lang in 1937: “I shall try not to use statistics as a drunken man uses lamp-posts, for support rather than for illumination.” HR analytics departments need future funding to exist, and to do that they must balance good news and bad news about the HR organization, and chose their battles. In particular, there are still several HR initiatives around that are more based on belief than evidence (one of the authors recently encountered a company that uses handwriting analysis in selection during recruitment). This is why HR analytics needs to link company specific findings to published research, and always quote what the external and independent research finds on the investigated topic. This also highlights a big difference between HR analytics and independent academic research, and the value the latter brings to the former. One positive thing that HR analytics can take from journalism is the clear storytelling – if you cannot tell your story, including implications and recommendations in one slide (regardless of study complexity and amount of data used) then the odds of getting executive buy-in are slim. Very good HR analytics work often fails because it adopts the academic communication style and loses its business audience (also at times because it wants to show all the work done, which is really not relevant to share – effort really does not give you any points, only results and insights count).
Our suggestions for moving HR analytics from fad to an ongoing part of management decision-making
On the positive side we also see a number of things pushing HR analytics in the right direction, both in terms of focus, setup, change management, and capabilities in HR.
Start with the business problem. HR analytics should not start with data or a preconceived approach to business problems, but with a business challenge. This point is often noted in the analytics discussion, and is actually the application of the “outside-in” thinking to this particular area of HR, as illustrated in Figure 1. This highlights that analytics and data are really only smaller and integrated parts of the overall diagnostic framework – means and not ends. We also recommend that analytics focuses on the three to five big-ticket issues for the business. This means resisting the temptation to continuously pursue many smaller and less value adding issues (e.g., turnover prediction, learning ROI, simple survey linkage analytics etc. when same are not core for a business issue). Ask yourself: “What are the biggest challenges facing our business the next 3–5 years, and how can HR support the business on same?” – that is typically the best starting point for actionable analytics.
Take HR analytics out of HR. This may sound drastic, but when HR analytics matures, it initially starts cooperating more with other departments’ teams (in finance, operations, etc.), and eventually becomes part of cross functional/end-to-end analytics – looking at human capital elements in the entire value-chain. HR analytics must transcend HR issues and become part of existing cross functional business analytics, just like the analytics from other functions must transcend their functional areas. Analytics typically only yields truly new insights when multiple fields and perspectives are combined (investor perspective, customers, technology, human capital, safety, etc.), so any functional denomination prior to “analytics” is really just a sign that it has not matured enough yet to just be a natural part of “analytics.” Most HR analytics functions are some years away from this, and perhaps need to be matured to some extent within the HR function first (this maturation can be accelerated by importing business analytics talent to run HR analytics – it is often easier to teach business analytics professionals HR than to teach HR professionals statistics and analytics). Technology is also driving the integration of functional analytics; historically, data platforms were limited so each function/line of business typically got its own and correspondingly developed its own reporting team and subsequently its own analytics team. The future belongs to the cloud, real-time data, and cross functional/line of business “enterprise” platforms (which also allows businesses to reduce cost by operating fewer platforms and systems) – and that paves the way for cross-functional, end-to-end analytics. It is time for HR to join the party and “get a seat at the analytics table” and not just sit at its own HR analytics table. This also solves the talent issue in HR analytics (people with statistical analytical capabilities and business understanding typically do not gravitate towards HR), while there may be some practical hurdles to overcome on data-privacy with an end-to-end analytics setup, as HR data is distinct from data used by other analytics teams. Finding a practical way to balance HR data privacy with the business value in insights from analyses of (anonymized) data is a growing issue in any case, but none the less a practical issue that can be overcome (Finance analytics teams face separate challenges, as the right aggregation of data actually can give them inside-trader status).
