Monte Carlo

Gartner’s latest Data and Analytics Governance Hype Cycle is live – and data observability has a great view from the top. ⬆ The category has been steadily on the rise since we launched it back in 2019. Now, data observability is no longer a nice-to-have when it comes to data quality – it’s an essential component of any data and AI strategy. As AI becomes more critical to the operations of enterprise data teams, developing the right features to enable data reliability at scale is essential. That’s why we’re committed to refining and accelerating our data observability solution to help data teams deliver reliable data products faster and easier than ever. Learn more about the future of data observability here: https://lnkd.in/dTkVkuDc #dataobservability #dataquality #datagovernance #AI

Really exciting to see Gartner's Market Guide for Data Observability Tools! The category has evolved so rapidly over the last 24 months, and Melody Chien and her team have done a fantastic job helping data leaders navigate what a strong data observability approach looks like and the zero-day problems it solves. I remember the first time I spoke with Melody about data observability, almost four years ago (!). We discussed the differences between data quality management solutions and data observability platforms; the importance of having strong integrations across the stack; and how data observability goes beyond detection to answer the "so what?" with investigation, resolution, FinOps, and impact assessment - among other capabilities. Onwards and upwards - we're just getting started!

Don't miss this fantastic article from Mike Carpenter and the Mission Lane team about their experiencing building a Continuous Compliance Monitoring program – an automated approach to detecting instances of non-compliance within their operations – using Monte Carlo's data observability solution. 🔥 Check out the full story on their blog! https://lnkd.in/gjGXFaGE #dataengineering #dataobservability #dataquality

We’re proud to announce the official integration between MotherDuck and Monte Carlo! By extending Monte Carlo's data observability solution across MotherDuck databases, teams can drastically reduce the time to detection and time to resolution for data quality issues. Check out the full announcement here: https://lnkd.in/gf8mAArH #duckDB #dataobservability #dataengineering

Which one are you? Let me know in the comments - happy Monday! #datamemes #dataquality

We're thrilled to announce AI anomaly detection for SQL Server! AI anomaly detection is efficient to deploy and can produce better coverage than relying on manual SQL queries with predefined thresholds to detect bad data in SQL Server. Learn why an AI-first approach to detection can help deliver more reliable data within the modern enterprise: https://lnkd.in/eC66HvCs #dataobservability #dataquality #SQL #AI #dateengineering

Don't miss this great article from Matt Aslett, Director of Research at Ventana Research on the crucial differences between data observability and data quality software. Some highlights: "While data quality software is concerned with the suitability of the data to a given task, data observability is concerned with the reliability and health of the overall data environment." "Data observability tools monitor not just the data in an individual environment for a specific purpose at a given point in time, but also the associated upstream and downstream data pipelines. By doing so, data observability software ensures that data is available and up to date, avoiding downtime caused by lost or inaccurate data due to schema changes, system failures or broken data pipelines." "While data quality software is designed to help users identify and resolve problems related to the validity of the data itself, data observability software is designed to automate the detection and identification of the causes of data quality problems. As such, data observability can potentially enable users to prevent data quality issues before they occur." Check out the full article: https://lnkd.in/gCDtQRUD #dataobservability #dataquality #dataanalytics #dataproduct

Happy Hump day data rockstars! So many people are talking about the correlation between data quality and AI and love that the attention that is finally being paid to the actual core driver of AI success. Here is a reminder of some concepts of why you need to implement #dataobservability tools. Improved Data Reliability: Helps you understand the full data pipeline and why things are happening the way they are. No more worrying about "is the data doing what it is supposed to?". Ensure Data Freshness: In a pipeline where you are getting millions of records per hour, you need confidence that you are getting the expected data changes (add, deletes, etc). Have you ever had to say after a day of stale dashboards, "we aren't getting the data from the source". You need to know immediately. Early Detection of Issues: From broken data contracts to pipeline delays, you can know right away, lowering the impact to the business. Data Drift: This is when the properties of data change over time in ways you didnt expect or data might be introduced to training sets that isn't the right context for use case. Observability tools can identify these changes. Data Governance: You should always know the path of data, who is using it and how it's used. Observability tools give you this information and documents these items. Just the beginning of the value of observability. Hit me up if you have questions. #data #analytics #ai Monte Carlo

Monte Carlo was just named #1 Data Observability solution by G2 for the 5th quarter in a row. However, as excited as I am to win, this award doesn't matter. Companies win awards all the time—at the end of the day, the only thing that really matters is whether or not you’re making a difference for your customers. And that’s exactly why we don't just build Monte Carlo for our customers—we build it with them. At Monte Carlo, we aren't just creating a bunch of tools in the hopes that our customers might use them. We're asking our customers every single day what problems they already have—and then we're working hard to solve them. From SQL server anomaly detection to our Jira Service Management integration, that same customer obsession we had back in 2019 when we created data observability is the same customer obsession that drives us forward today. And reading reviews like this gives me all the confidence that we’re headed in the right direction. #dataobservability #datareliability #dataquality #customerobsessed

We're thrilled to share that Monte Carlo was just named #1 Data Observability solution by G2 for the 5th quarter in a row! G2 is powered by real customer ratings and reviews, which makes this award all the more meaningful for us. At Monte Carlo, we strive to deliver meaningful customer impact in everything we do—and this award is the receipt. To all our amazing customers: thank you for being with us on the journey! We couldn't do it without you. And we're just getting started! 🚀 Check out the full list of awards here: https://lnkd.in/eYmwt__9