Harvard Business Review: Better People Analytics

Better People Analytics

Artificial Intelligence and Ethics

Artificial Intelligence and Ethics

How the Eagles Followed the Numbers to the Super Bowl

How the Eagles Followed the Numbers to the Super Bowl

How People Analytics Can Change Process, Culture, and Strategy

How People Analytics Can Change Process, Culture, and Strategy

University Took Uncommonly Close Look at Student-Conduct Data

Rutgers

Dodgers, Brewers show how analytics is changing baseball

Baseball

Little Privacy in the Workplace of the Future

Little Privacy in the Workplace of the Future

Google's Culture of Self-Surveying

Google

The Resume of the Future

The Resume of the Future

More Academic Articles

Small Cues Change Savings Choices
James J.Choi, Emily Haisley, Jennifer Kurkoski, and Cade Massey. 2017. “Small Cues Change Savings Choices.” Behavioral Evidence Hub. Publisher's VersionAbstract

PROJECT SUMMARY

Researchers tested the effects of including cues, anchors, and savings goals in a company email encouraging employee contributions to their 401(k).

IMPACT

Researchers found that providing high contribution rate or savings goal examples, or highlighting high savings thresholds created by the 401(k) plan rules, increased 401(k) contribution rates by 1-2% of income per pay period.

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Overcoming Algorithm Aversion: People Will Use Imperfect Algorithms If They Can (Even Slightly) Modify Them
Berkeley Dietvorst, Joseph P. Simmons, and Cade Massey. 6/13/2015. “Overcoming Algorithm Aversion: People Will Use Imperfect Algorithms If They Can (Even Slightly) Modify Them.” SSRN. Publisher's VersionAbstract
Although evidence-based algorithms consistently outperform human forecasters, people often fail to use them after learning that they are imperfect, a phenomenon known as algorithm aversion. In this paper, we present three studies investigating how to reduce algorithm aversion. In incentivized forecasting tasks, participants chose between using their own forecasts or those of an algorithm that was built by experts. Participants were considerably more likely to choose to use an imperfect algorithm when they could modify its forecasts, and they performed better as a result. Notably, the preference for modifiable algorithms held even when participants were severely restricted in the modifications they could make (Studies 1-3). In fact, our results suggest that participants’ preference for modifiable algorithms was indicative of a desire for some control over the forecasting outcome, and not for a desire for greater control over the forecasting outcome, as participants’ preference for modifiable algorithms was relatively insensitive to the magnitude of the modifications they were able to make (Study 2). Additionally, we found that giving participants the freedom to modify an imperfect algorithm made them feel more satisfied with the forecasting process, more likely to believe that the algorithm was superior, and more likely to choose to use an algorithm to make subsequent forecasts (Study 3). This research suggests that one can reduce algorithm aversion by giving people some control - even a slight amount - over an imperfect algorithm’s forecast.
The Bright Side of Being Prosocial at Work, and the Dark Side, Too
Mark C. Bolino and Adam Grant. 2016. “The Bright Side of Being Prosocial at Work, and the Dark Side, Too.” The Academy of Management Annals. Publisher's VersionAbstract
More than a quarter century ago, organizational scholars began to explore the implications of prosociality in organizations. Three interrelated streams have emerged from this work, which focus on prosocial motives (the desire to benefit others or expend effort out of concern for others), prosocial behaviors (acts that promote/protect the welfare of individuals, groups, or organizations), and prosocial impact (the experience of making a positive difference in the lives of others through one’s work). Prior studies have highlighted the importance of prosocial motives, behaviors, and impact, and have enhanced our understanding of each of them. However, there has been little effort to systematically review and integrate these related lines of work in a way that furthers our understanding of prosociality in organizations. In this article, we provide an overview of the current state of the literature, highlight key findings, identify major research themes, and address important controversies and debates. We call for an expanded view of prosocial behavior and a sharper focus on the costs and unintended consequences of prosocial phenomena. We conclude by suggesting a number of avenues for future research that will address unanswered questions and should provide a more complete understanding of prosociality in the workplace.
Shifts and Ladders: Comparing the Role of Internal and External Mobility in Managerial Careers
Matthew Bidwell and Ethan Mollick. 10/5/2015. “Shifts and Ladders: Comparing the Role of Internal and External Mobility in Managerial Careers.” Organization Science, 26, 6, Pp. 1553-1804. Publisher's VersionAbstract
Employees can build their careers either by moving into a new job within their current organization or else by moving to a different organization. We use matching perspectives on job mobility to develop predictions about the different roles that those internal and external moves will play within careers. Using data on the careers of master of business administration alumni, we show how internal and external mobility are associated with very different rewards: upward progression into a job with greater responsibilities is much more likely to happen through internal mobility than external mobility; yet despite this difference, external moves offer similar increases in pay to internal, as employers seek to attract external hires. Consistent with our arguments, we also show that the pay increases associated with external moves are lower when the moves take place for reasons other than career advancement, such as following a layoff or when moving into a different kind of work. Despite growing interest in boundaryless careers, our findings indicate that internal and external mobility play very different roles in executives’ careers, with upward mobility still happening overwhelmingly within organizations.
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More Popular Press

