Podcasts about barr moses

Barr Moses, CEO & Co-Founder of Monte Carlo, challenges the notion that models alone create competitive advantage, arguing instead that the real moat lies in how organizations manage their proprietary data and ensure end-to-end reliability. Tim and Juan chat with Barr to get the Honest, No-BS scoop of what AI observability is (hint, it's really data + AI) and how organizations can build resilient AI applications.

Barr Moses, CEO & Co-Founder of Monte Carlo, challenges the notion that models alone create competitive advantage, arguing instead that the real moat lies in how organizations manage their proprietary data and ensure end-to-end reliability. Tim and Juan chat with Barr to get the Honest, No-BS scoop of what AI observability is (hint, it's really data + AI) and how organizations can build resilient AI applications.

This episode is sponsored by Netsuite by Oracle, the number one cloud financial system, streamlining accounting, financial management, inventory, HR, and more.   NetSuite is offering a one-of-a-kind flexible financing program. Head to  https://netsuite.com/EYEONAI to know more.   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 Stay Updated: 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 

On this episode of Data Driven, we welcome Barr Moses, CEO and co-founder of Monte Carlo, as she delves into the fascinating world of data observability. Join hosts Frank La Vigne and Andy Leonard as they explore how reliable data is crucial for making sound business decisions in today's tech-driven world. Learn why a simple schema change at Unity resulted in a $100 million loss and how Monte Carlo is developing cutting-edge solutions to prevent similar disasters. From discussions on ensuring data integrity to the intriguing potential of AI in anomaly detection, Barr Moses shares insights that might just redefine your understanding of data's role in business. Tune in for a podcast that not only uncovers the nuances of data reliability but also touches on the quirky side of tech, like why, according to Google, you should never use superglue to fix slipping cheese on your pizza.Moments00:00 Monte Carlo: Data Reliability Innovator05:45 "Data & AI Observability Engineering"09:42 Data Industry's Growing Importance12:00 Cereal Supply Chain Data Optimization16:03 Data Observability and Lineage19:29 GenAI Uncertainties and Latency Concerns23:17 "Human Oversight in AI Accuracy"24:12 Data Observability and Human Role28:01 Adapting to Customer Language33:29 Data and Security Management Alignment35:20 Data Reliability and Observability Challenges38:17 Automated Code Analysis Tool Launch42:29 Data-Inspired Childhood44:12 Passionate About Impactful Work48:52 LinkedIn Security Concerns Highlighted53:19 "Data Observability Insights"

Join Barr Moses, CEO of Monte Carlo, as she discusses the crucial role of data and AI observability in building reliable AI products. She explains how enterprises can gain competitive advantage by leveraging their first-party data and implementing proper data quality monitoring systems. Moses highlights that regardless of industry, organizations face similar challenges with data reliability, which can be traced to four root causes: problems with the data itself, code issues, system failures, and model output errors.

Send Everyday AI and Jordan a text messageYour data is your moat. Everyone's got AI now. Find out how reliable data can make your competitive edge happen. Barr Moses, Co-Founder and CEO of Monte Carlo, joins us to discuss.Newsletter: Sign up for our free daily newsletterMore on this Episode: Episode PageJoin the discussion: Ask Jordan and Barr questions on AI and dataUpcoming Episodes: Check out the upcoming Everyday AI Livestream lineupWebsite: YourEverydayAI.comEmail The Show: info@youreverydayai.comConnect with Jordan on LinkedInTopics Covered in This Episode:1. the Importance of Data2. Challenges and Opportunities in Leveraging Data3. Adoption of Data Practices4. Data Use Case Examples5.Generative AI, LLMs, and Data IntegrationTimestamps:00:00 Empower AI proficiency with daily insights.06:02 Data observability ensures reliability and issue resolution.07:15 Understanding data's importance is crucial for businesses.13:07 Personalized AI relies on unique enterprise data.15:20 Large enterprises struggle with data consistency, smaller teams advantage.19:42 Generative AI analyzes sports data for insights.22:56 Personalized financial products using reliable data.23:56 Credit Karma Intune boosts external and internal productivity.28:02 Peak data reached; synthetic data becomes crucial.30:36 Recap available on your everydayai.com.Keywords:Generative AI, Data Usage, Data Accuracy, High-Quality Data, AI Implementation, Brand Reputation, Small Business Data Management, Data Systems, Trusting Data Sources, Everyday AI Podcast, Microsoft Partnership, Barr Moses, Monte Carlo, Data Downtime, Data Issues, Data Products, Data Observability, Data Adoption Forecast, Smaller Team Advantages, Microsoft WorkLab Podcast, Data Quality Monitor Recommendations, AI and Data Integration, Personalized Financial Products, Coding Assistants, AI for Compliance Reporting, Large Language Models, Synthetic Data, Real-World Data, Data Governance, Data Quality Management. Ready for ROI on GenAI? Go to youreverydayai.com/partner

