As long as work has been done, it has been analyzed. Many credit Frederick Taylor with first applying scientific methods to the workforce in the late 1800s, but using data and analytics to improve productivity is an idea at least as old as the Egyptian pyramids. Their construction required tens of thousands of workers whose efforts, it is believed, were documented and analyzed by scribes. From ancient times to today, workforce analytics have dictated how we work.

What the scribe was to the Egypt of 2500 BC the data analyst is to global business today. As digital technologies remake industry after industry, the data exhaust created supports new ways of measuring worker productivity. Breakthroughs in machine learning, social network analysis, human listening systems, and other areas of technology have coincided with important improvements in our understanding of human behavior. New insights into areas such as unconscious and conscious bias and decision architecture have combined with new technological tools to help build people analytics systems that are sophisticated enough to harness the full potential of an organization’s talent. Today, algorithms screen recruits, perform background testing, and track levels of diversity, equity, and inclusion; they evaluate employees’ skills and help train them. Computer models forecast workforce needs, match candidates to jobs, evaluate how efficiently we work, help determine fair compensation, and track talent goals.

When implemented well, these tools make both companies and talent more competitive in the marketplace (see Figure 1).

Bain & Company research finds a 40% disparity between the productivity of the best organizations and that of the rest. No wonder investment in talent-focused analytics and software companies has exploded in recent years. According to CB Insights, between 2010 and 2020, there were more than 4,000 talent analytics and software deals, netting more than $20 billion in capital.

With so many products available, it can be a challenge to track and evaluate all the options. Even with the new tools, improving productivity remains a stubborn challenge for many. Operating executives and those charged with broader corporate transformation continue to struggle for the kind of visibility that would make data truly useful.

Covid-19 has further complicated this challenge. The pandemic upended the way many companies operate, and it is accelerating the shift from traditional organizational systems centered on functional processes to systems that also include cross-functional Agile teams. Unfortunately, current human resources systems typically do not capture rich information about cross-functional team staffing or performance. People analytics systems will need to adapt to these new ways of working.

How to focus talent analytics

Harnessing the possibilities of today’s sophisticated people analytics can be a challenge, but by focusing analytics on their most important HR goals, companies are beginning to build systems that could truly help them meet their full potential.

Bain research shows that digital natives typically invest two to four times as much in analytics talent as their competitors. According to Dawn Klinghoffer, Microsoft’s head of people analytics, as of 2018, 70% of the Fortune 1000 had invested in people analytics; she expects that number to hit 100%. Microsoft started developing its people analytics function 15 years ago, and Klinghoffer attributes its rapid growth since to the successful work her group has done in collaboration with other departments aimed at solving their key challenges.

For HR, analytics can help:

• increase productivity by balancing efficiency with engagement;
• address the biases that limit corporate potential; and
• measure the things that truly matter to your business, not just what is possible.

Balance efficiency with engagement

When Microsoft’s analytics group recently worked with its sales excellence team on a project to develop insight into effective sales practices, the two groups deployed workplace analytics to capture those best practices. They also layered in an employee engagement survey, believing that it is not enough to look only at what will make someone productive; it’s vital to understand how engaged they are as well.

Balancing the push for efficiency with a sincere focus on employee engagement is the best way to create long-term value. It can sometimes seem, however, that efficiency trumps engagement, particularly at this moment, with so many organizations and industries hard hit by the pandemic. According to Gallup, only 36% of employees feel truly engaged today.

Failing to hit the proper balance between efficiency and engagement can carry a high cost. Consider the experience of many gig platforms. Deeply instrumented workplaces designed to maximize efficiency, some have nevertheless quickly lost business and market share after failing to listen and respond to the concerns of their workers. Platforms that once had near monopolies have seen as much as 70% of their workforce join other platforms out of frustration over the terms and conditions of their work. Only after the defections did those platforms begin to try actively listening to their workers in an attempt to earn back their loyalty. Systems that tap into employee sentiment and priorities can act as a counterbalance to a single-minded focus on efficiency and help avoid this type of scenario.

Fight bias with bias

No matter the size of their company or industry, virtually every organization has historic data that contains biases, and there is a real danger of algorithmically reinforcing those biases since this data does not fully reflect the diversity of the community in which the organization operates today. This is costly both to the communities that go untapped and the organization overall. Diverse teams produce better results, and diverse companies are better for them.

Recognizing the need for and benefits of better representation, many companies are now beginning the hard work of addressing unconscious bias. It must happen at every level and every point of the process, but it starts with understanding the issue and then consciously building in counter-bias. Then, insights gathered from underrepresented communities can be used to help redesign each step along the career path, from recruiting to hiring to talent development and beyond, with talent analytics supporting that work.