Remember the “human” in human resources. HR analytics forgot about the H of HR – data and evidence does not change anything, as neither people nor organizations are completely rational. Sometimes it actually just makes it harder to change the status quo. At best, HR analytics provides input for management discussions that can elevate the decision quality, but there is rarely a straight line from data and analyses to action. We can learn a lot from the traditional change management literature and from Festinger's findings on cognitive dissonance. These findings highlight that for most people, given the choice between existing beliefs and new data showing your beliefs are misguided, people will choose their belief system and reject the data. (In Festinger's research, when the members of a UFO cult realized that there would be no Armageddon on earth and Messiah on a spaceship coming to save them on the predicted date, they concluded that actually because they had been so strong in their faith – instead of reaching the logical yet more painful conclusion based on the data, that their belief system just could be wrong). The tendency to reject data that threatens existing beliefs is strong if people have invested time/effort/identity in projects or ideas. That is the case for most HR initiatives, which typically have a proud sponsor or owner, often a senior leader who may not particularly like findings from HR analytics casting doubt on the value of his or her initiative. This is why data and evidence from HR analytics often has little impact – it is not just about science and data – it is about activism and having a point of view, about intervention and change. HR analytics findings are products that have to be sold to have any impact. This is easier if HR analytics also includes qualitative data, intuition, experience and – most of all – if it works on co-creating a coherent story with the key stakeholders. And this story should always start with the business challenge.
Train HR professionals to have an analytical mindset. Let's be honest – most HR professionals are not attracted to HR because of the opportunity to work with data and analytics as part of their role. There is, however, a growing appetite amongst HR professionals to acquire analytical capabilities, in particular when they experience firsthand how it helps them support their business. There are few courses in HR analytics, and those that exist may be superficial. A course in HR analytics would include: deploying a diagnostic framework (see Figure 1), basic training in statistics and science methodology (or perhaps just a recap for some), change management, and storytelling. It is important to be realistic: we typically see a 20–60–20 split between groups of HR professionals who get it, those who can be taught, and those who will never get it. We recommend that you focus on the first two groups, and supplement training with hands-on projects, and closer cooperation with academics. In addition, we would argue that 80 percent of analytics is similar across functions/lines of businesses. The majority of analytics training should ideally be cross-functional, and only a smaller part of the training should be HR specific (or specific for other functions/lines of business).
Two cases showing the value of embedding HR analytics in business analytics
In the following we will illustrate two cases of HR analytics being successfully integrated in business analytics and leading to impactful interventions on offshore drilling performance optimization and technical talent development, respectively:
Leadership quality, crew competence, and outcomes on safety, operational performance, and customer satisfaction.
Maersk Drilling, a leading offshore drilling company and a business unit in the A.P. Moller – Maersk Group, experienced considerable variance in performance between similar drilling rigs operating under similar conditions, and at the same time faced the challenge of growing 40 percent within a four year period. Top management, including the CHRO, was interested in identifying: (1) What explains variance in performance between rigs? (2) How can that knowledge effectively be deployed to new rigs brought into operation?, and (3) How can the results be used to help convince prospective clients that the company will deliver on promised performance standards while growing considerably in a hot market? Business analytics using both qualitative and quantitative data, experience from the business, and offshore leaders’ intuition about what drives performance found strong and significant links between leadership quality (measured via a yearly people survey), crew competence (documented according to the industry standards and requirements), safety performance (from the company's safety system), environmental performance (spills documented in the company's health, safety, and environment (HSE) system according to the offshore industry standards), and outcomes on operational performance (via the company's operational business intelligence system) and customer satisfaction (via the company's commercial customer relationship management (CRM) system) across units in the company fleet. The findings were integrated in an end-to-end value chain analysis, and compiled into one coherent story: Customer satisfaction is about operational performance (in this case drilling performance/uptime), but other factors also matter for company success: leaders assessed more positively (on various standard leadership tasks) by their direct reports have lower crew turnover, lower turnover is associated with higher crew competence (fewer new people to train), which in turn is related to better safety performance, fewer spills, and fewer maintenance hours outstanding (i.e. the time it takes to fix stuff) which impacts customer satisfaction. Recommended action is to focus on leadership quality (training and selection), crew competence (training budget and controls) and maintenance hours outstanding across the fleet by placing same on unit scorecards, and to communicate the findings throughout the company to all leaders and employees and to existing and prospective clients.
Even though advanced statistical methods were used (logistical regression models on longitudinal data), the presentation just showed the r-squared values between the different elements, keeping in mind that this was not for an academic audience. It was to support storytelling for a (technical) business audience, and emphasizing the importance of co-creating the story with the many stakeholders. The analytics were part of a change management process Figure 2.
