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Employee odometers and technostress; big data trumps analytics; profiling the chief data officer.
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Organizations across an increasing number of sports and levels of competition are capitalizing on data to gain a competitive edge. Indeed, few industries have implemented data-driven decision making as successfully as sports. And learnings from the sports analytics revolution are applicable to a broad range of other industries.
New advances in machine learning are allowing sales teams to manage huge amounts of data to become more effective and efficient. Our research suggests three main ways machine learning is being successfully integrated into sales processes. First, it allows for a scientific approach that clarifies opaque parts of the sales process. Second, it enables more-effective data-driven experimentation. Third, it automates administrative duties that take time away from higher-value tasks.
Simulating a better culture; bolster your value proposition with software; downsizing the C-suite for digital.
The next time you’re sitting at a red light, savor the moment. If researchers from MIT’s SENSEable City Lab and the Ambient Mobility Lab have their way, your hours of waiting at traffic lights could be numbered. In an article published in PLoS One, a team led by MIT’s Carlo Ratti and Paoli Santi describe a system in which automobiles and transportation infrastructure would interact though an algorithm that would manage the safe flow of cars through busy intersections.
Using data and analytics to understand the complexities of modern business has become not only common, but essential. Gahl Berkooz joined Ford Motor Co. in 2004, eventually becoming head of data and governance and a member of the company’s global data insights and analytics skill team. Berkooz became acutely aware of how important analytics is to the company’s ability to thrive in the global marketplace. “What it boils down to,” he told MIT SMR’s Michael Fitzgerald, “is that we know how to make decisions. It’s about finding the opportunities to bring data and analytics to make better decisions.”
This new blog from MIT Sloan Management Review explores ideas from different corners of the MIT community that are relevant to business executives. We will introduce you to research, people and events you might not otherwise encounter — things we hope you find useful and perhaps provocative. This week we look at gaping security holes in the Internet of Things and revisit the analytical revelations of Michael Lewis’s Moneyball.
If you really want to create value, forget about burning platforms and start building them. A platform, explain professors Geoffrey Parker and Marshall Van Alstyne, and Sangeet Choudary, founder and CEO of Platform Thinking Labs, in Platform Revolution: How Networked Markets are Transforming the Economy and How to Make Them Work for You, is a “business model that uses technology to connect people, organizations, and resources in an interactive ecosystem.”
Analytics offers managers a great way to fine-tune processes, but too many executives focus on metrics at the expense of the bigger picture. The blinders and focus that work well to optimize the details of a problem may prevent managers from seeing other options, and intense focus on a narrow measure can address only the well-specified puzzle — resulting in a myopic view of the problem. Executives who desire bigger breakthroughs need to encourage exploration.
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