2016 global human capital trends

People analytics

Gaining speed

February 29, 2016

The use of analytics in HR is growing, with organizations aggressively building people analytics teams, buying analytics offerings, and developing analytics solutions. HR now has the chance to demonstrate ROI on its analytics efforts, helping to make the case for further investment.

View the complete Global Human Capital Trends 2016 report

The people analytics revolution is gaining speed. While HR organizations have been talking about building analytics teams for several years, in 2016 we see a major leap forward in capabilities. Businesses have recognized they need data to figure out what makes people join, perform well in, and stay with an organization; who will likely be successful; who will make the best leaders; and what is required to deliver the highest-quality customer service and innovation. All of this can be directly informed by people analytics. Companies are hiring people analytics staff, cleaning up their data, and developing models that help transform their businesses.

  • This year, the percentage of companies that believe they are fully capable of developing predictive models doubled, from 4 percent in 2015 to 8 percent in 2016. In 2015, only 24 percent of companies felt ready or somewhat ready for analytics; this year, that number jumped by one-third, to 32 percent.
  • People analytics today brings together HR and business data from different parts of the business and is now addressing a wide range of challenges: analyzing flight risk, selecting high-performing job applicants, identifying characteristics of high-performing sales and service teams, predicting compliance risks, analyzing engagement and culture, and identifying high-value career paths and leadership candidates.
  • Analytics technology is now available off the shelf, embedded in most ERP and talent management systems, engagement tools, text and semantic analysis tools, and recruitment and learning platforms.

After several years of discussing the need for analytics within the HR function, last year’s Global Human Capital Trends report concluded that the drive for analytics was “stuck in neutral.1 Companies were investing heavily in HR systems replacement projects and talking about analytics, but few were actually making progress in this vital new business function.

Driven by competitive pressures and the greater availability of more integrated systems, organizations are aggressively building people analytics teams.

People-feature-imageThis year, the situation has changed for the better. Driven by competitive pressures and the greater availability of more integrated systems, organizations are aggressively building people analytics teams, buying analytics offerings, and developing analytics solutions. Fully 77 percent of all organizations believe people analytics is important. (See figure 1 for our survey respondents’ ratings of people analytics’ importance across global regions and selected countries.) And more than half (52 percent) of the organizations now rate themselves as excellent and 38 percent as adequate at conducting multi-year workforce planning.

ER_3028_Figure 1. People analytics: Percentage of respondents rating this trend “important” or “very important”

The name of this trend—“people analytics”—reflects the use of people-related data to improve and inform all types of management, business, and HR decisions throughout the company. The focus areas vary based on industry and specific business issues.

What are companies doing? Examples of positive momentum can be found in a number of different areas:

Sales performance and recruiting

  • Insurance companies have analyzed the profiles of top salespeople and now know that screening candidates for grade point average or academic pedigree is no longer considered a strong indicator of future sales performance.
  • A high-tech company developed an analytics model that accurately predicts job candidates who are likely to become “toxic employees” (those who lie, cheat, or commit crimes) and dramatically reduced this population among its hires by scrutinizing special parts of the interview process.


  • Software companies, retail banks, and manufacturers are looking at the characteristics of top salespeople, realizing that their personal networks, how they work internally, and the time they spend with customers predict results much more accurately than the amount of sales training or experience.
  • IT and HR departments are now looking at email metadata to understand why some people are more productive than others, then reducing the number of internal meetings to improve output.
  • A large cosmetics manufacturer set up a “sales productivity center of excellence” in HR to study hiring patterns, training, compensation, and other people practices in the sales force to optimize productivity using HR and people-related data.
  • A UK retailer found that by linking retail sales data to the recruitment of store managers, analytics improved profitability dramatically both at the store level and for the organization as a whole. In short, the data showed precisely how better leaders, higher offer acceptance rates, and reduced time to hire drove store profitability.
  • Automobile companies are studying the patterns of unplanned absences to predict when people are likely to take a day off, pre-scheduling extra staff to make up for known periods of absence.
  • The Ministry of Energy of the Government of Mexico is using a predictive workforce planning and analytics model to identify current and future talent and skills gaps in critical oil and gas occupations over a 10-year horizon.2 The model leverages a number of adjustable macroeconomic variables such as oil price and exchange rates that correlate strongly to the demand and supply of skilled labor. Based on an understanding of these gaps in critical skills, the ministry is able to work proactively with multiple stakeholders to address them. Building off from this initiative, the ministry has expanded the use of workforce planning and analytics to cover other sectors it is responsible for, such as renewable energy and sustainability.


