Uber Technologies Inc. and other pioneers of the so-called gig economy became some of the world’s most valuable private companies by using apps and algorithms to hand out tasks to an army of self-employed workers. Now, established companies like Royal Dutch Shell PLC and General Electric Co. are adopting elements of that model for the full-time workforce.
Companies say the new tools make them more efficient and give employees more opportunities to do new kinds of work. But the software also is starting to take on management tasks that humans have long handled, such as scheduling and shepherding strategic projects. Researchers say the shift could lead to narrower roles for some managers and displace others.
During Jeff Immelt’s 16 years as CEO, GE radically changed its mix of businesses and its strategy.
Its focus—becoming a truly global, technology-driven industrial company that’s blazing the path for the internet of things—has had dramatic implications for the profile of its workforce. Currently, 50% of GE’s 300,000 employees have been with the company for five years or less, meaning that they may lack the personal networks needed to succeed and get ahead. The skills of GE’s workforce have been rapidly changing as well, largely because of the company’s ongoing transformation into a state-of-the-art digital industrial organization that excels at analytics. The good news is that GE has managed to attract thousands of digerati. The bad news is that they have little tolerance for the bureaucracy of a conventional multinational. As is the case with younger workers in general, they want to be in charge of their own careers and don’t want to depend solely on their bosses or HR to identify opportunities and figure out the training and experiences needed to pursue their professional goals.
What’s the solution to these challenges? GE hopes it’s HR analytics. “We need a set of complementary technologies that can take a company that’s in 180 countries around the world and make it small,” says James Gallman, who until recently was the GE executive responsible for people analytics and planning. The technologies he’s referring to are a set of self-service applications available to employees, leaders, and HR. All the apps are based on a generic matching algorithm built by data scientists at GE’s Global Research Center in conjunction with HR. “It’s GE’s version of Match.com,” quips Gallman. “It can take a person and match him or her to something else: online or conventional educational programs, another person, or a job.”
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.
Do you feel as if there’s always someone watching you at work?
You might be right: the way companies monitor employees has broadened beyond simply requiring workers to tap in and out of an office building. Advances in technology and a hunger for data have now created a market for devices that can measure workers’ movements, fitness and even sleep – all in the name of productivity.
Take Humanyze, for example, a start-up based in Boston, Massachusetts, which supplies companies with employee ID badges replete with inbuilt biometric measuring capabilities.
A plethora of tech within the badges tracks everything from movements and interactions around the office, to lengths of conversations, and even voice tone. CEO Ben Waber told Techworld earlier this year that microphones within the badges can process vocal information to detect whether a person dominates conversations, as well as tone, volume and speed of speech.
With these, the company aims to change the traditional role of management consultants in the workplace. According to Humanyze, these “people analytics,” can help measure everything from how often workers are disrupted, to the effectiveness of diversity and inclusion programmes.
Workforce analytics is the discovery, interpretation, and communication of meaningful patterns in workforce-related data to inform decision making and improve performance.
The human race’s quest for information is never-ending. Businesses and organizations are no exceptions. Business leaders continually seek out knowledge about their organizations to gain insights from all the data that exist. This is so they can make evidence-based decisions to improve their organization’s performance and gain competitive advantage in the marketplace.
The discipline called “analytics” exists to meet this need. Analytics concerning human resources, people and the workforce is known as workforce analytics, and that is the focus of the book, The Power of People. This book sets out how to establish, operate and lead workforce analytics to better serve organizational ambitions.
Previously in the ‘Demystifying People Analytics’ series I’ve written about where the team should sit (Part I), the skills and capabilities required to do people analytics (Part II) and the vital role of storytelling (Part III).
This time I’m going to provide examples of people analytics projects companies have undertaken to help drive both business and employee outcomes.
The five example projects outlined are spread across the employee lifecycle (workforce planning, talent acquisition, engagement, retention and compliance). Whilst the projects themselves are different, what they share in common is that they were all undertaken to help solve business challenges that were high priority for the organisations concerned.
Two examples (Cisco and Shell) were presented at HR Tech World in Paris last October. I am proud to once again be moderating the Smart Data breakout at the forthcoming HR Tech World in London on 21-22 March, and would urge all those interested or involved in people analytics to attend. Presenters from the likes of Adidas, ING, EY, Merck and Quintiles will be outlining the people analytics journeys at their respective companies.
Machine-learning prediction methods have been extremely productive in applications ranging from medicine to allocating fire and health inspectors in cities. However, there are a number of gaps between making a prediction and making a decision, and underlying assumptions need to be understood in order to optimize data-driven decision-making. Read More.
Researchers tested the effects of including cues, anchors, and savings goals in a company email encouraging employee contributions to their 401(k).
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.
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.
Our data-saturated age enables us to examine our work habits and office quirks with a scrutiny that our cubicle-bound forebears could only dream of. Today, on corporate campuses and within university laboratories, psychologists, sociologists and statisticians are devoting themselves to studying everything from team composition to email patterns in order to figure out how to make employees into faster, better and more productive versions of themselves. ‘‘We’re living through a golden age of understanding personal productivity,’’ says Marshall Van Alstyne, a professor at Boston University who studies how people share information. ‘‘All of a sudden, we can pick apart the small choices that all of us make, decisions most of us don’t even notice, and figure out why some people are so much more effective than everyone else.’’
