#247 Barr Moses: Why Reliable Data is Key to Building Good AI Systems
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In this episode of Eye on AI, Craig Smith sits down with Barr Moses, Co-Founder & CEO of Monte Carlo, the pioneer of data and AI observability. Together, they explore the hidden force behind every great AI system: reliable, trustworthy data.
With AI adoption soaring across industries, companies now face a critical question: Can we trust the data feeding our models? Barr unpacks why data quality is more important than ever, how observability helps detect and resolve data issues, and why clean data—not access to GPT or Claude—is the real competitive moat in AI today.
What You'll Learn in This Episode:
Why access to AI models is no longer a competitive advantage
How Monte Carlo helps teams monitor complex data estates in real-time
The dangers of "data hallucinations" and how to prevent them
Real-world examples of data failures and their impact on AI outputs
The difference between data observability and explainability
Why legacy methods of data review no longer work in an AI-first world
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Craig Smith on X:https://x.com/craigss
Eye on A.I. on X: https://x.com/EyeOn_AI
(00:00) Intro
(01:08) How Monte Carlo Fixed Broken Data
(03:08) What Is Data & AI Observability?
(05:00) Structured vs Unstructured Data Monitoring
(08:48) How Monte Carlo Integrates Across Data Stacks
(13:35) Why Clean Data Is the New Competitive Advantage
(16:57) How Monte Carlo Uses AI Internally
(19:20) 4 Failure Points: Data, Systems, Code, Models
(23:08) Can Observability Detect Bias in Data?
(26:15) Why Data Quality Needs a Modern Definition
(29:22) Explosion of Data Tools & Monte Carlo's 50+ Integrations
(33:18) Data Observability vs Explainability
(36:18) Human Evaluation vs Automated Monitoring
(39:23) What Monte Carlo Looks Like for Users
(46:03) How Fast Can You Deploy Monte Carlo?
(51:56) Why Manual Data Checks No Longer Work
(53:26) The Future of AI Depends on Trustworthy Data
Quantifying The Return On Investment For Your Data Team
Summary
As businesses increasingly invest in technology and talent focused on data engineering and analytics, they want to know whether they are benefiting. So how do you calculate the return on investment for data? In this episode Barr Moses and Anna Filippova explore that question and provide useful exercises to start answering that in your company.
Announcements
Hello and welcome to the Data Engineering Podcast, the show about modern data management
Introducing RudderStack Profiles. RudderStack Profiles takes the SaaS guesswork and SQL grunt work out of building complete customer profiles so you can quickly ship actionable, enriched data to every downstream team. You specify the customer traits, then Profiles runs the joins and computations for you to create complete customer profiles. Get all of the details and try the new product today at dataengineeringpodcast.com/rudderstack
Your host is Tobias Macey and today I'm interviewing Barr Moses and Anna Filippova about how and whether to measure the ROI of your data team
Interview
Introduction
How did you get involved in the area of data management?
What are the typical motivations for measuring and tracking the ROI for a data team?
Who is responsible for collecting that information?
How is that information used and by whom?
What are some of the downsides/risks of tracking this metric? (law of unintended consequences)
What are the inputs to the number that constitutes the "investment"? infrastructure, payroll of employees on team, time spent working with other teams?
What are the aspects of data work and its impact on the business that complicate a calculation of the "return" that is generated?
How should teams think about measuring data team ROI?
What are some concrete ROI metrics data teams can use?
What level of detail is useful? What dimensions should be used for segmenting the calculations?
How can visibility into this ROI metric be best used to inform the priorities and project scopes of the team?
With so many tools in the modern data stack today, what is the role of technology in helping drive or measure this impact?
How do your respective solutions, Monte Carlo and dbt, help teams measure and scale data value?
With generative AI on the upswing of the hype cycle, what are the impacts that you see it having on data teams?
What are the unrealistic expectations that it will produce?
How can it speed up time to delivery?
What are the most interesting, innovative, or unexpected ways that you have seen data team ROI calculated and/or used?
What are the most interesting, unexpected, or challenging lessons that you have learned while working on measuring the ROI of data teams?
When is measuring ROI the wrong choice?
Contact Info
Barr
LinkedIn
Anna
LinkedIn
Parting Question
From your perspective, what is the biggest gap in the tooling or technology for data management today?
Closing Announcements
Thank you for listening! Don't forget to check out our other shows. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used. The Machine Learning Podcast helps you go from idea to production with machine learning.
Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.
If you've learned something or tried out a project from the show then tell us about it! Email hosts@dataengineeringpodcast.com) with your story.
