Data, AI, & Machine Learning

Developments in enabling technology are opening up more use cases for virtual models of real-world objects.

New research highlights nine key factors impeding organizations’ ability to advance their data science progress.

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

Monolithic, highly interdependent organizations can become modular ones by embracing microservices.

Two new branches of digital olfaction technology could help companies use smells to improve the customer experience.

Anticipating and withstanding cyberattacks — cyber resilience — must become a companywide concern.

Data-driven decision-making anchors on available data, which can lead decision makers to focus on the wrong question.

Organizations that struggle to gain payback from data science efforts can recognize and overcome five common obstacles.

To gain business agility, leaders must deconstruct jobs into tasks and deploy workers based on their skills.

Data accessibility must be managed from the start of AI projects in order to be implemented in production.