Monte Carlo

⚡ Boost your data quality at scale with high-performance observability! Join TDWI’s "Driving Data Quality at Scale with High-Performance Observability" webinar on October 24, featuring James Kobielus, Raghu Kuppala from Amazon Web Services (AWS), and Michael Segner from Monte Carlo. 🔗 https://bit.ly/4873wAi Gain actionable insights on: ✅ Leveraging data observability to maintain data quality across cloud environments ✅ Detecting and resolving data issues in real-time ✅ Scaling data quality management with cutting-edge tools Ensure your enterprise data stays reliable and efficient. Register now! #DataQuality #DataObservability #CloudData #TDWI #AWS #MonteCarlo #DataManagement

AI chatbots are a dime a dozen these days (or $15 / 1M output tokens if you’re using OpenAI). But building a valuable chatbot takes more than an OpenAI subscription.This story of how the data team at WHOOP used GenAI to democratize access to reliable insights is a masterclass in how to make a useful chatbot. According to Matt Luizzi, his team had “several hundred dashboards and all the typical sprawl you see in BI… Everyone’s creating things, nobody knows what’s being used or what’s correct… Depending on where you go, you may or may not get the right answer.” Matt’s team saw an AI chatbot as the perfect way to create a single source of truth that could be easily—and reliably—queried by his stakeholders. The first order of business? Getting their data quality in order. Here’s how they did it: Step 1. Re-architect their dbt project to improve documentation and accessibility. Step 2. Leverage lineage to deprecate dashboards that weren’t being used. Step 3. Define “golden questions” to audit the chatbot’s outputs. In the end, Matt and his team eliminated 80% of their existing dashboards, and implemented new data quality practices that improved not just the quality of reliability of their chatbot, but the reliability of their broader data platform as well. “Getting in the room and having conversations with the right stakeholders is half the battle,” says Matt. “For us, being able to showcase the fact that we’re able to not only create dashboards and run A/B tests but actually build tooling that’s serving the business — that’s gotten us a lot of value in the organization.” Check out the full story via link in the comments to get all the insights and find out what’s next for the data team at WHOOP.

Mark your calendar: Philip Zelitchenko, VP of Data and Analytics at ZoomInfo, will be speaking at IMPACT 2024 on November 14th! 🚀 He'll share practical strategies for ensuring data products are both innovative and aligned with business goals, plus insights into optimizing product management processes, fostering a culture of empowerment, and driving continuous innovation to achieve sustainable business success. The session starts at 11am! Register here: https://lnkd.in/eiT7tBUx #IMPACT2024 #dataobservability #dataquality #dataproducts

We're thrilled to announce our new data observability features for Azure Data Factory, Informatica, and Databricks Workflows. With this new release, Monte Carlo users can gain visibility into pipeline failures, lineage, and pipeline performance – all in a single pane of glass. Learn more here: https://lnkd.in/eicnMCY3 #dataobservability #datapipeline #datalineage #dataquality

Tomorrow! Meet us at the Snowflake World Tour in Stockholm! ❄️ Be sure to swing by the booth, and don't miss Pedro Sá Martins, Head of Data Engineering at OutSystems, speaking at 16:45! In his session, he'll discuss the evolution of OutSystems’ data landscape, including how OutSystems has partnered with Snowflake, Fivetran and Monte Carlo to address modern data challenges, and his best practices for implementing scalable data quality programs. See you there! Register here: https://lnkd.in/d8gA3E8X #enterpriseAI #dataobservability #dataquality #snowflakeworldtour

For data teams, nothing causes a bigger headache than when your pipeline fails and you don't know why - or how. All too often, we over-index on the symptom instead of focusing on curing the disease. This is a problem because: - It makes stakeholders happy in the moment (my dashboard is updated) but doesn't get to the root cause. - It incentivizes tech debt instead of building robust and scalable systems. - It turns firefighting into the de-facto method of incident management instead of defining clear owners and processes. - In the long term, it positions data as a cost center instead of a well-managed, reliable revenue driver. And the list goes on. But there's a better way, and my colleague Neil Gleeson, one of Monte Carlo's most tenured customer success managers, shares how. Check out his post on the core steps for rolling out an effective and and headache-proof data incident management program.

Don’t miss Bronte Baer, Manager of Data Platforms & Analytics Engineering at Earnest, at IMPACT 2024 on November 14th. 🚀 In her session at 11am, Bronte will share lessons learned during Earnest’s transition from a monolithic data system to a microservices-based architecture, including the reliability challenges that come with data migration, monitoring, and validation in a new decentralized environment. Mark your calendars! Register here: https://lnkd.in/eB8Gaeae #IMPACT2024 #dataobservability #datareliability #dataquality

Earlier this year, I was stressed out by a debt collection call. A small mistake was made by PG&E, where they misread my $184 check as $18.40. Just one decimal in the wrong place, and suddenly, I’m on a debt collector’s list, with my credit score at risk. This isn't just about my experience. Let's do some quick math: PG&E has ~16 million customers. So, even with a 0.1% error rate in their billing, that's potentially 16,000 affected people! Now, in a world where a misplaced decimal can spiral into a collection call, imagine the impact of data quality issues on AI products. Because when it comes to AI, the stakes are higher. A small crack could get to the algorithms and magnify when the model is in production. “One small piece of garbage in, 100X garbage out.” Don't let the data product you build make the same mistake that sent me into a panic. If you want to learn how to safeguard your data and AI systems, the upcoming Data Reliability Summit by Monte Carlo is your go-to event. You'll learn from top data leaders about: · Driving trust in data and AI at scale · Building robust data systems for increasing AI adoption · Avoiding million-dollar mistakes with data quality issues Register here: https://lnkd.in/eBi4YEgc In the end, we all want our bills paid, our customers happy, and our AI trustworthy. Let's make it happen together by taking care of our data quality first.

The best piece of advice I ever received? Don’t listen to advice. Listen to your customers. That ethos has helped me both build a company - Monte Carlo - and a category - data observability as we know it today. Of course, like all "advice," it's easier said than done, particularly when the FOMO hits. But FOMO doesn't provide constructive product feedback or speak on behalf of your vision to future customers. FOMO doesn't build a category with you or help you solve real world problems. In my recent interview with Jyoti Bansal for the CEO: Rapidfire newsletter, I share some of my perspectives on FOMO, leadership, company building, and category creation. Check out the newsletter to learn more. Thanks, Jyoti Bansal, for the opportunity - had a blast! https://lnkd.in/g9MmaeMH

As a data analyst, delivering trusted insights to your stakeholders can cost a lot of time – and maybe even some sleep. 💤 Many analysts lose hours profiling their data, identifying thresholds, creating manual rules, and following up on data quality issues — all to make sure the data products they deliver meet their data quality standards. But, every hour that’s spent on data quality is another hour that can’t be spent delivering value. So, what’s a data analyst to do? We rounded up five ways you can take data quality off your endless to do list and make it a little less painful, including: ✅ Automating test creation for baseline data quality rules ✅ Leveraging AI-powered monitor suggestions for business rules ✅ Deploying no-code validation tools ✅ Routing data quality alerts to your domain Slack channels ✅ Conducting regular data audits to prioritize coverage and deprecate unused tables Learn more here: https://lnkd.in/g7-8e_vx #dataanalytics #analyticsengineering #dataquality #dataobservability