There will be little privacy in the workplace of the future
3/28/2018. “There will be little privacy in the workplace of the future”.Abstract

Walk up a set of steep stairs next to a vegan Chinese restaurant in Palo Alto in Silicon Valley, and you will see the future of work, or at least one version of it. This is the local office of Humanyze, a firm that provides “people analytics”. It counts several Fortune 500 companies among its clients (though it will not say who they are). Its employees mill around an office full of sunlight and computers, as well as beacons that track their location and interactions. Everyone is wearing an ID badge the size of a credit card and the depth of a book of matches. It contains a microphone that picks up whether they are talking to one another; Bluetooth and infrared sensors to monitor where they are; and an accelerometer to record when they move.

“Every aspect of business is becoming more data-driven. There’s no reason the people side of business shouldn’t be the same,” says Ben Waber, Humanyze’s boss. The company’s staff are treated much the same way as its clients. Data from their employees’ badges are integrated with information from their e-mail and calendars to form a full picture of how they spend their time at work. Clients get to see only team-level statistics, but Humanyze’s employees can look at their own data, which include metrics such as time spent with people of the same sex, activity levels and the ratio of time spent speaking versus listening.

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How Can Organizational Network Analysis (ONA) Help Improve Company Performance?
Talha Oz. 2018. “How Can Organizational Network Analysis (ONA) Help Improve Company Performance?” Humanyze. Publisher's VersionAbstract

Organizational Network Analysis (ONA) is the set of scientific methods and theories to help understand interactions within an organization. It helps executives and managers to intervene at critical times, increase performance, and reduce costs.

There’s increasing pressure on executives to drive sustained, long-term growth. Yet, they lack the information they need to make informed business decisions and successfully initiate change. As organizations restructure departments to have fewer hierarchical levels, work increasingly occurs between social networks, rather than though prescribed reporting structures. Research shows that employees look to their networks to find information and to solve problems. Communication no longer flows solely from senior management to individual contributors – information moves through social networks, between colleagues and different teams. Organizations can analyze social networks to assess how information flows between teams and to intervene at critical times in order to improve how work gets done.

Key takeaways:

– Explore the benefits of supporting organizational networks
– How network analysis can impact company performance
– How to interpret network graphs
– Business applications of ONA  for human resources, business processes, and corporate real estate decisions

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Meet Your New Boss: An Algorithm

Meet Your New Boss: An Algorithm

A.I. as Talent Scout: Unorthodox Hires, and Maybe Lower Pay

A.I. as Talent Scout: Unorthodox Hires, and Maybe Lower Pay

The Performance Management Revolution

Performance Management

Amazon scrapped 'sexist AI' tool

Amazon AI

Making it easier to discover datasets

Google AI

HR Must Make People Analytics More User-Friendly

HR Must Make People Analytics More User-Friendly

More Harvard Business Review

Better People Analytics
Paul Leonardi and Noshir Contractor. 11/1/2018. “Better People Analytics.” Harvard Business Review. Publisher's VersionAbstract

"We have charts and graphs to back us up. So f*** off.” New hires in Google’s people analytics department began receiving a laptop sticker with that slogan a few years ago, when the group probably felt it needed to defend its work. Back then people analytics—using statistical insights from employee data to make talent management decisions—was still a provocative idea with plenty of skeptics who feared it might lead companies to reduce individuals to numbers. HR collected data on workers, but the notion that it could be actively mined to understand and manage them was novel—and suspect.

