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Talent analytics in practice

5 years ago


Analytics is an exciting and fast-growing area in human resources, but many companies are lagging. How can they address this game-changing area of HR to move quickly and methodically into the future?

HR is evolving into a data-driven function, with the focus shifting from simply reporting data to enabling the business to make informed talent decisions, predict employee performance, and conduct advanced workforce planning.
While 78 percent of large companies (with 10,000 or more employees) rated HR and talent analytics as “urgent” or “important,” enough to place analytics among the top three most urgent trends, 45 percent of the same companies rated themselves “not ready” when assessing their readiness in HR analytics—among the lowest readiness rankings for any of the 12 global trends. Only 7 percent of large companies rated their organizations as having “strong” HR data analytics capabilities today.
Companies that successfully leverage analytics and big data will be positioned to outperform their peers in executing their talent strategies.
At a time when big data is becoming a mainstream strategy in many business functions, HR is playing catch-up. Right now, 86 percent of companies report no analytics capability in the HR function, compared to 81 percent of companies that utilize analytics in finance, 77 percent in operations, 58 percent in sales, and 56 percent in marketing.1

The good news is that 57 percent of HR teams increased their investment in measurement and analytics in 2013.2 Companies that are ahead of the game in this area are doubling their improvements in recruiting, tripling their leadership development capabilities, and enjoying 30 percent higher stock prices than their peers.3

Today’s focus on HR analytics is not new. Companies have been trying to understand workforce data since the early 1900s. The evolving discipline of talent analytics, however, combines workforce data with business data to help companies make better business decisions about people. Critical questions—such as whom to hire, how to manage people, and what drives performance, retention, and customers—can now be understood statistically and answered with data, not just opinion or experience.

Despite understanding the importance of HR analytics, respondents to our survey are largely unprepared to meet this challenge. Companies in major industrialized nations, such as Japan, Germany, and the United Kingdom report that they are especially behind the curve (figure 1).

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The key leap from talk to action
Despite the powerful improvements analytics can deliver, most companies have yet to convert these capabilities into action. While 14 percent of companies now have some form of analytics capabilities, more than 60 percent are still stuck with a disorganized set of HR systems and no clear way to make meaningful data-driven decisions.4

This may be one reason why at least nine in ten respondents in our survey rate their companies as “weak” or just “adequate” when judging their current talent and HR analytics capabilities. Organizations rate themselves poorly when using HR data to predict workforce performance and improvement, with more than two-thirds (67 percent) calling this capability “weak” (figure 2).

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Aware of their weaknesses, nearly half (48 percent) of global respondents are actively developing or planning to move ahead with talent and HR analytics capabilities (figure 3).

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In 2014, the focus on big data in business will challenge HR leaders to build a talent analytics team, bring together multi-disciplinary skills, and develop a long-range plan to “datafy” HR.5

A transition of this magnitude cannot happen overnight, but more than 60 percent of companies are putting plans in place now.6 Examples of high-value solutions include:

Understanding the characteristics of high-performing salespeople to better select and attract leading candidates
Identifying work-related factors that correlate to fraud and accidents, enabling managers to dramatically reduce loss by focusing on well-known patterns
Setting up an internal platform for veteran employees to find new positions within a firm by matching skills with jobs
Creating analytics models that understand and predict turnover so managers can more rapidly change work conditions or behavior to keep top people from leaving
Understanding the impact of pay increases in detail to make more scientific decisions about where to invest to maximize performance.

Successful talent analytics programs require focused investment, dedicated cross-functional teams, and strong partnerships between HR, IT, and business operations. HR should take a leadership role by embracing this positive disruption—an opportunity to bring together different parts of the business to solve problems and drive business results.

A key insight provided by talent analytics is the ability to link business goals directly to talent strategies. Rather than focusing on HR spending and measuring HR metrics alone, talent analytics today has the power to analyze the contribution people make to business outcomes across the board—from sales and customer service to accident reduction and quality improvement.


A global pharmaceutical company facing an extremely competitive talent market in China understood it had to reduce workforce turnover to meet its growth targets. It embarked on a predictive analytics effort to improve retention, particularly among its sales force.

Using data from the previous three years, the company developed and implemented a model to provide predictive insights on critical sales roles for the company and pivot points that influenced retention. The model enabled prediction down to the level of the individual employee, identifying which variables were strong predictors of retention and turnover, and informing the development of focused retention strategies. For example, despite an intensely competitive talent market, compensation was not the primary driver of turnover.

Using this highly data-driven model to improve the targeting and effectiveness of its retention strategy, the company has been able to use the analysis to take targeted action to improve retention. The company was able to focus its investments on the retention initiatives that offer the highest value and impact.


For many companies, the transition from data reporting to data analytics is a leap into the unknown. HR teams question if they have the skills and understanding to put this function together. Industrial and organizational psychologists, statisticians, and data analysts may all be needed to help HR build this new capability.

Potential starting points include:

Look for skilled analysts to lead the team: Having a skilled analyst on your team isn’t the same as having a skilled analyst lead the team. That said, depending on the maturity of the organization, a skilled salesperson may be better equipped at leading the team, given the amount of convincing the organizations (both HR and business) will likely require on the topic.
Add a couple of outlier profiles to the analytics team, such as econometricians, demographers, computer/applied scientists, and business intelligence specialists. They usually bring in a different view to the challenges at hand while being hands-on with numerical analyses, fact-finding, and generating insights from data.
Create a community of practice where intrinsically interested professionals can share experiences and best practices. They will become your best ambassadors, and establishing a community of practice ties in to the overall action of raising visibility for fact-based decision making through analytics.
Equip analysts with HR technology, performance consulting, visualization, and project management skills. Build a close relationship between HR and IT; HR organizations working in predictive analytics often have an IT specialist on the HR staff.
Identify specific business challenges to be addressed: Use data to meet visible business challenges by working with business units to agree on deliverables, reports, and expectations. Don’t just try to analyze data; start by focusing on business problems.
Build capabilities by experimenting: Choose a business problem, bring people from different functions together, consider which types of data might help solve that problem, and find the techniques that might help the team analyze the data and devise solutions.
Make analytics user-friendly for the entire organization through the use of tools such as dashboards in order to provide maximum value to business units.
Do not let the perfect be the enemy of the good: Recognize that without quality data, analytics projects will likely fail; at the same time, insisting on 100 percent data quality means a project will likely never begin. Data quality remains a challenge for all functions in analytics; it is valuable to leverage the data that does exist to start improving people-related decisions today.
Over a decade ago, author Michael Lewis’s book Moneyball7 told the story of how people decisions in professional sports could be dramatically improved by using numbers and analysis, rather than on the basis of subjective opinion alone—a conclusion that has been widely accepted in business. In the 2014 Global Human Capital Trends survey, companies indicated that they understood the importance of building their HR and talent analytics capabilities, but also revealed significant gaps in their current readiness and capabilities. It can take three to five years to build a strong talent analytics function and the same length of time, or longer, to develop a mindset and culture in which people make decisions based on data and not just instinct. It is important to start laying the groundwork. In 2014, companies should take action to build HR and talent analytics capabilities, to seek out and conduct pilot projects focused on critical business and talent problems, and to invest in developing the analytics capabilities to drive the HR function going forward. Recognizing HR’s reputation as a profession and function that shies away from numbers and data, it is critical to move from talk to action.

Article originally appeared here.


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