Data, AI, & Machine Learning

Even if your job is high paying, it is wise to ask yourself whether it is common and repetitive enough to be done by a machine. If you conclude that it is, it’s time to look for — or create — less commoditized work.

Composing valuable strategies requires seeing the world in new and unique ways. It requires asking novel questions that prompt fresh insight. Even the most sophisticated, deep learning-enhanced computers or algorithms simply cannot generate such an outlook. Innovative strategies depend more on novel, well-reasoned theories than on well-crunched numbers.

The successful use of analytics in sports, both on the field and off, comes down to integrating analytics within an organization. Three strategies — collaborative analytics, a common language, and accessible technology — are key.

Identifying the optimal prices for products was once a time-consuming process. That’s changing as businesses start to take advantage of advances in machine learning, increases in computing speed, and greater availability of data.

Plummeting data acquisition costs have been a big part of the surge in business analytics. We have much richer samples of data to use for insight. But more data doesn’t inherently remove sampling bias; in fact, it may make it worse.

To gain competitive advantage from supply chain analytics, many companies need to reduce the time it takes to act on the insights those analytics generate.

Changing consumer expectations, new regulations, and an influx of patient data has created a perfect storm for health care organizations like Schumacher Clinical Partners to rethink how they leverage digital tools to better serve their patients and providers.

Even when faced with evidence that an algorithm will deliver better results than human judgment, we consistently choose to follow our own minds. Why? MIT Sloan Management Review editor in chief Paul Michelman sat down with the University of Chicago’s Berkeley Dietvorst to find out.

Many executives believe they are good at identifying leadership talent. However, when asked how they make their decisions, they often cite intuition or “gut” instincts. Social science research, on the other hand, suggests that individuals are often prone to cognitive biases in such decisions. Rather than just relying on the subjective opinions of executives, some companies are using assessment tools to identify high-potential talent.