Top-selling author, Dan Pink; Nobel Laureate, Daniel Kahneman; Wharton professor, Adam Grant; Zappos CEO, Tony Hsieh; and Wharton Professor, Cade Massey were among the more than 20 speakers who met to talk about people analytics, described simply as “a data-driven approach to managing people at work.”
More than a technical or HR conference, this gathering focused on Leadership—and how leaders can make better decisions using human capital and other business data. The discussion covered topics ranging from decision-making, the purpose and scope of analytics, culture and values, teams, motivation, diversity, performance management, and perseverance.
It covered the intersection, or perhaps Millennials might say the mash-up, of economics, psychology, management practice, and sports. Here are just a few of the themes discussed.
1. People Analytics is about leaders making better people decisions with human capital and other business data.
Algorithms are cool, but they are no match for intuition and experience, the opening keynoters indicated. In a conversation with Dan Pink, Daniel Kahneman, known for his Nobel Memorial Prize in Economic Sciences (2002) and recent work in cognitive bias, talked about how difficult it is to make good decisions. Analytics should not replace human judgment, but supplement it, he suggested.
Pink posed the question of “how to achieve a balance of algorithm with intuition, judgment and experience?”
It’s not easy, as it turns out, because of how the brain is wired. “We jump to stories to create meaning,” Kahneman explained. “But organizations don’t think that way, they think more slowly.”
Error in human judgment can be traced to cognitive bias, Kahneman’s work suggests, as the mind has two distinct filters: System One is fast, intuitive, and emotional. System Two is slower, more deliberative, and more logical. Leaders need “algorithms which focus the mind” but also need training to understand the intuitive, fast processes, Kahneman said.
“Decision analysis is very threatening to the leaders of organizations,” Kahneman said, as most are accustomed to using intuitive decision processes (i.e., from the gut). Unfortunately, he explained, “We’re worst at making very big decisions because we have few opportunities to practice them.” Both Pink and Kahneman counseled the need to be systematic and compassionate and to avoid overconfidence in decision making. “There is a limit to how predictive performance is,” Kahneman said. “We underestimate the role of luck and inconsistency in human affairs.”
2. People analytics can be used to address inefficiencies in labor markets and talent shortages.
Can people analytics be used to rewire labor markets for economic good? In a discussion with Wharton Dean Geoffrey Garrett, Byron Auguste, managing director at Opportunity@Work and former Deputy Director of the White House National Economic Council, asserted that human capital—the most valuable asset on America’s economic balance sheet—is not realizing its full value and that current practices in hiring are undermining dynamic economic growth.
“A waste of talent is a market failure,” Auguste asserted. As example, he cited the number of people who can do the work but who are unable to get the job because of the overuse of credentials in costly and risk-averse corporate screening processes. Credentials and even experience, the data suggests, are poor ways to select talent.
“It is a question of what we incent people to do with the technology,” Auguste offered. “Analytics can be used to segment people [out]—or it can be used to develop people,” he said.
“[It] can explode like a bomb or be a rocket for growth.” Auguste continued. His work suggests more attention be paid to the demand side of the labor market—i.e., how employers use analytics tools to recruit, train, hire, and promote talent as means to both reduce unemployment, but also alleviate talent shortages.
3. People Analytics can be organized around what people need to be happy and purposeful.
“The future of work is self organization,” explained Zappos CEO, Tony Hsieh, in an interview with Adam Grant. Hsieh, who has managed his company as “an ongoing learning process and experiment,” reiterated the importance of purpose and cultural values in driving organizational success—but also the need for individuals to be aligned with corporate values and purpose. His research, for example, shows that companies with a higher sense of purpose outperformed those without by 400%.
Hsieh has made a practice of applying the science of happiness to corporate culture and purpose. His “holacracy” system of organizational governance is one in which authority and decision making are distributed through self-organizing teams rather than vested in a management hierarchy.
“The default future for companies is death, and you can choose to ignore the data or not,” Hsieh said citing the history of Fortune 500 performance. “Self organization works," said Hsieh. Among the top values Hsieh cited: entrepreneurial values that include questioning and curiosity.
4. Algorithms are cool and provide new ways to leverage technology and insight into old problems.
Yes, algorithms are cool. Let’s get to that. Modeling, statistical tools, and technology are key elements of people analytics. The student case competition, in partnership with Doctors Without Borders, showed how data modeling and analysis can solve retention, development and deployment issues. The inspiring competition matched analytics with passion for social purpose – a powerful combination.
