Paul Michelman, editor-in-chief of MIT Sloan Management Review
MIT Sloan Lecturer Ben Shields
From MIT Sloan Management Review
Almost every team’s fans can point to a high-profile, free-agent signing that fell flat. Sometimes the reasons for the player’s underperformance are clear — injuries, for example. But often, it’s harder to pinpoint the cause. In those cases, we tend to point to lack of effort as the culprit. This is what researchers call shirking. According to Richard Paulsen, who presented his paper, “New Evidence in the Study of Shirking in Major League Baseball,” at the Sloan Sports Analytics Conference, “Shirking occurs when an employee exerts effort … that is suboptimal in the eyes of the employer.” Paulsen speaks with us about his research on shirking and his belief that the Phillies are going to regret at least one free-agent signing from this off-season.
Fellow, MIT Center for Digital Business, Tom Davenport
From BizEd Magazine
The rise of data analytics is one of the hallmarks of 21st-century business. By the turn of the century, companies had been accumulating data in various transaction systems for several decades, and many desired to analyze the data to make better decisions. Their interest intensified in the early 2000s as they saw the great success of online firms from Silicon Valley, many of which were highly analytical.
In fact, during the mid-2000s, I conducted research showing that some companies were “competing on analytics”— that is, emphasizing their analytical capabilities as a key element of their strategies—and that those companies tended to outperform other firms in their industries. Information about analytics even made it into popular culture, especially through books such as Moneyball, which was also a successful movie. Both depicted the way the Oakland A’s of California built a winning baseball team through targeted data analysis.
The American engine of progress and prosperity is in serious trouble. Innovation has stalled. The number of good, middle-class jobs is dwindling. Wealth and opportunity are increasingly concentrated in a few coastal megacities. And cultural divides are widening. How do we turn this tide?
The answer lies in science — specifically, government-funded science. Investment in science is the ultimate pro-growth policy: It leads to more invention, higher productivity and broad-based economic development.
According to our research, if the U.S. government were to boost funding by $100 billion per year with strategic, geographically dispersed investments and initiatives, the result would be roughly 4 million new jobs.
Pamella Gonçalves, MIT Sloan Management alumna, MBA ’17.
The practice of Urban Analytics is taking off within the real estate industry. Data science and algorithmic logic are close to the forefront of new urban development practices. How close? is the question — experts predict that digitization will go far beyond intelligent building management systems. New analytical tools with predictive capabilities will dramatically affect the future of urban development, reshaping the real estate industry in the process.
In his introduction to ‘Smart Cities,’ Anthony Townsend raises the issue clearly: “Today more people live in cities than in the countryside, mobile broadband connections outnumber fixed ones and machines outnumber people on a new Internet of Things.” Yet neither the glossy marketing of major IT players such as IBM and Cisco nor the dystopian theories of critical scientists like Adam Greenfield admit that the digital revolution washing over cities has yet to be fully evidenced. Instead, over the last decade we have witnessed the slow emergence followed by strong growth of the computational paradigm applied to urban planning and real estate.
MIT Sloan Visiting Lecturer Irving Wladawsky-Berger
From The Wall Street Journal
People have long feared that machines are coming for our jobs. Throughout the Industrial Revolution there were periodic panics about the impact of automation on work, going back to the so-called Luddites, textile workers who in the 1810s smashed the new machines that were threatening their jobs.
Automation anxieties have understandably accelerated in recent years, as our increasingly smart machines are now being applied to activities requiring intelligence and cognitive capabilities that not long ago were viewed as the exclusive domain of humans. But on balance, such fears appear to be unfounded, noted the World Bank in a comprehensive recent report on The Changing Nature of Work. Our problem is not that there won’t be enough work in the future. Our key problem is that, in many countries, the workforce is not prepared for our fast unfolding future.