ROI and Strategic Impact of Technical Trainee Acceleration Program
The same offshore drilling company, Maersk Drilling, had challenges filling lead specialist positions due to industry talent shortage and growth. It had experimented with a strategic initiative to develop technical talent for the senior specialist target positions. Business analytics was used to identify that the company graduate program for Specialist Trainees showed desirable results on key outcomes compared with their peer-group (see Table 1and Figure 3). In addition to showing simple training ROI, the findings fed into a strategic talent discussion (build/buy/borrow). The company decided to double the graduate program intake to sustain its growth plans. Again, simple statistics were used to support the story (see Table 1 and Figure 3). In this case, it was also the co-creation of the story – backed by data and analytics – and that analytics was treated like a change management process that paved the way for the results to have a positive business impact.
We experience that as soon as we question the analytics movement, we get labeled troglodytes who live in the past and are out of date with modern HR. We disagree. The HR field is littered with good ideas that have not been institutionalized. We hope that our recommendations offer a way to make HR analytics a realistic and ongoing part of improved HR impact.
For a comprehensive illustration of the role of HR analytics and the use of HR data to tie people, strategy, and performance together, see E. Becker, M. Huselid and D. Ulrich, The HR Scorecard: Linking People, Strategy, and Performance (Harvard Business Press, 2001). An in-depth discussion of HR metrics and analytics in evidence-based management can be found in E. Lawler, A. Levenson, and J. Boudreau, “HR Metrics and Analytics: Use and Impact,” Human Resource Planning, 2004, 27(3), 27–35; J. Pfeffer and R.I. Sutton, Hard Facts, Dangerous Half-Truths and Total Nonsense: Profiting From Evidence-Based Management (Cambridge: Harvard Business School Press, 2006); J. Phillips and P. Phillips, ROI at Work: Best-Practice Case Studies from the Real World. (ASTD Press, 2005); and D. Rousseau, “Is There Such a Thing as Evidence-Based Management?” Academy of Management Review, 2006, 31(2), 256–269. For a perspective from economics on the use of data and statistics as input for decision-making in management on personnel related matters, see E. Lazear, “The Future of Personnel Economics,”Economic Journal, 2001, 110(467), F661–F639. For a classical perspective of the limited impact of facts and data on belief systems, see L. Festinger, R. Henry, and S. Stanley, When Prophecy Fails: A Social and Psychological Study of a Modern Group that Predicted the Destruction of the World (University of Minnesota Press, 1956).
Thomas Rasmussen is vice president, HR data & analytics, at Royal Dutch Shell and has previously led HR analytics at A.P. Moller-Maersk. He received his M.Sc. and Ph.D. in psychology from the University of Aarhus, Denmark, and his main interest is bridging management science with practical application. (Royal Dutch Shell, Carel van Bylandtlaan 16, 2596 JM, The Hague, The Netherlands, tel.: +31703774716, e-mail: Thomas.Rasmussen@shell.com).
Dave Ulrich is the Rensis Likert Professor of Business at the Ross School of Business, University of Michigan. He has published over 25 books and 200 articles on leadership, organization, and human resources. His work demonstrates how organizations create value for employees, customers, and investors. (Rensis Likert Professor, Ross School of Business, University of Michigan, 701 Tappan St., Ann Arbor, MI 48109-1234, tel.: +1 (734) 764-1817, e-mail: firstname.lastname@example.org email@example.com).
Tel.: +1 (734) 764-1817.
Five essentials for HR analytics success
Find one source of truth
Remember data is more important than tech
Build strategic internal alliances
Make analytics part of the HR skillset
Be passionate about HR analytics
The Geeks Arrive In HR: People Analytics Is Here
Today, for the first time in the fifteen years I've been an analyst, human resources departments are getting serious about analytics. And I mean serious.
I was in a meeting several weeks ago in San Francisco and we had eight PhD statisticians, engineers, and computer scientists together, all working on people analytics for their companies. These are serious mathematicians and data scientists trying to apply data science to the people side of their businesses.
This last week I had another similar meeting and we had three of the world's leading insurance companies, two large retailers, three health care companies, and two manufacturing companies with serious mathematicians and scientists assigned to HR.
