
Leading Change
The Surprising Value of Obvious Insights
Confirming what people already believe can sometimes help organizations overcome barriers to change.
Confirming what people already believe can sometimes help organizations overcome barriers to change.
Five steps to make sure your data and analytics efforts pay off in the long term.
The 2018 Artificial Intelligence Report by MIT SMR shows early leaders pushing forward with an eye toward scale.
The best path to AI is to establish a foundation in capturing, preparing, and analyzing data.
Small errors in data linkages can sometimes prove damaging — but not if managers watch for them.
University of Chicago’s Berkeley Dietvorst explains why we can’t let go of human judgment — to our own detriment.
The 2017 Data & Analytics Report by MIT Sloan Management Review finds that companies that embraced analytics have begun to find new ways to derive strategic benefit from analytics.
Miscommunications between decision makers and data scientists are common. Enter the data translator.
A series of small errors in data can lead to major mistakes.
Technology innovators should be wary of letting big data speak for itself.
Case studies from a range of businesses highlight how analytics requires organizations to evolve.
What’s happening this week at the intersection of management and technology.
What’s happening this week at the intersection of management and technology.
Here’s what it takes to lead a high-performing data science team in which team members (and their managers) are excited by what their teammates can do.
Companies adding analytics professionals must navigate cultural tradition and turf tensions.
Data analysts may have external agendas that shape how they address a data set — but a savvy manager can identify biases.
When it comes to big data, GE avoids warehousing and instead turns to the data lake approach.
Can we automate enough of what data scientists do to ease the skills gap?
If you think analytics is just about the math, you’re telling yourself the wrong story.
Companies are having a tough time finding the data scientists they need — but that doesn’t mean those projects need to halt altogether.