Yet many of today’s most valuable firms have come to realize that analyzing and improving individual workers — a practice known as ‘‘employee performance optimization’’ — isn’t enough. As commerce becomes increasingly global and complex, the bulk of modern work is more and more team-based. One study, published in The Harvard Business Review last month, found that ‘‘the time spent by managers and employees in collaborative activities has ballooned by 50 percent or more’’ over the last two decades and that, at many companies, more than three-quarters of an employee’s day is spent communicating with colleagues.
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.
Speaking up at work can be difficult. People worry that their boss or colleagues won’t like what they have to say. As a result, people hold back on everything from good ideas to great questions. But by fostering psychological safety, all employees can feel safe to speak up.
storytelling with data teaches you the fundamentals of data visualization and how to communicate effectively with data. You'll discover the power of storytelling and the way to make data a pivotal point in your story. The lessons in this illuminative text are grounded in theory, but made accessible through numerous real-world examples—ready for immediate application to your next graph or presentation.
Storytelling is not an inherent skill, especially when it comes to data visualization, and the tools at our disposal don't make it any easier. This book demonstrates how to go beyond conventional tools to reach the root of your data, and how to use your data to create an engaging, informative, compelling story.
Specifically, you'll learn how to:
Understand the importance of context
Determine the appropriate type of graph
Recognize and eliminate the clutter
Direct your audience's attention
Think like a designer when visualizing data
Leverage the power of storytelling to help your message resonate with your audience
Together, the lessons in this book will help you turn your data into high-impact visual stories that stick with your audience. Rid your world of ineffective graphs, one exploding 3D pie chart at a time. There is a story in your data—storytelling with data will give you the skills and power to tell it!
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.
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.
We are here because the editor of this magazine asked me, “Can you tell me what code is?”
“No,” I said. “First of all, I’m not good at the math. I’m a programmer, yes, but I’m an East Coast programmer, not one of these serious platform people from the Bay Area.”
I began to program nearly 20 years ago, learning via oraperl, a special version of the Perl language modified to work with the Oracle database. A month into the work, I damaged the accounts of 30,000 fantasy basketball players. They sent some angry e-mails. After that, I decided to get better.
Which is to say I’m not a natural. I love computers, but they never made any sense to me. And yet, after two decades of jamming information into my code-resistant brain, I’ve amassed enough knowledge that the computer has revealed itself. Its magic has been stripped away. I can talk to someone who used to work at Amazon.com or Microsoft about his or her work without feeling a burning shame. I’d happily talk to people from Google and Apple, too, but they so rarely reenter the general population.
There are lots of other neighborhoods, too: There are people who write code for embedded computers smaller than your thumb. There are people who write the code that runs your TV. There are programmers for everything. They have different cultures, different tribal folklores, that they use to organize their working life. If you told me a systems administrator was taking a juggling class, that would make sense, and I’d expect a product manager to take a trapeze class. I’ve met information architects who list and rank their friendships in spreadsheets. Security research specialists love to party.
What I’m saying is, I’m one of 18 million. So that’s what I’m writing: my view of software development, as an individual among millions. Code has been my life, and it has been your life, too. It is time to understand how it all works.
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.
Unconscious biases are created and reinforced by our environments and experiences. Our mind is constantly processing information, oftentimes without our conscious awareness. When we are moving fast or lack all the data, our unconscious biases fill in the gaps, influencing everything from product decisions to our interactions with coworkers. There is a growing body of research – led by scientists at Google – surrounding unconscious bias and how we can prevent it from negatively impacting our decision making.
Job titles help organizations manage their human capital and have far-reaching implications for employees’ identities. Because titles do not always reflect the unique value that employees bring to their jobs, some organizations have recently experimented with encouraging employees to create their own job titles. To explore the psychological implications of self-reflective job titles, we conducted field research combining inductive qualitative and deductive experimental methods. In Study 1, a qualitative study at the Make-A-Wish Foundation, we were surprised to learn that employees experienced self-reflective job titles as reducing their emotional exhaustion. We triangulated interviews, observations, and archival documents to identify three explanatory mechanisms through which self-reflective job titles may operate: selfverification, psychological safety, and external rapport. In Study 2, a field quasiexperiment within a health care system, we found that employees who created selfreflective job titles experienced less emotional exhaustion five weeks later, whereas employees in two control groups did not. These effects were mediated by increases in self-verification and psychological safety, but not external rapport. Our research suggests that self-reflective job titles can be important vehicles for identity expression and stress reduction, offering meaningful implications for research on job titles, identity, and emotional exhaustion.
What happens when Big Data meets human resources? The emerging practice of "people analytics" is already transforming how employers hire, fire, and promote.
in 2003, thanks to Michael Lewis and his best seller Moneyball, the general manager of the Oakland A’s, Billy Beane, became a star. The previous year, Beane had turned his back on his scouts and had instead entrusted player-acquisition decisions to mathematical models developed by a young, Harvard-trained statistical wizard on his staff. What happened next has become baseball lore. The A’s, a small-market team with a paltry budget, ripped off the longest winning streak in American League history and rolled up 103 wins for the season. Only the mighty Yankees, who had spent three times as much on player salaries, won as many games. The team’s success, in turn, launched a revolution. In the years that followed, team after team began to use detailed predictive models to assess players’ potential and monetary value, and the early adopters, by and large, gained a measurable competitive edge over their more hidebound peers.
That’s the story as most of us know it. But it is incomplete. What would seem at first glance to be nothing but a memorable tale about baseball may turn out to be the opening chapter of a much larger story about jobs. Predictive statistical analysis, harnessed to big data, appears poised to alter the way millions of people are hired and assessed.