What's going on?
As I recently discussed in the article "How People Management is Replacing Talent Management?" there is a major shift taking place in the market for people analytics. After years of talking about the opportunity to apply data to people decisions, companies are now stepping up and making the investment. And more exciting than that, the serious math and data people are flocking to HR.
A little history is in order.
The area of HR analytics, talent analytics, or as it is now called "people analytics" has been around for a long time. As an analyst (and former analytics product manager) I've been talking with companies about how to measure learning and HR for a decade. Back in 2005, after several frustrating years trying to figure out how to measure training, I wrote a book called The Training Measurement Book, which sets the stage for L&D teams to move beyond the traditional Kirkpatrick measurement model. Today learning organizations continue to try to analyze the impact and effectiveness of training, but it no longer stands alone.
If you look back in time, ten years ago companies tried to build "HR Analytics" systems (typicall called HR data warehouses) to help companies look at simple metrics like "total headcount," "time to hire" and "retention rate" and clean up their messy, often inaccurate people data. Quite a few companies built these databases, but they were primarily used to be a single system of record across the many HR platforms in place.
In the 1990s vendors like PeopleSoft, Oracle, and NCR/Teradata built analytics products to support this market. They didn't sell very well, primarily because companies had such complex HR systems they didn't have the budget or IT support to build the HR data warehouse. (Some companies did do this, and they have been benefiting from this for many years.)
About five years ago the book Moneyball came out, and we started a global marketplace called "Big Data." Tools like Hadoop, R, and other parallel data management tools became productized and industries like marketing, advertising, and finance started to analyze massive amounts of data. Much of this started at Facebook, Google, LinkedIn and other internet companies who simply had to analyze enormous amounts of data to run their businesses.
Along the way the term "Data Science" was invented, and today there are hundreds of jobs for "Data Scientists." (Typically defines as people who understand information management, Big Data tools, statistics, and modeling - a rare breed.)
During the last ten years we watched the discussion with HR stay very tactical, focused on operational reporting and simply fixing the mess of incompatible HR systems we have. There were many HR and learning analytics presentations and a few conferences, but most of the focus was helping technical practitioners improve their reporting systems. The idea of predictive analytics was little more than ROI studies to look at whether a training program worked.
(Full disclosure, I was the head of product management for two companies that built advanced learning analytics solutions in the early 2000s.)
Suddenly around 2011, with the focus on Big Data, we sensed a shift in the market. To understand how well predictive analytics was taking hold, we started our early research on "Big Data in HR" and developed a maturity model (it was published in the Fall of 2012). We discovered a world of "Haves" and "Have Nots." A small number of companies were investing heavily in predictive people analytics, but most were barely getting started.
The whole idea of our focus on "Big Data in HR" was to help HR organizations realize that they, too, could enjoy the wave of interest in Moneyball and BigData. HR is not as interesting a topic as homeland security or cyberwarfare, but it's a big area of spending (more than $4 trillion is spent on payroll around the world) so there's a lot of opportunity in this huge data set. And the world of "People Analytics" was born.
There is a deep history of data analysis in the HR profession, starting with Frederick Taylor in the late 1800s. The article "The Datafication of HR" describes this evolution, and I think everyone in this space should read this article and get to know the history. Today we are standing on the shoulders of some giants and very innovative thinkers - they just didn't have computers to help.
Today, while the topic is hot, HR teams are just starting to get good at analytics. The problem has not been the concept, but rather the focus. We spent far too much time trying to measure HR and L&D spending, and figure out which HR programs were adding value. While that seems interesting HR managers, typically business people just don't care. What they want is information that helps them run the company better: "Get me the right people into the job, make them productive and happy, and get them to help us attract more customers and drive more revenue. I don't care if your L&D program has a 200% ROI or not."
(Slideshare History of People Analytics: The Datafication of HR: People Science is Here )
We now see this as a huge trend, so we launched a focused research area on this topic. With the help of my partner Karen O'Leonard and others on our team, we launched a series of industry studies on what we called "Talent Analytics." Our biggest report, entitled High-Impact Talent Analytics, established the first-ever research-based maturity model for analytics. It showed that there were a small set of companies (less than 5% of the market) that were way ahead of the curve. These advanced companies were looking at people-related data in a very strategic way, and they were making far better decisions about who to hire, who to promote, how much to pay people, and much more.