Send Everyday AI and Jordan a text messageYour data is your moat. Everyone's got AI now. Find out how reliable data can make your competitive edge happen. Barr Moses, Co-Founder and CEO of Monte Carlo, joins us to discuss. Newsletter: Sign up for our free daily newsletterMore on this Episode: Episode PageJoin the discussion: Ask Jordan and Barr questions on AI and dataUpcoming Episodes: Check out the upcoming Everyday AI Livestream lineupWebsite: YourEverydayAI.comEmail The Show: info@youreverydayai.comConnect with Jordan on LinkedInTopics Covered in This Episode:1. the Importance of Data2. Challenges and Opportunities in Leveraging Data3. Adoption of Data Practices4. Data Use Case Examples5.Generative AI, LLMs, and Data IntegrationTimestamps:00:00 Empower AI proficiency with daily insights.06:02 Data observability ensures reliability and issue resolution.07:15 Understanding data's importance is crucial for businesses.13:07 Personalized AI relies on unique enterprise data.15:20 Large enterprises struggle with data consistency, smaller teams advantage.19:42 Generative AI analyzes sports data for insights.22:56 Personalized financial products using reliable data.23:56 Credit Karma Intune boosts external and internal productivity.28:02 Peak data reached; synthetic data becomes crucial.30:36 Recap available on your everydayai.com.Keywords:Generative AI, Data Usage, Data Accuracy, High-Quality Data, AI Implementation, Brand Reputation, Small Business Data Management, Data Systems, Trusting Data Sources, Everyday AI Podcast, Microsoft Partnership, Barr Moses, Monte Carlo, Data Downtime, Data Issues, Data Products, Data Observability, Data Adoption Forecast, Smaller Team Advantages, Microsoft WorkLab Podcast, Data Quality Monitor Recommendations, AI and Data Integration, Personalized Financial Products, Coding Assistants, AI for Compliance Reporting, Large Language Models, Synthetic Data, Real-World Data, Data Governance, Data Quality Management.

Every year kicks off with an air of expectation. How much of our Professional Life in 2025 is going to look a lot like 2024? How much will look different, but we have a pretty good idea of what the difference will be? What will surprise us entirely—the unknown unknowns? By definition, that last one is unknowable. But we thought it would be fun to sit down with returning guest Barr Moses from Monte Carlo to see what we could nail down anyway. The result? A pretty wide-ranging discussion about data observability, data completeness vs. data connectedness, structured data vs. unstructured data, and where AI sits from an input and an output and a processing engine. And more. Moe and Tim even briefly saw eye to eye on a thing or two (although maybe that was just a hallucination). For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

