A Classic Data Problem is Taking Hold as AI Readies for Production

AI quality issues are on the rise and data + AI leaders are just beginning to feel the pain. One of the most common perpetrators?  Data quality issues. At Monte Carlo, we’re no strangers to the impact of data quality—particularly at the scale and complexity of AI applications. However, we recently experienced that impact first-hand—and …

“We need to get our data AI-ready,” is far and away the most common refrain we hear from data + AI leaders.  This somewhat amorphous concept has gradually been defined by Gartner and others as a three step process: And there are no objections here! There was even a time way back in 2023 when …

Experts from venture capital, Snowflake, and Monte Carlo discuss how generative AI will benefit data teams…and the challenges they must help solve. 

Building Reliable Foundations for Data + AI Systems It’s no big revelation that data teams are being challenged to do more with AI. But while deploying an AI prototype has never been easier, operating those applications safely in production is harder than ever.  Achieving high-quality data remains one of the most critical components for trustworthy …

By now, most data leaders know that developing useful AI applications takes more than RAG pipelines and fine-tuned models — it takes accurate, reliable, AI-ready data that you can trust in real-time. To borrow a well-worn idiom, when you put garbage data into your AI model, you get garbage results out of it.  Of course, …

Data teams are more important than ever before - but they need to get closer to the business. Here’s how we can right the ship.