Consider this real-life scenario: General Electric Co. is interested in studying the relationships between variables in one of its manufacturing plants. In one project, the industrial conglomerate attempts to show how its human resources function could affect plant productivity and profitability. An analysis of variables that affect productivity and profits reveals the important variables tracked by the HR department in a particular plant. They are:
- Accidents: Disruptive and decrease productivity.
- Employee turnover: Excessive turnover is disruptive operationally and increases costs.
- Unplanned absences: Excessive unplanned absences are disruptive, as workdays are lost.
- Grievances: Excessive grievances reduce productivity because they often affect morale of employees. Grievances often stem from problems in the organization that block or inhibit productivity.
- Initial dispensary visits: Unnecessary first aid visits disrupt operations and increase costs.
The HR team tracked these measures and developed a mathematical relationship between them and productivity. A composite score of these variables is developed and named the employee relations index. The indicators were combined by means of a multiple regression formula, with the variables receiving different weights. Constants were added depending on the level of the variable in a plant and for the particular plant or group in question. According to its users, the ERI was intended to help managers evaluate policies and practices, track trends in employee relations, find trouble spots, perform human relations duties more effectively and control HR costs. Index values were compared with plant profitability (ratio of net income before taxes to capital investment). Although the plants with the higher ERIs were the most profitable ones, the relationship was not statistically significant.
The most interesting aspect of this study isn’t its results — it’s the date. While the study’s analytic elements appear in line with today’s statistical culture, it was actually conducted in the 1950s and published in the Harvard Business Review in 1955. For many years, GE focused on quantitative methods and mathematical processes. The company even developed an important series of books on mathematical models for management in the 1960s.
In today’s human capital analytics terminology, this study is based on a classic predictive model, where the independent variables (accidents, grievances, absenteeism, first aid visits and turnover) predict a dependent variable of plant profitability. Perhaps, if the researchers had used gross productivity (revenue per employee) instead of plant profitability, a significant connection (and causal relationship) could have been developed. In the 1950s, however, this project wasn’t labeled analytics; it was simply referred to as the development of an ERI index.
This study underscores the deep roots of the field of human capital analytics. Yet, for some leaders, analytics is a generally new phenomenon. While human capital analytics has been in place for some time, it has lacked a necessary cohesive focus and label. Moreover, the field hasn’t, until now, had the computing power or tools available to make the process easy for anyone to use.
Here we examine the brief history of human capital analytics. We also use the results of a survey our organization, ROI Institute, recently conducted with a number of partners to establish the state of the field today, as well as offer lessons for how executives can maximize use of the tool to help them lead their organizations along the way.
In 2007, the book, “Competing on Analytics: The New Science of Winning,” by Tom Davenport and Jeanne Harris revealed the power of analytics to make critical business discussions. While most of the book’s focus is on marketing, where analytics has been very powerful, it also mentions the possibility of using analytics in other business areas, such as IT, operations, logistics and human resources. After all, the largest expenditure in an organization is its people. With that in mind, the prospect of using people analytics created a great backdrop for human capital analytics growth.
Since Davenport and Harris published their book, use of analytics has proliferated throughout business, including human resources. Whether it’s called human capital analytics, HR analytics, people analytics or talent analytics, there are now dedicated teams focusing on this important topic. At the same time, organizations have struggled to get their human capital analytics teams up to speed and running smoothly. Specifically, most organizations have struggled with these five questions:
- Who should be part of the team? In the beginning, it was assumed that statisticians and IT professionals should dominate the analytics sphere, so the power of technology and data analysis tools could be released. In some cases, that didn’t work because these individuals may not have clearly understood the opportunities, possibilities and strategies needed for success with analytics.
- How much should be spent on analytics? For many, at first this was a guess, starting small and growing as needed. For the most part, budgets are still fairly modest.
- Which projects should be pursued? This is another area executives first struggled with in terms of how to maximize people analytics. Leaders at first didn’t know how to implore the function and at what scale. Larger projects have potentially the most impact, but companies need to use the tool for smaller projects as well. Initially, many executives had difficulty finding the right balance.
