Data, AI, & Machine Learning

Advances in inventory and sales analytics make it possible to deliver products both cheaply and quickly.

Join the co-authors of “Using Analytics to Improve Customer Engagement” and special guest Teddy Bekele as they show how analytical innovators are gathering and sharing data to build loyalty and keep customers.

Aspiring leaders need to harbor healthy skepticism of the digital technologies they champion.

Machine learning is susceptible to unintended biases that require careful planning to avoid.

Sports analytics leaders are now using data to understand fans as well as they know their players.

Part 1 of our eight-part video series explores how integrated digital technologies can transform business.

In this webinar, David Harcourt of Yum! Brands Inc. shares his research and experimentation with HR analytics.

MIT SMR’s 2018 Data & Analytics Report finds a link between customer engagement and data analytics.

MIT SMR hosts a Twitter chat on analytics and customer service on March 1, 2018, at 11 a.m. EST #MITSMRChat.

Data audits may be helpful in maintaining balance between data-rich and data-poor companies.

New technology-driven business models are undercutting the traditional advantages of economies of scale.

Robots that use body language can have a positive effect on their human colleagues.

A new survey reports companies are now seeing a direct correlation between big data and AI success.

Microsoft has launched a $50 million initiative to figure out ways to help AI “save the world.”

Organizations that make use of data from a variety of sources excel at customer engagement, as explained in the 2018 Data & Analytics Report.

An infographic based on the 2018 Data & Analytics Report by MIT Sloan Management Review illustrates how companies can better engage with customers using analytics.

The most valuable contributions of AI to the economy may be as an adjunct to advancing discoveries.

Video: Two of the biggest barriers to corporate adoption of AI are access to talent and usable data.

The key lessons learned by Sprint, Bayer and Tapestry in implementing data-driven strategies.

Given the growing importance of data to companies, how should managers measure its value?