Data & Data Culture

Dramatic changes within the life sciences industry present unique opportunities to use AI.

JoAnn Stonier, chief data officer at Mastercard, discusses how design thinking enables better AI implementation.

To make data migration more effective, start with a minimum set of viable data, leave out nice-to-have data, and weigh speed vs. quality.

Amit Shah, president of 1-800-Flowers, explains why lifelong learners are the best technical talent.

Will Grannis of Google Cloud explains the organization’s collaborative approach to AI and machine learning innovation.

Testing can guide decisions such as who needs to work in an office and what work hours are optimal.

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

Data science obstacles, concerns about data executive roles, and the virtual hiring process.

Only a third of data executives feel that their role is “successful and established.”

Making diversity and inclusion real, clarifying pandemic data, and how Amazon will innovate post-Bezos.

The authors describe three key challenges leaders face in communicating pandemic data and how to address them.

To deliver on the promise of AI, leaders must focus on improving their organization’s culture and decision-making capabilities.

Companies that emphasize collaboration between AI and human workers are best positioned for success.

Transforming a company into a truly data-driven business involves fundamental organizational changes.

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

Leaders must focus on quality, build organizational capabilities, and put data to work in new ways.

Top chief data officers share insights on how leaders and organizations can ensure data success.

Leaders need to examine their core beliefs if they want to prosper in a COVID-19 world.

A Q&A with AWS’s Ishit Vachhrajani on how leaders can generate excitement about and support for AI organization-wide.

Companies need to identify the type of talent they need in order to become data-driven.