Since then, interest in this market has exploded. And I mean like an atomic bomb. Everyone is now talking about it, and the whole concept has changed.
A few weeks ago I had a meeting with five major Silicon Valley and New York companies who are focused in this area, and the room was filled with statistics PhDs, engineers (like me), and I/O psychologist PhDs. Thus the title of this article:
The geeks have arrived, and we're all happier for it.
At this point, entering 2015, I believe "The Geeks have Arrived." Statisticians, mathematicians, and engineers have entered the people analytics space.
In this meeting I recently attended, the practitioners, who are among the leaders in this space, were all experienced in bringing together data, cleaning it up, and doing all types of analysis. Of course their companies have various issues with data quality, systems, and infrastructure - but they, as a group "get it." They understand the potential, they understand the problem, and they have the skills to get work done. And they are not just analyzing HR issues, they are analyzing the business.
Today this new business function is called "People Analytics." And over time, I believe it doesn't even belong within HR. While it may reside in HR to begin with, over time this team takes responsible for analysis of sales productivity, turnover, retention, accidents, fraud, and even the people-issues that drive customer retention and customer satisfaction.
· High tech companies now know why top engineers quit and how to build compensation and work environments to get people to stay.
· Financial services companies are now analyzing why certain people commit fraud and what environmental or hiring issues might contribute to such violations.
· Product companies are now analyzing the demographic, educational, and experiential factors that correlate with high performing sales people and why top sales people quit.
· Health care companies are looking at why certain hospitals or departments have higher infection rates and what people issues are behind these problems.
· Manufacturers and product companies are looking at the patterns of email traffic and communications to understand how high performing managers behave and what work styles result in the highest levels of performance.
These are all real-world business problems, not HR problems. The data which helps support these decisions includes experience, demographics, age, family status, as well as training, personality, intelligence, and dozens of other factors. More and more this will include data on email communications, employee sentiment, and ad-hoc feedback.
Many of the factors which contribute to fraud or turnover have nothing to do with the people - they are environmental. Where is the manager physically located? Who else is hiring in this location? So People Analytics requires a look at external data, not just internal data.
This is why this function eventually belongs outside of HR, it is really a part of a company's bigger "business analytics" team.
Just for grins I did a Google Trends search on the terms HR Analytics, Talent Analytics, and People Analytics, and look at what I found. "People Analytics" is winning.
As we talk about in our research, this is a huge market opportunity for business - one that is just beginning. Vendors of all shapes and sizes are starting to grow, and most of the large platform providers now include predictive analytics tools embedded in their core HR software. (Flight risk indicators are a good example - not necessarily accurate yet, but the right idea.)
And exciting new companies are joining the marketplace. (Read People Analytics Heats Up for more on all the vendor activity.) Not only are the large ERP players involved, but serious software entrepreneurs are joining the market. Last week I met with two senior software executives (both from large search engine companies and other companies they had sold) now entering the market for HR engagement analytics and measurement systems. I wont mention the company yet (it's not yet launched), but this is a company that is likely to bring serious software engineering to the people analytics market.
While most HR organizations are still struggling to clean up their data and build their teams, the momentum is coming on strong. And technical talent has now figured out that the old-fashioned backwater HR department may be one of the most exciting places to work.
We'll be doing a lot more research on this topic over the coming years, but let me simply state clearly "The Geeks have Arrived: People Analytics is Here."
The Talent Returns on an HR Analytics Investment
If you’re reading this article, you’ve already bought into the critical need for talent analytics as an HR capability. Analytics is a prime focus for HR professionals for good reason. It’s a time-intensive, perilous path full of obstacles and plenty of chances for missteps. Few companies—even multinational corporations pouring millions of dollars into Big Data about their people—use high-value analytics effectively, or in many cases, much at all. For HR, analytics are a clear path to stronger executive influence as an “anticipator” rather than a “reactor” or “partner,” and “future-oriented” is the most notable characteristic of HR data seen by senior business partners as valuable and ideal (even more so than “relevant” or “frequent”).
