Data Management

It’s impossible to abolish AI bias in the data behind artificial intelligence models, but companies can remediate it.

Artificial intelligence is quietly improving the management of data, including its quality and security.

ADP’s Jack Berkowitz explains the benefits of having data strategy, data products, and AI oversight within the CDO role.

Successful cyberattacks often result from isolated decisions made without fully considering the potential consequences.

Data science project failure can often be attributed to poor problem definition, but early intervention can prevent it.

A new article series explores how organizations must manage and monitor technology in new ways to achieve positive ethical outcomes.

Making your organization fit for data, enhancing value with nontraditional stakeholders, and supporting working parents.

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.

Organizations should manage data science with an appropriate structure and enterprisewide process.

Crowdsourcing platforms, updating how work gets done in a new normal, and simplifying data migration.

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.

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.

Revolutionary recommendation engines, data access as a leadership priority, and the essentials of successful corporate social justice efforts.

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

MIT SMR’s winter issue looks at why teams work (or don’t), plus innovation, supply chains, and data for AI.

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.