How do you get your data “AI-ready”? And more importantly…what does that even mean? At #IMPACT this year, we talked a lot about what it means to prepare for AI. ICYMI, We're moving to @TechTargetNews's Eric Avidon recently covered our AI-readiness session featuring insights from Sri

If 2024 was the year of generative AI... 2025 will be the year of setting reasonable expectations. Wondering what you can expect from data and AI in the new year? In my latest newsletter, I share the 10 trends that I believe will dominate the data and AI conversation in 2025.

Happy holidays, everyone! youtu.be/dxWBCithcfw

Two years ago, I said that the next big crisis for data teams boiled down to one simple problem: proximity to the business. Today, as I reflect on another year of GenAI madness, that problem is more prescient than ever. The key to building useful data products (AI or

Bad data is coming for your AI models. Poor data quality has wreaked havoc on dashboards and ML models for years. But at the scale of AI, the data quality challenge is bigger than ever. Relying on manual data quality checks to effectively cover the massive volumes of data

According to Gartner, AI-ready data will be the biggest area for investment over the next 2-3 years. And if AI-ready data is number one, data quality and governance will always be number two. But why? For anyone following the game, enterprise-ready AI needs more than a flashy

Data engineers are the unsung heroes of the AI revolution. Over the last two years—particularly as large models have gotten better at predictive use cases— there’s been a lot of generalized fear about AI replacing XYZ role. But for data teams—and engineers in particular—it's

Here are 6 things I think every CDO needs to hear about AI-readiness this year: 1. If you’re not in the cloud, you need to be. 2. Your first-party data is your ONLY moat. 3. It’s not enough to make your data available for AI if it isn’t also understandable—so invest in semantic

Bad data is coming for your AI models. Poor data quality has wreaked havoc on dashboards and ML models for years. But at the scale of AI, the data quality challenge is bigger than ever. Relying on manual data quality checks to effectively cover the massive volumes of data

The internet was powering AOL chatrooms before site reliability engineering delivered the fabled “5 9s of reliability.” Data warehouses were creating printed charts to ignore in board rooms before data observability was protecting critical cloud-based data products like ML

Explore the convergence of structured/unstructured data, AI, and SaaS stacks, and why end-to-end observability is crucial for enterprise-grade reliability in 2026. Read the full article by Barr Moses free now. towardsdatascience.com/2026-will-be-t…

Data and AI are no longer two separate technologies. It’s time we stopped treating them that way. That’s why I’m ecstatic to announce that Monte Carlo will be extending our partnership with Databricks to bring our vision for data + AI observability to the Databricks’ Data

Question—Is it possible to drive differentiation through better governance? Governance has historically been viewed as a cost-center for most teams—a pair of administrative handcuffs to mitigate regulatory or compliance risk (among other things). But is it possible that it’s

I don't care how big your context window is. While it’s true that running a single complex action on a model with a large context window would lead to a more favorable output than a smaller model all other things being equal, that assumes that you actually need to run that

Have you seen Stanford University's new AI Index Report? There's a ton to digest, but this takeaway stands out to me the most: “The responsible AI ecosystem evolves—unevenly.” In the report, the editors highlight that AI-related incidents are on the rise, but standardized

This might be the biggest news we’ve shared all year—and I’m so freakin’ excited about it. Monte Carlo just announced our first-ever Observability Agents to accelerate reliability workflows for enterprise teams—beginning with our Monitoring and Troubleshooting Agents to

Agents to drive reliable data + AI applications are here. When we launched Monte Carlo in 2019, we wanted to help every team own the quality and trust of their data—and this is an enormous leap towards that reality.

Think model evaluation will replace the need for high quality data? Think again. Bad data has been eroding great data pipelines for years—but in an agentic workflow, those risks can cascade into all kinds of systemic problems. And the worst part? Model evaluation is leading

According to IDC, 90% of data is unstructured. The question has always been—how do we make it reliable for production? Well, now we have an answer. I’m thrilled to OFFICIALLY ANNOUNCE Monte Carlo’s support for Unstructured Data Monitoring. As part of an ongoing commitment to