In addition, academic research papers offered insights into how human capital data modeling can drive business value in areas such as culture, metrics, job design, and more.
A third area of interest was the start up showcase which featured new businesses—primarily technology driven—with people applications in such areas as culture assessment, organizational health, personality assessment, and other predictive analytics tools.
5. People Analytics can be used to drive better business outcomes in productivity, team performance, diversity, and leadership. This often means questioning basic assumptions with data.
Several speakers focused on how the systematic collection of data can contribute to understanding performance levers that drive business outcomes. But because new data can bring into question long-held assumptions, execution is not an easy task and may require attention to end results and stakeholder interests.
Brian Welle, Director of People Analytics at Google, has studied unconscious bias and teams, and stressed the need to “start with the end goal.” For executives, this means starting with a conversation about results. Welle’s research, for example, shows that “how a team works matters more than who is on the team.” Such elements as psychological safety, dependability, structure and clarity of goals, meaning, and impact can shape team performance more than the individuals on the team.
A panel on diversity also raised questions about how to frame diversity and the end goal of performance. Ester Bongennaar, who leads HR analytics for Shell, asked for example, “How do you tie performance to diversity and the different types of diversity?” She spoke about the challenges of delivering counterintuitive insights to HR leadership.
Another counterintuitive set of data was offered on “how to speak like a visionary.” Data challenges assumptions, offered panelist Noah Zanden, CEO of Quantified Communications. Visionaries focus on the present. They use clear and simple language. They bring people into the experience. It’s not about lofty goals and, well, “visions.” It’s about connecting to people’s own experience.
Data from another study by researcher Kate Glazebrook questioned industry’s overreliance on CVs (i.e., resumes) as a means to identify top candidates. Her research showed how assessment tools outperform CVs as a screening tool with virtually no relationship of CVs to applied scores of success in the job. Yet most organizations don’t have assessment tools in place for identifying candidates and continue to rely on CV-based applicant screening.
In short, data is just the start of the people analytics journey. There is a problem, there is a framing of the problem, there is research and insight, and then there is the complicated task of making the insight “move people” to decisions.
6. People analytics will shift resources—time and investment—to the areas that matter most to creating future performance.
For those stuck in analytics 101 (that would be retention analytics), a panel with Wharton Professor Peter Cappelli, one of the first to bring supply chain concepts to HR, suggested the need for a more holistic approach to what adds value in the employee supply chain. This means more attention to stages in the employee lifecycle such as development and enablement of performance (versus past performance calibration, where tremendous amounts of (duh) executive time have been focused).
The panel, a session on performance management, offered insights into how to align with changing business models and how to drive two key performance factors—employee development and performance feedback.
“After changes in our business model, we needed major changes and adjustments,” explained Adobe HR Director Megan Taylor, who described her performance management intervention (eliminating ratings and driving more feedback) as a “marathon.”
Love that analogy. It’s also about the marathon of performance analytics as a long distance run.
7. Perseverance, grit and focus lead to winning in sports and in People Analytics.
In a conversation with Wharton Professor Angela Duckworth), Olympic gold medalist and retired soccer star Abby Wambach was especially humble (Wambach was arrested on suspicion of DUI this month) but her humility and self reflection gave her a power equal to any of the other speakers. Wambach stressed the need for role flexibility and perseverance. And how to learn from mistakes.
“You have flashy star players and role players, and roles can change. It’s the number of role players that matters most,” explained Wambach, who described the need for development and mentoring of players as one of the most critical requirements of team performance.
And yet, it may be Duckworth, who’s research focuses on grit—a combination of passion and perseverance for a singularly important goal—who may have the best data about how to succeed in people analytics. Beyond the algorithms and analytical capabilities, her research suggests, that grit will be the hallmark of high achievers in this emerging domain of people analytics.
Organized by a team of students and Wharton Professors Adam Grant and Cade Massey, the Wharton People Analytics Conference offered more than 20 panelists and speakers (stay tuned for online postings). More information may be found online. To audit a free Wharton course on people analytics or to see more information on Wharton’s people analytics certification program with (the awesome) Cade Massey, simply go to Wharton to learn more.