A look at three new books and a TV series: The Work of Art, by Adam Moss; The Real Work, by Adam Gopnik; All That Happiness Is, by Adam Gopnik; and Grand Designs, from Naked Television.
When researchers from Cass Business School compared data on board diversity with fines levied for misconduct, they found that banks with more female directors were fined less often and less significantly.
Despite heavy investments to acquire talented data scientists and take advantage of the analytics boom, many companies have been disappointed in the results. The problem is that those scientists are trained to ask smart questions, wrangle the relevant data, and uncover insights--but not to communicate what those insights mean for the business. To be successful, the author writes, a data science team needs six talents: project management, data wrangling, data analysis, subject expertise, design, and storytelling. He outlines four steps for achieving that success: (1) Define talents, not team members. (2) Hire to create a portfolio of necessary talents. (3) Expose team members to talents they don't have. (4)  Structure projects around talents.
Though the research on meditation and mindfulness is almost universally positive, a new study points to a potential downside of being present in the moment: A significant decrease in motivation. Yet, surprisingly, the inspirational dip appeared to have no impact on task performance.
The idea behind performance feedback is that to help people grow, we need to shine a light on the things they can't see about themselves. But a recent study shows that when employees receive critical appraisals from their peers, it mostly makes them seek out colleagues who will give them positive reviews instead.
If your industry is in turmoil, your instinct might be to double down on innovation so that your firm can get ahead of all the change. But new research from a team led by a professor from City University of London suggests you might want to hold off. Its study of innovation in Formula 1 racing showed that when car technologies were undergoing rapid shifts, the teams that produced very basic vehicles outperformed the rest.
When researchers compared daily data from the S&P 500 index with daily environmental data from Lower Manhattan, they discovered a connection between poor air quality and diminished stock performance.
Not long ago, the ability to create smart data visualizations (or dataviz) was a nice-to-have skill for design- and data-minded managers. But now it's a must-have skill for all managers, because it's often the only way to make sense of the work they do. Decision making increasingly relies on data, which arrives with such overwhelming velocity, and in such volume, that some level of abstraction is crucial. Thanks to the internet and a growing number of affordable tools, visualization is accessible for everyone--but that convenience can lead to charts that are merely adequate or even ineffective. By answering just two questions, Berinato writes, you can set yourself up to succeed: "Is the information conceptual or data-driven?" and "Am I declaring something or exploring something?" He leads readers through a simple process of identifying which of the four types of visualization they might use to achieve their goals most effectively: idea illustration, idea generation, visual discovery, or everyday dataviz. This article is adapted from the author's just-published book, "Good Charts: The HBR Guide to Making Smarter, More Persuasive Data Visualizations."
Big data and the "internet of things" promise revolutionary change to management and society. But their success rests on the assumption that all the data being generated by internet companies and devices scattered across the planet belongs to the organizations collecting it. Pentland suggests that companies don't own the data, and that without rules defining who does, consumers will revolt, regulators will swoop down, and the internet of things will fail to reach its potential. To avoid this, he has proposed a set of principles and practices to define the ownership of data and control its flow. He calls it the New Deal on Data. The New Deal is "rebalancing the ownership of data in favor of the individual whose data was collected," Pentland says. "People would have the same rights they now have over their physical bodies and their money." They could see what was being collected and then opt out or opt in. Many companies are afraid that the regulation of data collection will kill their business models, he says. But he believes that it will make for a healthier economy--and that it will prevent disasters such as the criminal use of data in a way that affects critical systems and causes deaths. "If that kind of disaster happened," Pentland says, "there would be an overreaction: Shut it down. You'd see very strong regulation passed overnight, and a lot of companies would be in deep trouble."
In a new study, subjects chose to experience a higher electric shock immediately over waiting a while to experience a milder shock--indicating that dread may be a powerful negative force.