- How should the data be used? To develop an analytics study and send it to decision-makers without much explanation is a recipe for disaster. The study needs to tell a story, be action oriented and contain information to help executives make decisions.
- How is the human capital analytics practice sustained? The big challenge is to sustain the practice. The success factors for an analytics practice are presented later.
The ROI Institute recently completed a major analytics study along with The Institute for Corporate Productivity, The Center for Talent Reporting and Vestrics. Data was collected from 317 human resource teams with a dedicated analytics practice to find out the state of the industry. Here we outline the study’s major findings, along with same figures to show the data in more detail.
Executives Support Talent Analytics
Almost 70 percent of survey respondents expect their analytics budgets to increase in 2017. Still, today’s budgetary standard is not a very high bar to exceed — most analytics budgets in organizations are relatively modest. It’s not atypical for organizations to have an analytics staff of one or perhaps a few people who also have other responsibilities. Our survey suggests that is quickly changing, as many organizations are hiring human capital analytics professionals and some are beginning to fund platforms and tools for these teams.
Management appears more convinced than ever of the power of people-related data, according to the survey, as about 43 percent reported having strong support from the management team versus 27 percent who said they did not have similar support. Roughly 34 percent of respondents said they had executive commitment — a relatively small number, but promising nevertheless.
There were some areas in the survey where the results suggest the field is in need of some improvement. Among the most notable findings on this front is that only 30 percent said they believe the human capital analytics function has strong credibility within their organizations. Additionally, just 22 percent said they believe they have achieved several major successes with the function each year. It is our view that these percentages will undoubtedly grow as human capital analytics tackles more strategic projects within organizations.
The survey found that organizational effectiveness was the primary purpose for the existence of the HCA function itself. When organizations begin to show concrete improvements in effectiveness as a result of data analysis, the levels of management and executive commitment to people analytics will also see concrete improvements.
Indeed, the ability to support strategic planning was the second-highest response to the question of the purpose of the human capital analytics function, accentuating the need for more evidence-based decision-making in organizations about their workforce forecasts.
When asked about how projects are identified for analysis, about 70 percent of respondents chose strategic priorities far above the 23 percent that said they used analytics to conduct program cost analysis. Furthermore, the majority of respondents said in the survey that requests from top executives and business leaders dominate new project identification.
Decisions on analytics are also taking a decidedly strategic and executive approach. When asked how the human capital analytics team follows up on action items, the top response was that actions are placed on executive agendas. Who views the data is significant. Money and management support matters. So does measurement.
Analytics Teams Are Maturing
An important dilemma organizations face is whether to outsource the people analytics function or develop it internally. It’s surprising that, given the growing interest in the people analytics field combined with ongoing resource constraints, there aren’t more outsourced offerings available in today’s market. As a result, most organizations have elected to tackle this task internally; our research shows that about 58 percent of organizations are using their own resources, whereas 42 percent are using external consultants.
When cultivating human capital analytics proficiency organically, a common quandary for organizations is determining the skill set needed for the function. Anecdotally, many organizations have a collection of skills and backgrounds that make up the human capital analytics team. Responses to this question in our survey supported this, as the skills cited varied widely.
According to the survey, a company’s human resources function was by far the top answer to a question about the expertise of people analytics team members. Organizations often lament their trouble in finding talented analytics professionals who understand human resources. In fact, as our survey suggests, many wrestle with the question of whether or not to hire for analytics acumen and teach the HR part when vetting qualified candidates — or to hire existing HR staff to step up and handle analytics tasks. Having too many quantitative analysts, or quants, created a problem with some teams among early companies to adopt people analytics. While quants generate the data, the ability to tell the HR story is typically a challenge for analytics teams, the survey shows. Judging by the responses to the question about the skills of human capital analytics team members, HR is an expertise that is highly sought after and valued within people analytics teams.
Unsurprisingly, many related skills were cited by respondents — statistics, math, operations research, IT, data, systems, operations and support all received significant responses. And unlike many of the questions asked, the “other” category received over a quarter of the responses, reflecting that analytics teams are comprised of people with widely varying skills and competencies working on an organization’s people analytics issues.