  • A pharmaceutical company and a software company are now collecting data from LinkedIn and other social networks to predict the “high-flight-risk” candidates among their high-potential employees.
  • Companies like Deloitte Canada are experimenting with smart badges, using them to gather data suggesting that offices with larger shared work rooms, more light, and more inter-company collaboration have higher retention and productivity.3
  • MasterCard is developing predictive models directed at improving the employee experience through a range of data sources. The company is analyzing patterns in people data that will allow decision makers to assume accountability for issues such as retention of high-potential employees and predicting attrition.4

Compliance and risk

  • Banks are studying patterns of fraud and noncompliance, and can now predict behaviors that will likely result in unethical behavior.
  • A UK financial services company uses analytics to evaluate individual employees, spotting potential “rogue traders” and other compliance breaches as a part of proactive risk management.
  • A large electric utility that recently had an accident analyzed employee feedback and engagement data and realized it could have predicted some of the problems before they occurred, and is now monitoring these data more regularly.


  • A team of organizational development experts and data scientists from eBay measures the strength and adoption of its cultural values through a combination of internal and external data metrics. To compare eBay employees’ views with external perspectives, the team also conducts thematic analysis and natural-language-based analysis on news articles and Glassdoor to get a view of the external market perspective of eBay’s culture.5

Each of these examples (and there are hundreds more) reveals the opportunity to take people data (some from HR, some from outside HR, and some external to the company) to make better management decisions. Google, Twitter, and most other tech firms have people analytics teams.6

We expect the trend toward analytics-driven HR to continue gathering strength over the coming year.

Today’s people analytics teams often call themselves the “employee listening” department. They bring together data from a range of sources, including core HR systems, employee engagement data, survey data, external data (from LinkedIn, Glassdoor, and other systems), and text data from employee comments. Then they analyze these data to understand company culture, find opportunities to improve retention or performance, or diagnose management weaknesses or other operational problems.

What is driving the upsurge in people analytics adoption?

First, companies are now rapidly adopting more integrated cloud-based HR systems, giving them an opportunity to look at their HR data in an integrated way for the first time. Nearly 40 percent of all global firms are either replacing or plan to replace their core HR systems over the next two years.7

Second, people with analytics backgrounds are coming into HR.8 Companies are now bringing industrial and organizational psychologists, statisticians, and analysts from other domains into HR; they are attracted to analytics because it is an exciting, new, and still-fluid area. Data science careers are now hot professions for college graduates and more people are coming to this profession than ever before.

Third, the vendor market is exploding. Nearly every ERP vendor and talent management provider now offers off-the-shelf analytics tools, and many include embedded models. Some are starting to offer analytics services that provide repeatable solutions across clients. In addition, organizational data are more useful than before: This year, 42 percent of survey respondents said the data supporting HR analytics were “good” or “very good”; only 17 percent still rated their data as “poor.”

Fourth, there is now a small army of people science experts, many of whom were pioneers at some of the early adopters, available to consult with large companies. They are sharing ideas and bringing expertise to companies new to the domain.

Finally, CEOs are reading about this topic in the business press, so they are pressing their CHROs to build this capability. For instance, a CHRO of one of the largest health care insurance providers is investing in a three-year, multi-million-dollar program just to clean up employee data, so the company can take a lead in analytics within four to five years.

While there has been much progress, there is much room for improvement. In this year’s survey, 62 percent of organizations rate themselves as “weak” in using big data in recruiting. Some 55 percent of organizations similarly report being weak at using HR data to predict workforce performance and improvement.

We expect the trend toward analytics-driven HR to continue gathering strength over the coming year. As this happens, analytics will penetrate deeper within HR, extending beyond talent acquisition to learning and development and operations. In fact, the Global Human Capital Trends survey data show us that HR is now more convinced of people analytics’ importance than the business, with 82 percent of HR respondents viewing it as important or very important, compared to only 69 percent of business people viewing it as important or very important. HR has the opportunity to show the value and ROI that investment in analytics can bring, which will result in a willingness to invest further and spur acceleration in analytics capabilities.

