Data, AI, & Machine Learning

How should organizations approach strategic decision-making?

Sports analytics shows that the best performance enhancer is sleep.

Data on its own has value, but insights from that data substantially increase its value.

A Twitter chat featuring Ben Alamar examines how sports analytics can inform companies’ use of data.

What if, instead of perpetuating harmful biases, AI helped us overcome them?

Digital tools are making the hiring process easier and more precise — despite their limitations.

An industry executive and a scholar discuss how AI-based tools can transform the retail business.

Why do losing teams often out-perform winners at the box office?

Would NBA teams make fewer draft mistakes if they measured basketball IQ?

Done right, automation can be a win for everyone — even workers.

Banks need a clear AI strategy to get digital transformation right.

Recommendation algorithms don’t just reflect consumer preferences — they also shape them.

Businesses that make and sell products that replicate human connection are serving a deep need, but they may also be changing social norms in ways that can’t be reversed.

Makers of AI applications should stop overpromising, be transparent, and consider certification.

One key strategy for AI success: retraining employees to have the skills your company will need.

Retail companies that neglect machine learning do so at their peril.

An industry executive and a scholar offer perspectives on new competition in the staffing business.

Prediction is appealing, but detection may be equally valuable for businesses.

Data-driven decision-making comes with challenges and opportunities, says GoDaddy’s chief revenue officer.

Data best supports marketing when researchers fully understand what they want to measure and how.