To help other people find the show please leave a review on Apple Podcasts and tell your friends and co-workers
Links
Monte Carlo
Podcast Episode
dbt
Podcast Episode
JetBlue Snowflake Con Presentation
Generative AI
Large Language Models
The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA
Sponsored By:
Rudderstack: 
Introducing RudderStack Profiles. RudderStack Profiles takes the SaaS guesswork and SQL grunt work out of building complete customer profiles so you can quickly ship actionable, enriched data to every downstream team. You specify the customer traits, then Profiles runs the joins and computations for you to create complete customer profiles. Get all of the details and try the new product today at [dataengineeringpodcast.com/rudderstack](https://www.dataengineeringpodcast.com/rudderstack)
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A Reflection On Data Observability As It Reaches Broader Adoption
Summary
Data observability is a product category that has seen massive growth and adoption in recent years. Monte Carlo is in the vanguard of companies who have been enabling data teams to observe and understand their complex data systems. In this episode founders Barr Moses and Lior Gavish rejoin the show to reflect on the evolution and adoption of data observability technologies and the capabilities that are being introduced as the broader ecosystem adopts the practices.
Announcements
Hello and welcome to the Data Engineering Podcast, the show about modern data management
When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode. With their new managed database service you can launch a production ready MySQL, Postgres, or MongoDB cluster in minutes, with automated backups, 40 Gbps connections from your application hosts, and high throughput SSDs. Go to dataengineeringpodcast.com/linode today and get a $100 credit to launch a database, create a Kubernetes cluster, or take advantage of all of their other services. And don’t forget to thank them for their continued support of this show!
Atlan is the metadata hub for your data ecosystem. Instead of locking your metadata into a new silo, unleash its transformative potential with Atlan’s active metadata capabilities. Push information about data freshness and quality to your business intelligence, automatically scale up and down your warehouse based on usage patterns, and let the bots answer those questions in Slack so that the humans can focus on delivering real value. Go to dataengineeringpodcast.com/atlan today to learn more about how Atlan’s active metadata platform is helping pioneering data teams like Postman, Plaid, WeWork & Unilever achieve extraordinary things with metadata and escape the chaos.
RudderStack helps you build a customer data platform on your warehouse or data lake. Instead of trapping data in a black box, they enable you to easily collect customer data from the entire stack and build an identity graph on your warehouse, giving you full visibility and control. Their SDKs make event streaming from any app or website easy, and their state-of-the-art reverse ETL pipelines enable you to send enriched data to any cloud tool. Sign up free… or just get the free t-shirt for being a listener of the Data Engineering Podcast at dataengineeringpodcast.com/rudder.
The only thing worse than having bad data is not knowing that you have it. With Bigeye’s data observability platform, if there is an issue with your data or data pipelines you’ll know right away and can get it fixed before the business is impacted. Bigeye let’s data teams measure, improve, and communicate the quality of your data to company stakeholders. With complete API access, a user-friendly interface, and automated yet flexible alerting, you’ve got everything you need to establish and maintain trust in your data. Go to dataengineeringpodcast.com/bigeye today to sign up and start trusting your analyses.
Your host is Tobias Macey and today I’m interviewing Barr Moses and Lior Gavish about the state of the market for data observability and their own work at Monte Carlo
Interview
Introduction
How did you get involved in the area of data management?
Can you give the elevator pitch for Monte Carlo?
What are the notable changes in the Monte Carlo product and business since our last conversation in October 2020?
You were one of the early entrants in the market of data quality/data observability products. In your work to gain visibility and traction you invested substantially in content creation (blog posts, presentations, round table conversations, etc.). How would you summarize the focus of your initial efforts?
Why do you think data observability has really taken off? A few years ago, the category barely existed – what’s changed?
There’s a larger debate within the data engineering community regarding whether it makes sense to go deep or go broad when it comes to monitoring your data. In other words, do you start with a few important data sets, or do you attempt to cover the entire ecosystem. What is your take?
For engineers and teams who are just now investigating and investing in observability/quality automation for their data, what are their motivations?
How has the conversation around the value/motivating factors matured or changed over the past couple of years?
In what way have the requirements and capabilities of data observability platforms shifted?
What are the forces in the ecosystem that have driven those changes?
How has the scope and vision for your work at Monte Carlo evolved as the understanding and impact of data quality have become more widespread?
When teams invest in data quality/observability what are some of the ways that the insights gained influence their other priorities and design choices? (e.g. platform design, pipeline design, data usage, etc.)
When it comes to selecting what parts of the data stack to invest in, how do data leaders prioritize? For instance, when does it make sense to build or buy a data catalog? A data observability platform?