Unsurprisingly, all this leads back to greater investment in HR, generating a virtuous cycle where higher ROI justifies greater analytics investment. The success of analytics comes down to measuring the value of people to an organization—and analytics is key to unlocking that value.

However, providing great data and insights is only part of the solution. The real value is in turning these insights into change that delivers business value. The hardest part of people analytics is implementing the changes recommended by the models, which call for people analytics to be accompanied by sound change management practices. One large company recently discovered it was underpaying its high performers and overpaying its mid-level performers. It took several years to teach managers (and the organization itself) that it makes business sense to offer a large raise for high performance and a middling raise for fair performance. The key is to invest simultaneously in analytical skills and in interpretative and transformational skills to ensure that the insights deliver value to the business.

Lessons from the front lines

In September 2015, GE brought together all the digital and analytics capabilities across the company into one organization, GE Digital. At the same time, the organization put forward the goal to be a top 10 software company by 2020.9

Developing an integrated talent management strategy was critical in making the move from a center of excellence to a full-fledged business with ambitious goals in a competitive talent market. One of GE Digital’s initial focus areas was strategic talent planning linked to learning and recruiting; the unit gathered fresh data in a rigorous process. This was combined with other GE people data to assemble a data set of more than 6,000,000 data points to use in a variety of talent decisions.

GE Digital has been able to complete robust talent planning by leveraging detailed information on what success looks like in terms of skill level, number, and location and by using predictive modeling to identify gaps. The organization’s strategies include recruiting as well as targeted training (when recruitment will not be able to meet needs), and these data have also informed acquisition strategies to help acquire specialized talent.

GE Digital has also developed a strong link between talent planning and learning. It uses data analysis and predictive models to support organizational design to inform hiring practices, to identify reskilling needs, and to refashion leadership development programs—all areas of future focus for the GE Digital team.

The success of analytics comes down to measuring the value of people to an organization—and analytics is key to unlocking that value.

The most critical success factors have been business involvement and employee transparency. The business has been instrumental in defining key capabilities and identifying learning requirements. Employees now understand the critical skills required for success in the organization and have been given tools to identify gaps and strengths as well as to develop needed skills, all of which have been positively received.10

Where companies can start

  • Stay focused on business priorities: Avoid the problem of spending a great deal of time on a problem only to find later that it is not on the CEO’s agenda. Start with problems the CEO or senior business leaders care about, such as sales productivity, product quality, risk, growth, or customer retention. Spend time where the company makes money, and people analytics projects will rapidly pay for themselves.
  • Build a single people analytics team: To embark on this effort, companies must recruit the right talent and/or integrate disparate analytics efforts—that is, employee engagement, recruitment analytics, learning analytics, compensation analytics, and workforce planning. This is a key ingredient of successful analytics projects.
  • Build a team that can consult: Remember that building a model alone will not solve a business problem. The analytics team should include people who also serve as a business partner or consultant, so they can engage directly with the business and help apply the findings to real interventions or management changes.
  • Leverage analytics skills outside HR: Enlist the support of IT, marketing analytics, and other analytics teams in the company. Many of them are ready to join in this exciting effort.
  • Join an external, industry-specific people analytics working group: There are now dozens of places HR professionals can meet with other companies to learn and explore this area.
  • Explore new technologies: There are many tools in the market to explore. Teams should learn about new analytics vendors to find the right tapestry of technologies.
  • Invest in cleaning data: Do not let data management be your biggest barrier. The highest value in analytics comes after the company is running an integrated, valid, and reliable database. This effort may take time and involve IT, but it pays off.
  • Focus on security, privacy, and anonymity: Many leading organizations define security policies as part of their people analytics governance early in the process. HR departments are well-served to understand the complex issues surrounding data security, privacy, and identity protection.

Bottom line

Companies are no longer “stuck in neutral” in their deployment of people analytics. As analytics moves into the corporate mainstream, organizations that are still in the early stages of adopting technology and building teams with data skills risk being left behind.

In the not-too-distant future, it will become impossible to make any HR decisions without analytics. Indeed, analytics capabilities will be a fundamental requirement for the effective HR business partner.

Originally published at Deloitte Insights

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