The process requires commitment. In his book Trailblazer, Salesforce CEO Marc Benioff tells the story of the repeated efforts required to ensure that people of all genders are paid equally for equal work. In March 2015, when two female executives who suspected that women were paid less than men for the same work asked Benioff for an audit of the company’s pay practices, he agreed but was skeptical. Benioff had already spent years working to expand the number of women at all levels and was a bit put off by the suggestion that there could be an ongoing problem, he wrote. The audit, which analyzed all levels of the company and each of the company’s 17,000 employees at the time, took a few months to complete. It found significant pay gaps throughout the company. Over the next two years, subsequent reviews would discover further inequity and require additional pay equity adjustments, many for employees who had been brought on through corporate acquisitions. In time, the company created job codes and standards to make sure everyone performing similar work is similarly compensated from the start. Merit increases, bonuses, stock grants, and promotions are also reviewed for disparities. What Benioff thought would amount to nothing turned into a multiyear process of continuous monitoring and improvement.

Equal pay is, of course, an essential component of attracting and retaining a diverse workforce, but there are other elements critical to true equity and inclusion. An employee’s sense of belonging, feeling supported, and his or her trust in the fairness and effectiveness of a company’s systems and processes all contribute to a sense of true inclusion (see the Bain Brief “Belonging, Support, and Trust: Racial Equity at Work”). Underrepresented groups often struggle to be included unless explicit efforts are made to overcome natural barriers in the workplace.

People analytics can shed light on the challenges faced on all three dimensions—belonging, support, and trust—and help prioritize specific interventions. Internal engagement tools, social listening, and web scraping software can, for example, help flesh out sentiment differences by populations and subsegments. Analytics can help measure networking effectiveness and connectivity across populations, keep track of leaders’ success developing diverse talent on their teams and within their functions, and compare leaders to one another on those measures.

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Anchor decisions about people in facts

At Bain & Company, our ability to attract and retain talent is the single greatest contributor to our business success. We seek to identify and integrate valuable data into every major talent decision made along an employee’s life cycle, from hiring and evaluations to compensation and promotion.

One way we do this is by using analytics to make choosing good candidates easier and to help manage a rapidly increasing pace of hiring at our offices around the world. Over the past decade, the number of people Bain employs has roughly tripled, as has the discrete number of job families—that is, groups of positions with related knowledge and background requirements—for which we recruit. In 2020, we received more than 120,000 résumés. To make time for more live interaction with candidates, we designed a tool to automate résumé reviews and annotation so that consultants involved in recruiting can rapidly sort through the hundreds of résumés they each personally review. The tool doesn’t just save consultants’ time, however; it also ensures that an objective standard is applied to all résumés and reduces issues such as a reader’s similarity bias (or tendency to connect with those who share similar interests, experiences, and backgrounds).

As useful as résumé analytics and other assessments have been, a personal interview remains both an invaluable way for us to assess a candidate’s potential to thrive at Bain and a crucial chance to explain and sell all that we have to offer. Because the written record on any candidate is incomplete, we are training our interviewers to recognize a person who possesses the intangible traits to be successful at the firm. Interviewers are increasingly skilled at assessing important traits that can be hard to see in quantitative data, things like how good a candidate will be at taking feedback or how they will react when they realize they made an error that must be addressed.

Great candidates don’t always check all the discrete boxes of an automated system, so we are exploring ways to automate the process of recognizing the atypical backgrounds and characteristics of those with high professional potential and then spread that insight systematically across the firm. As part of this work, we are tracking people after they are hired and searching for the common traits, skills, or experiences among the most successful. Using this analysis, one office identified resilience as a trait that correlates with success at Bain. Resilience can be hard to articulate and capture in a piece of data, but we are incorporating it more explicitly and consistently into interviews.

By asking how we can use analytics to help capture our interviewers’ pattern recognition, we are trying to measure what really matters, not just what’s easy to gauge. Talent and HR analytics enrich and deepen our ability to make informed decisions, and we credit the balance of analytic and human factors with helping us build the highly engaged team and culture that has ranked No. 1 on Glassdoor’s Best Places to Work list five times, including in 2021.

Shaping the conversation

Whether your company is already using analytics to balance efficiency with engagement, address the biases that limit corporate potential, and measure what truly matters or is just starting out, here is a checklist of questions all organizations can use to help shape their important conversations on this topic:

• Which employee populations matter most to our performance?
• Where in our talent life cycle can analytics have the greatest effect? Which parts of the process are painfully manual and could benefit from these tools?
• How can we improve the data signals we are gathering on those life cycle steps or employee populations?
• How accurate are our recruiting assessments when we compare them to later performance, promotions, and engagement?
• How can we make better decisions and eliminate historic bias?
• How do specific coaches or talent developers perform with diverse populations?
• How can we use analytics to energize, not disempower, the workforce?
• What talent and operating models will deliver the greatest return on our investment in people analytics?