Telecom networks are closer than ever to being able to fix their own data, and the implications are significant.

Cycling's high-performance evolution is pushing riders toward burnout. Here's how they overcome the perils of a 24/7 sport.

Data quality issues emerge from multiple failure points from development practices to production life cycle, each compounding ...

A strong data governance foundation is essential for higher education institutions to deploy trustworthy, effective ...

Bad customer data – databases where customer data is inaccurate, incomplete and inconsistent – causes huge issues for financial institutions. Accurate customer data informs effective customer ...

Duolingo (DUOL) offers a compelling near-term long opportunity, driven by misunderstood AI risks and a strong catalyst path into 2026. The bear case overstates AI disruption, missing DUOL's core value ...

It was 2020, and Nicolas Orban, Stéphane Derosiaux, and Stéphane Maarek were beyond frustrated with Apache Kafka. The tool for handling real-time data streams simply couldn’t keep up with the trio’s ...

The 1:10:100 rule—coined in 1992 by George Labovitz and Yu Sang Chang, the rule describes how much bad data costs. Preventing the creation of bad data at its source costs $1. Remediating bad data ...

Editor’s note: These are big, complex topics — so we've spent more time exploring them. Welcome to GT Spotlight. Have an idea for a feature? Email Associate Editor Zack Quaintance at ...

Everyone understands data is important, but many business leaders don’t realize how impactful data quality can be on day-to-day operations. In my experience, nearly all process breakdowns have root ...

AI’s future doesn’t depend on ever-larger models but on better, human-curated data. AI risks bias, hallucinations and irrelevance without expert oversight and high-quality training sets. AI is a paper ...