So talent analytics are well-recognized as an input to HR success—but what’s the output? If analytics are a means to an end, what are the near-term outcomes and long-term returns from getting talent analytics right, and how does a high-caliber analytics program translate to how companies actually manage their leadership talent?
In our Global Leadership Forecast 2014|2015, we researched 1,500 organizations ranging across the spectrum of sophistication for analytics, and gathered information on how many of these seven types of leader-focused analytics they did well:
- Gathering efficiency/reactions metrics about leadership programs—30% did it effectively
- Benchmarking leaders internally—27% did it effectively
- Gathering results metrics about leadership programs—24% did it effectively
- Using data to forecast future leadership talent needs—23% did it effectively
- Using data to design/optimize leadership talent programs—22% did it effectively
- Gathering business impact metrics about leadership programs—21% did it effectively
- Benchmarking leaders externally—13% did it effectively
Across this set of analytics techniques, only 5 percent of organizations have mastered all of them, while 47 percent were either not doing any of them well or weren’t doing them at all. Alongside the questions on analytics, we also asked HR professionals in these organizations to tell us about their talent practices and outcomes. We found five near-term talent outcomes to best differentiate the 1 in 20 companies executing all types of analytics well (Analytics Masters) from the worse half of their peers doing none effectively (Analytics Laggards):
How Analytics Masters Differ from Analytics Laggards: Near-Term Outcomes
- Leaders have high-quality development plans, which they review regularly with their managers—Talent-focused analytics drive current know-how of what leaders need to succeed, making actionable development stickier and more targeted.
- Organizations measure the effectiveness of high-potential programs—High-potentials often get (and justifiably so) an outsized time/expense investment; Analytics Masters gauge whether these programs are worth it and how to course-correct if they’re off-track.
- Know the up-to-date status of leader talent—Knowing who you have and where they’re strong and weak is heavily fueled by analytics, particularly workforce planning.
- Use a systematic process to identify the quantity and quality of leadership to drive business success—Analytics Masters gather high-quality information about what it takes for leaders to succeed, and how many leaders are needed.
- Use formal programs to ensure smooth leadership transitions at all levels—Leadership transitions are extremely risky; analytics collect information about the diagnostic assessment programs used to identify and prepare leaders for new roles—are leaders reacting to these programs positively, and is data in place to prove their impact?
The practices above are lead indicators of analytics success—looking further out, at talent outcomes whose effects take longer to observe, which of these do Analytics Masters achieve? In our research, four outcomes rose above the rest as key long-term differentiators:
How Analytics Masters Differ from Analytics Laggards: Long-Term Outcomes
- Stronger bench strength for next three years—Analytics Masters use analytics to understand and reduce talent risk, and when looking three years forward, have a much stronger set of future leaders in place—on average they can fill 19 percent more critical roles immediately with internal candidates compared to Laggards.
- Higher current quality at all levels, front line to senior—Though Analytics Masters don’t outperform Laggards in current leader quality as much so as in bench strength for the upcoming generation of leaders, they do have higher-quality leaders from the front line to the C-suite.
- Success rate for high-potential and expatriate leaders—Analytics Masters are more disciplined in their management of costly high-potential and expatriate talent, and their success rates for these roles are 15 percent higher than Analytics Laggards.
- “Stickier” leadership development—Analytics Master companies build better personal development plans for their leaders and know more precisely which characteristics drive success—helping leaders apply learning back on the job at a much higher rate.
For the few organizations able to reach Analytics Mastery, advanced and deep proficiency in these methods comes at a high price, but generates a healthy payoff. These benefits are rooted in accurate talent audits, well-aligned leader development plans and programs, ongoing, systematic measurement of program effects and impact, and ultimately, a stronger current and future roster of talent to lead them forward. In contrast, Analytics Laggards’ shortfalls not only leave them lacking in these same areas, but in a lengthy state of data-blind ignorance about just how far behind they are.
For more information about the Global Leadership Forecast 2014|2015 research, including 25 highly actionable findings about the current state of leadership, an evidence-based roadmap for leadership development, a scoreboard of 20 common talent management practices, and global benchmarks for 11 metrics about leadership talent, see http://www.ddiworld.com/glf2014.