Today there’s no need for stickers. More than 70% of companies now say they consider people analytics to be a high priority. The field even has celebrated case studies, like Google’s Project Oxygen, which uncovered the practices of the tech giant’s best managers and then used them in coaching sessions to improve the work of low performers. Other examples, such as Dell’s experiments with increasing the success of its sales force, also point to the power of people analytics.

But hype, as it often does, has outpaced reality. The truth is, people analytics has made only modest progress over the past decade. A survey by Tata Consultancy Services found that just 5% of big-data investments go to HR, the group that typically manages people analytics. And a recent study by Deloitte showed that although people analytics has become mainstream, only 9% of companies believe they have a good understanding of which talent dimensions drive performance in their organizations.

What gives? If, as the sticker says, people analytics teams have charts and graphs to back them up, why haven’t results followed? We believe it’s because most rely on a narrow approach to data analysis: They use data only about individual people, when data about the interplay among people is equally or more important.

People’s interactions are the focus of an emerging discipline we call relational analytics. By incorporating it into their people analytics strategies, companies can better identify employees who are capable of helping them achieve their goals, whether for increased innovation, influence, or efficiency. Firms will also gain insight into which key players they can’t afford to lose and where silos exist in their organizations.

Most people analytics teams rely on a narrow approach to data analysis.

Fortunately, the raw material for relational analytics already exists in companies. It’s the data created by e-mail exchanges, chats, and file transfers—the digital exhaust of a company. By mining it, firms can build good relational analytics models.

In this article we present a framework for understanding and applying relational analytics. And we have the charts and graphs to back us up.

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HR Must Make People Analytics More User-Friendly
John Boudreau. 6/16/2017. “HR Must Make People Analytics More User-Friendly.” Harvard Business Review. Publisher's VersionAbstract

Managing HR-related data is critical to any organization’s success. And yet progress in HR analytics has been glacially slow. Consulting firms in the U.S. and Europe lament the slow progress. But a Harvard Business Review analytics study of 230 executives suggests a stunning rate of anticipated progress: 15% said they use “predictive analytics based on HR data and data from other sources within or outside the organization,” while 48% predicted they would be doing so in two years. The reality seems less impressive, as a global IBM survey of more than 1,700 CEOs found that 71% identified human capital as a key source of competitive advantage, yet a global study by Tata Consultancy Services showed that only 5% of big-data investments were in human resources.

Recently, my colleague Wayne Cascio and I took up the question of why HR analytics progress has been so slow despite many decades of research and practical tool building, an exponential increase in available HR data, and consistent evidence that improved HR and talent management leads to stronger organizational performance. Our article in the Journal of Organizational Effectiveness: People and Performance discusses factors that can effectively “push” HR measures and analysis to audiences in a more impactful way, as well as factors that can effectively lead others to “pull” that data for analysis throughout the organization.

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Why "Many-Model-Thinkers" Make Better Decisions
Scott E. Page. 11/19/2018. “Why "Many-Model-Thinkers" Make Better Decisions.” Harvard Business Review. Publisher's VersionAbstract

Without models, making sense of data is hard. Data helps describe reality, albeit imperfectly. On its own, though, data can’t recommend one decision over another. If you notice that your best-performing teams are also your most diverse, that may be interesting. But to turn that data point into insight, you need to plug it into some model of the world — for instance, you may hypothesize that having a greater variety of perspectives on a team leads to better decision-making. Your hypothesis represents a model of the world.

Though single models can perform well, ensembles of models work even better. That is why the best thinkers, the most accurate predictors, and the most effective design teams use ensembles of models. They are what I call, many-model thinkers.

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