The Use of Human Capital Analytics
The team that is using results from human capital analytics projects is primarily human capital professionals (81 percent). But what was quite encouraging is that the next most cited groups included the top executive team, operating managers and the CEO. This finding aligns with the qualitative interviews conducted for the study, which showed that leaders in high-performance organizations are increasingly relying on people analytics to help drive better business results.
Despite the growing use of people analytics by business leaders, our survey research suggests that this is a capability that still needs to evolve in many organizations. When we asked survey respondents how important analytics was to decisions they make in broad areas of human capital, there were several answers that garnered almost the same number of responses. Fewer than eight percentage points separated these answers, and the list represents essentially the life cycle of the workforce: plan, acquire, develop, engage and retain. Organizations are using analytics in numerous ways to make decisions on their talent throughout the employee life cycle.
Projects are Moving from Descriptive to Predictive
The types of human capital analytics projects that teams embark on can be broad, but one area stood out: measuring the impact and return on investment of a program. Almost 70 percent of respondents mentioned this, along with another 29 percent that said “forecasting ROI” was a primary driver for their projects. The ability to accurately predict and then calculate ROI is alive and well, particularly for talent-related programs in which determining the impact on the bottom line is often a challenge. This study confirms that the gap between what CEOs reported they want in our 2010 CEO study from the talent investment (impact and ROI) and what they have been receiving is beginning to close. Return on investment is the ultimate measure of program success, and the results of this study reinforce its importance in the human capital space. If a program is expensive, important and strategic, organizations must have the capability to show its value up to ROI.
The second type of project is “relationships between variables.” Meanwhile, the third type of project is “developing predictive models” with these relationships. Converting hard-to-value measures such as engagement and stress to monetary values accounts for 32 percent of analytics projects, the survey shows.
One of the biggest areas in talent that has historically tackled analytics is learning and development, which topped the list when respondents were asked about the functional areas they were addressing with people analytics. For decades, learning professionals have struggled to show ROI for their programs, but with planning, guidance and tools, the mature departments have been accurately calculating ROI on a variety of programs, including leadership development.
Other functional areas respondents said they were addressing with human capital analytics included engagement, organizational development, recruiting and selection succession planning, and diversity and inclusion. Each of these areas shows significant impact to an organization, as long as professionals know where to pinpoint their efforts.
Many of the ways in which human capital analytics is used today could be labeled as descriptive, meaning the data is simply being used to tell the story of what has happened and what is currently happening in the function. Of course, the goal of any analytics professional is to reach the predictive analytics stage. Understandably, this is difficult to do, but once organizations are able to use data to help predict items such as attrition, workforce staffing needs, talent and skills gaps, they gain a tremendous advantage, our survey data suggests. For instance, several interesting areas of predictive relationships were reported as shown in Figure 1.
When we asked those same people what relationships they envisioned being important in the future, in addition to those already listed, we saw significant jumps in relationships such as job satisfaction vs. customer satisfaction; job satisfaction vs. (candidate) attraction; stress vs. productivity; and conflict vs. productivity.
Benefits of Talent Analytics: Sustaining the Practice
The influence of truly integrated talent management is being felt in many of the world’s top organizations, and people analytics are playing a big role in improving each component of the employee life cycle. The goal of all of this is to positively affect the bottom line. The majority of respondents said “driving business performance” is the top driver of talent analytics in their organizations, far above more HR-related reasons such as “enhance respect for human resources” or “increase funding for HR.”
The key takeaway for leaders from this study is for them to have a more accurate picture of where they can have the biggest influence on their organization’s performance using people analytics. Some organizations are farther along than others when it comes to instituting a talent analytics function and making use of its strategic usefulness. Even more encouraging is the fact that many are still early in their analytics life cycles. The challenge ahead is to sustain and grow the human capital analytics practice.
Jack J. Phillips is chairman of ROI Institute Inc., a research and measurement advisory firm. Patti P. Phillips is president and CEO of the firm. To comment, email firstname.lastname@example.org.Filed under: Talent EconomyTagged with: analysis, analytics, calculate, data, human capital, money