Data & Data Culture

IHG is obtaining a competitive advantage from applying advanced analytics to pricing and marketing.

What’s happening this week at the intersection of management and technology.

“Big data” is less about handling massive data sets and more about integrating multiple data sources.

Managers today expect computing technology to augment rather than replace the work of humans.

This on-demand webinar highlights research from “Only Humans Need Apply: Winners and Losers in the Age of Smart Machines.”

What’s happening in wearables at work, virtual reality in hiring, and enhancing big data ROI.

Is the forestry industry ready for an industry-specific Internet of Things?

A poll of Fortune 1000 executives shows that obtaining insights rapidly is the true value of Big Data.

The blinders and focus that work well to optimize the details of a problem may prevent managers from seeing other options.

U.S. veterans are helping customize medicine through big data that will unravel the role of genes in health and disease.

Organizations need maturity around analytics, including a better distinction between what “could” and what “should” be done.

Email archive data presents patterns that managers can use to improve organizational performance.

Secrets are a casualty of analytical prowess, and companies have new incentives to act honorably.

Leading companies are using an array of detection and response techniques to become more resilient.

Companies adding analytics professionals must navigate cultural tradition and turf tensions.

Analytics acts as an amplifier for business processes — but companies should keep four principles in mind to avoid increasing “noise.”

The Echo Nest, a “music intelligence” company, uses machine-learning technology to connect people with new music.

What differentiates data scientists from other quantitative analysts? It’s partly their skill set and partly their mind set.

Data analysts may have external agendas that shape how they address a data set — but a savvy manager can identify biases.

Can we automate enough of what data scientists do to ease the skills gap?