Data & Data Culture
Detecting Bias in Data Analysis
Data analysts may have external agendas that shape how they address a data set — but a savvy manager can identify biases.
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.
As business moves to a real-time, data-driven focus, the search for talent has undergone a quantum shift.
Can we automate enough of what data scientists do to ease the skills gap?
Simulations can help shrink the gap between what analysts try to explain and what decision makers understand.
The NFL’s CIO discusses the organization’s customer-focused approach to big data and analytics.
As sensors and computer-mediated transactions become universal, Google’s Hal Varian warns that organizations need to prepare for a flood of data.
If you think analytics is just about the math, you’re telling yourself the wrong story.
Stories of your competitors’ analytics prowess are probably overblown — so take steps to move forward now.
Companies are having a tough time finding the data scientists they need — but that doesn’t mean those projects need to halt altogether.
A company that wants to successfully use analytics needs to make sure its data scientists are fully integrated into business units.
Kaiser’s John Mattison describes the data-driven healthcare system of the future — and says companies need to get in gear to meet its challenges now.
Hot shots get all the attention, but other team members can be the ones who make a group really tick.
It’s never a good idea to assume your dataset is capturing all the information you need to make good decisions — but there are ways to mitigate uncertainty.
With millions of data points and an amazing array of analysts, Amadeus turns air travel into an exercise in data science.
NC State’s Institute for Advanced Analytics is the first business analytics program in the country — and way ahead of its time.
To create real business value, top management must learn how to manage data scientists effectively.
When using analytics becomes a routine practice, four key changes will follow.
Companies seeking to use analytics for predictive decision making commonly make these four errors.
The impulse to collect and store all data on the off chance it might be useful is counterproductive.