The adoption of any tool that adds constraints is a delicate balance. What have you found to be the predominant patterns for teams who are incorporating Monte Carlo? (e.g. maintaining delivery velocity and adding safety/trust)
A corollary to the goal of data engineers for higher reliability and visibility is the need by the business/team leadership to identify "return on investment". How do you and your customers think about the useful metrics and measurement goals to justify the time spent on "non-functional" requirements?
What are the most interesting, innovative, or unexpected ways that you have seen Monte Carlo used?
What are the most interesting, unexpected, or challenging lessons that you have learned while working on Monte Carlo?
When is Monte Carlo the wrong choice?
What do you have planned for the future of Monte Carlo?
Contact Info
Barr
LinkedIn
@BM_DataDowntime on Twitter
Lior
LinkedIn
@lgavish on Twitter
Parting Question
From your perspective, what is the biggest gap in the tooling or technology for data management today?
Closing Announcements
Thank you for listening! Don’t forget to check out our other shows. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used. The Machine Learning Podcast helps you go from idea to production with machine learning.
Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.
If you’ve learned something or tried out a project from the show then tell us about it! Email hosts@dataengineeringpodcast.com) with your story.
To help other people find the show please leave a review on Apple Podcasts and tell your friends and co-workers
Links
Monte Carlo
Podcast Episode
App Dynamics
Datadog
New Relic
Data Quality Fundamentals book
State Of Data Quality Survey
dbt
Podcast Episode
Airflow
Dagster
Podcast Episode
Episode: Incident Management For Data Teams
Databricks Delta
Patch.tech Snowflake APIs
Hightouch
Podcast Episode
The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA
Support Data Engineering Podcast
499: Data Meshes and Data Reliability
Barr Moses joins us to discuss the importance of data reliability for pipelines and how companies can achieve data mesh.
In this episode you will learn:
Data meshes [4:25]
Self-serve data reliability [15:36]
How Monte Carlo helps data up time [21:13]
How to build an effective data science team [26:50]
LinkedIn Q&A [31:50]
Additional materials: www.superdatascience.com/499
Better Data Quality Through Observability With Monte Carlo
Summary
In order for analytics and machine learning projects to be useful, they require a high degree of data quality. To ensure that your pipelines are healthy you need a way to make them observable. In this episode Barr Moses and Lior Gavish, co-founders of Monte Carlo, share the leading causes of what they refer to as data downtime and how it manifests. They also discuss methods for gaining visibility into the flow of data through your infrastructure, how to diagnose and prevent potential problems, and what they are building at Monte Carlo to help you maintain your data’s uptime.
Announcements
Hello and welcome to the Data Engineering Podcast, the show about modern data management
What are the pieces of advice that you wish you had received early in your career of data engineering? If you hand a book to a new data engineer, what wisdom would you add to it? I’m working with O’Reilly on a project to collect the 97 things that every data engineer should know, and I need your help. Go to dataengineeringpodcast.com/97things to add your voice and share your hard-earned expertise.
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Your host is Tobias Macey and today I’m interviewing Barr Moses and Lior Gavish about observability for your data pipelines and how they are addressing it at Monte Carlo.
Interview
Introduction
How did you get involved in the area of data management?
How did you come up with the idea to found Monte Carlo?
What is "data downtime"?
Can you start by giving your definition of observability in the context of data workflows?
What are some of the contributing factors that lead to poor data quality at the different stages of the lifecycle?
Monitoring and observability of infrastructure and software applications is a well understood problem. In what ways does observability of data applications differ from "traditional" software systems?
What are some of the metrics or signals that we should be looking at to identify problems in our data applications?
Why is this the year that so many companies are working to address the issue of data quality and observability?
How are you addressing the challenge of bringing observability to data platforms at Monte Carlo?
What are the areas of integration that you are targeting and how did you identify where to prioritize your efforts?
For someone who is using Monte Carlo, how does the platform help them to identify and resolve issues in their data?
What stage of the data lifecycle have you found to be the biggest contributor to downtime and quality issues?
What are the most challenging systems, platforms, or tool chains to gain visibility into?
What are some of the most interesting, innovative, or unexpected ways that you have seen teams address their observability needs?
What are the most interesting, unexpected, or challenging lessons that you have learned while building the business and technology of Monte Carlo?
What are the alternatives to Monte Carlo?
What do you have planned for the future of the platform?
Contact Info
Visit www.montecarlodata.com?utm_source=rss&utm_medium=rss to lean more about our data reliability platform;
Or reach out directly to barr@montecarlodata.com — happy to chat about all things data!
Parting Question
From your perspective, what is the biggest gap in the tooling or technology for data management today?
Links
Monte Carlo
Monte Carlo Platform
Observability
Gainsight
Barracuda Networks
DevOps
New Relic
Datadog
Netflix RAD Outlier Detection
The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA
Support Data Engineering Podcast