Harvard Business Review: Better People Analytics

Better People Analytics

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

Predicting students' happiness from physiology, phone, mobility, and behavioral data
Natasha Jaques. 2015. “Predicting students' happiness from physiology, phone, mobility, and behavioral data”. Publisher's VersionAbstract

In order to model students' happiness, we apply machine learning methods to data collected from undergrad students monitored over the course of one month each. The data collected include physiological signals, location, smartphone logs, and survey responses to behavioral questions. Each day, participants reported their wellbeing on measures including stress, health, and happiness. Because of the relationship between happiness and depression, modeling happiness may help us to detect individuals who are at risk of depression and guide interventions to help them. We are also interested in how behavioral factors (such as sleep and social activity) affect happiness positively and negatively. A variety of machine learning and feature selection techniques are compared, including Gaussian Mixture Models and ensemble classification. We achieve 70% classification accuracy of self-reported happiness on held-out test data.

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More Popular Press

The new fast.ai research datasets collection, on AWS Open Data
Jeremy Howard and Jed Sundwall. 10/16/2018. “The new fast.ai research datasets collection, on AWS Open Data.” fast.ai. Publisher's VersionAbstract

In machine learning and deep learning we can’t do anything without data. So the people that create datasets for us to train our models are the (often under-appreciated) heroes. Some of the most useful and important datasets are those that become important “academic baselines”; that is, datasets that are widely studied by researchers and used to compare algorithmic changes. Some of these become household names (at least, among households that train models!), such as MNISTCIFAR 10, and Imagenet.

We all owe a debt of gratitude to those kind folks who have made datasets available for the research community. So fast.ai and the AWS Public Dataset Program have teamed up to try to give back a little: we’ve made some of the most important of these datasets available in a single place, using standard formats, on reliable and fast infrastructure. For a full list and links see the fast.ai datasets page.

fast.ai uses these datasets in the Deep Learning for Coders courses, because they provide great examples of the kind of data that students are likely to encounter, and the academic literature has many examples of model results using these datasets which students can compare their work to. If you use any of these datasets in your research, please show your gratitude by citing the original paper (we’ve provided the appropriate citation link below for each), and if you use them as part of a commercial or educational project, consider adding a note of thanks and a link to the dataset.

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A.I. as Talent Scout: Unorthodox Hires, and Maybe Lower Pay
Noam Scheiber. 12/6/2018. “A.I. as Talent Scout: Unorthodox Hires, and Maybe Lower Pay.” The New York Times. Publisher's VersionAbstract

One day this fall, Ashutosh Garg, the chief executive of a recruiting service called Eightfold.ai, turned up a résumé that piqued his interest.

It belonged to a prospective data scientist, someone who unearths patterns in data to help businesses make decisions, like how to target ads. But curiously, the résumé featured the term “data science” nowhere.

Instead, the résumé belonged to an analyst at Barclays who had done graduate work in physics at the University of California, Los Angeles. Though his profile on the social network LinkedIn indicated that he had never worked as a data scientist, Eightfold’s software flagged him as a good fit. He was similar in certain key ways, like his math and computer chops, to four actual data scientists whom Mr. Garg had instructed the software to consider as a model.

The idea is not to focus on job titles, but “what skills they have,” Mr. Garg said. “You’re really looking for people who have not done it, but can do it.”

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Dodgers, Brewers show how analytics is changing baseball
Bradford Doolittle. 10/19/2018. “Dodgers, Brewers show how analytics is changing baseball.” ESPN. Publisher's VersionAbstract

 You want to know which teams are at the forefront of analytics? Just look around at the teams still playing.

Once upon a time, there was the Oakland Athletics and a sacred tome called "Moneyball." It was about baseball teams winning with statistics. Only it wasn't about that at all. It was about market inefficiency. Then John Henry bought the Boston Red Sox, hired Bill James, made Theo Epstein his general manager, and Moneyball spread to a big market.

We're several iterations past all of that. Things move fast in technology, so fast it can even carry a tradition-based industry like baseball into the digital age. These days, every team is playing Moneyball. All of them, as in 30 for 30.

"At this point, I think everyone assumes that their counterpart is smart," Brewers general manager David Stearns said. "And everyone is doing what they can do to unearth competitive advantages." To call it Moneyball is not right, either. Michael Lewis is still turning out ground-breaking work, but to fully capture what is happening in big league front offices, circa 2018, the next inside look at analytics and baseball would need to be authored by someone like the late Stephen Hawking. It's hard to say what you'd call it. "The Singularity" has already been taken.

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Artificial Intelligence's 'Black Box' Is Nothing to Fear
Vijay Pande. 1/25/2018. “Artificial Intelligence's 'Black Box' Is Nothing to Fear.” The New York Times. Publisher's VersionAbstract

Alongside the excitement and hype about our growing reliance on artificial intelligence, there’s fear about the way the technology works. A recent MIT Technology Review article titled “The Dark Secret at the Heart of AI” warned: “No one really knows how the most advanced algorithms do what they do. That could be a problem.” Thanks to this uncertainty and lack of accountability, a report by the AI Now Instituterecommended that public agencies responsible for criminal justice, health care, welfare and education shouldn’t use such technology.

Given these types of concerns, the unseeable space between where data goes in and answers come out is often referred to as a “black box” — seemingly a reference to the hardy (and in fact orange, not black) data recorders mandated on aircraft and often examined after accidents. In the context of A.I., the term more broadly suggests an image of being in the “dark” about how the technology works: We put in and provide the data and models and architectures, and then computers provide us answers while continuing to learn on their own, in a way that’s seemingly impossible — and certainly too complicated — for us to understand.

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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

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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The Performance Management Revolution
Peter Cappelli and Anna Tavis. 10/2016. “The Performance Management Revolution.” Harvard Business Review. Publisher's VersionAbstract

When Brian Jensen told his audience of HR executives that Colorcon wasn’t bothering with annual reviews anymore, they were appalled. This was in 2002, during his tenure as the drugmaker’s head of global human resources. In his presentation at the Wharton School, Jensen explained that Colorcon had found a more effective way of reinforcing desired behaviors and managing performance: Supervisors were giving people instant feedback, tying it to individuals’ own goals, and handing out small weekly bonuses to employees they saw doing good things.

Back then the idea of abandoning the traditional appraisal process—and all that followed from it—seemed heretical. But now, by some estimates, more than one-third of U.S. companies are doing just that. From Silicon Valley to New York, and in offices across the world, firms are replacing annual reviews with frequent, informal check-ins between managers and employees.

How We Got Here

Historical and economic context has played a large role in the evolution of performance management over the decades. When human capital was plentiful, the focus was on which people to let go, which to keep, and which to reward—and for those purposes, traditional appraisals (with their emphasis on individual accountability) worked pretty well. But when talent was in shorter supply, as it is now, developing people became a greater concern—and organizations had to find new ways of meeting that need.

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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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