Analytics and data science

If you have to do it, be transparent about it.

A guide to the most promising opportunities.

By reverse engineering your team's workflow, you can customize generic AI tools to fit your processes.

What to do after you’ve identified where gaps exist.

Why they often find it challenging to reproduce their success in a new environment.

How to weigh these competing demands.

Lessons from FICO.

Efforts to manage cyber risk must be transparent, structured, justifiable, and agile.

Studies have shown that people dislike attempts to make AI convincingly human. But emphasizing the essential role of humans in AI tools can help reduce resistance to adoption.

Steps managers can take to make sure everyone sees data as a crucial part of their job.

Artificial intelligence is only part of the future.

An annual survey of Fortune 1000 and global business leaders shows how companies are doubling down on data projects that they’ve struggled with in the past.

Use these four lenses to focus on what matters.

Make sure your people and your technology work well together.

Learn fast by democratizing testing.

It can cut decision-making time from days to minutes and dramatically improve results.

Process management has long been a go-to method for achieving the improvements in operational performance that stakeholders constantly demand. By applying...