Data Strategy

This Strategy Guide offers expert advice on creating a comprehensive and flexible cloud strategy.

The journey to be more client-centric has brought a focus on artificial intelligence and data at Northwestern Mutual.

This issue of MIT SMR focuses on talent management, innovation strategies, and emerging technologies.

Based on their research, the authors share four key ways companies can advance their strategic data-sharing initiatives.

The human side of data continues to challenge companies.

New data-efficient AI techniques can help when developers lack sufficient volumes of labeled training data.

Embedding data, analytics, and artificial intelligence into products has been a game changer for businesses.

Most companies that are using AI are deploying it for augmentation, not large-scale automation.

This issue of MIT SMR focuses on customer relationships and their connection to innovation and value.

Disciplined experimentation is bringing the use of artificial intelligence into clinical health care at Mayo Clinic.

Plenty of organizations dabble with AI, but Mastercard’s capabilities — and confidence — set it apart.

Health care organizations are seeing significant savings using AI in administrative systems.

Businesses must use data and analytics to better anticipate consumer needs and humanize digital interactions.

Respondents to recent global surveys say their organizations are capturing substantial value from AI.

Scotiabank’s focus on AI projects likely to deliver value in a short time frame is paying off.

Fostering tech-mediated collaboration, dignity in employee data use, and in-house social intrapreneurship.

DBS Bank’s CEO exemplifies how a willingness to experiment and even fail can help advance new technologies like AI.

New value creation with strategic data assets, in-store shopping to build customer loyalty, and habits to enable enduring culture change.

To monetize data, companies must first transform it so it can be reused and recombined to create new value.

Six strategies can help guide data science teams toward greater success in cross-unit projects.