Key Moments: Focusing on Value with Bill Schmarzo 1:48Unlocking the Collective Genius with Walid Mehanna 4:07Building a Data-Literate Workforce with Valerie Logan 5:58Creating a Human-Centric AI Strategy with Sadie St. Lawrence 7:40Selecting the Right Tools with Katie Russell 11:23Implementing tools responsibly with Robert Garnett 16:00Why Clean Data Matters with Barr Moses 19:36Ensuring Responsible AI for the Long-Term with Dr. Gary Marcus 25:45 Key Quotes:“Data-driven is not important. Value-driven—that's what's important. We should focus on value.” — Bill Schmarzo, Head of Customer Data Innovation at Dell Technologies“Our role was rather to activate the organizational muscle… to try things out and tell us what has the highest opportunity and possibility.” — Walid Mehanna, Chief Data and AI Officer at Merck Group“It's really a mindset and a muscle… we need to foster this kind of lasting change.” — Valerie Logan, CEO of the Datalodge“Teaching people to ask better questions is more about critical thinking than technology.” — Sadie St. Lawrence, Founder of the Human Machine Collaboration Institute“We wanted to make analytics accessible to everyone, combining real-time data and intuitive tools so every team member can gain insights and contribute to our mission to decarbonize.” — Katie Russell, Head of Data and Analytics at OVO Energy As we are looking at applications of AI within our environment, we are focused first on responsibility, making sure that we have a broad enough data set when we're building machine learning models, for instance. And so that's at the heart of anything that we do.” – Robert Garnett, Vice President for Government Analytics and Health Benefits Cost of Care at Elevance Health“Our world is moving towards a place where data is the product—and in that world, directionally accurate just doesn't cut it anymore.” — Barr Moses, CEO and Co-Founder of Monte Carlo“The tech policy that we set right now is going to really affect the rest of our lives.” —  Dr. Gary Marcus, Scientist, Advisor to Governments and Corporations, and Author of Taming Silicon ValleyGuest Bios Bill Schmarzo Bill Schmarzo has extensive hands-on experience in the areas of big data, data science, designthinking, data monetization, and data economics. Bill is currently part of Dell Technology's core data management leadership team, where he is responsible for spearheading customer co-creation engagement to identify and prioritize the key data management, data science, and data monetization requirements.Walid MehannaWalid Mehanna is Chief Data & AI Officer at Merck KGaA, Darmstadt, Germany, where he leads the company's Data & AI organization, delivering value, governance, architecture, engineering, and operations across the company globally. With many years experience in startups, IT, and consulting major corporations, Walid encompasses a strong understanding of the intersection between business and technology. Katie RussellKatie Russell is the Data Director at OVO Energy, leading teams of Data Scientists, Data Engineers and Analysts who are transforming OVO's data capability. As part of a technology led business, leveraging data using artificial intelligence keeps OVO truly innovative, delivering the best possible service for our customers. Rob GarnettRobert Garnett serves as Vice President for Government Analytics and Health Benefits Cost of Care at Elevance Health. In this role, he leads a data-driven organization supporting analytics and insights for Medicaid, Medicare, Commercial and enterprise customers in the areas of population health, cost of care, performance management, operational excellence, and quality improvement. Valerie LoganFounding The Data Lodge in 2019, Valerie is as committed to data literacy as it gets. With train-the-trainer bootcamps, and a peer community, she's certifying the world's first Data Literacy Program Leads. In 2023, The Data Lodge was acquired as the basis of a newly formed venture, Data Society Group (DSG), aimed at fostering data and AI literacy and cultural change at scale. Valerie is excited to also serve as the Chief Strategy Officer of DSG. Previously, Valerie was a Gartner Research VP in the CDO team where she pioneered Data Literacy research and was awarded Gartner's Top Thought Leadership Award.Sadie St. LawrenceSadie St. Lawrence  is on a personal mission to create a more compassionate and connected world through technology. Having grown up on a farm in Iowa she witnessed first-hand how advancements in technology rapidly changed how we work and earn a living, which in turn affected the overall success of a community. Through her work, she noticed that while many organizations and individuals have good intentions when it comes to D&I in data careers, there was a lack of progress.Dr. Gary MarcusGary Marcus is a leading voice in artificial intelligence. He is a scientist, best-selling author, and serial entrepreneur (Founder of Robust.AI and Geometric.AI, acquired by Uber). He is well-known for his challenges to contemporary AI, anticipating many of the current limitations decades in advance, and for his research in human language development and cognitive neuroscience. An Emeritus Professor of Psychology and Neural Science at NYU, he is the author of six books. Hear more from Cindi Howson here. Sponsored by ThoughtSpot.

Key Moments:Why is the data wrong? (6:00)Our products are our data (11:00)The true size of the data quality problem (14:00)Clean your data before you prioritize shiny new tools (26:00)The next frontier: GenAI and unstructured data (31:00)Key Quotes:“The data estate has changed significantly. But the way in which we manage data and data quality specifically has not adapted.” – Barr Moses“I tracked every single change in the data that I made, and could calculate how much money a company saved after a data cleanup. For a mid-size company, the difference was approximately a quarter of a billion dollars. For a large company, it could be several billion dollars. 45% of the data I cleaned had errors.” – Olga Maydanchik“The competitive advantage is really the access to your proprietary data that you have as an enterprise. So you need to make sure that that data is accurate, reliable, and on time. Now, how do you do that? That's something that people are still figuring out.”  – Barr MosesMentions:Information Quality Applied: Best Practices for Improving Business Information, Outcomes and Systems: Book by Larry EnglishThe Rest is History PodcastFreakonomics PodcastThe Matrix Film SeriesThe Play That Goes WrongBio: Barr Moses: Barr Moses is the CEO and Co-Founder of Monte Carlo, the data reliability company. Monte Carlo is the creator of the industry's first end-to-end Data Observability platform. She is also co-author of O'Reilly's Data Quality Fundamentals: Building Reliable Data Pipelines. Previously, she was VP Customer Operations at Gainsight, a management consultant at Bain & Company and served in the Israeli Air Force as a commander of an intelligence data analyst unit.Olga Maydanchik:Olga Maydanchik is a data governance, data quality, and data architecture thought leader and practitioner. She is an expert in design and implementation of enterprise-wide data management programs, who has led data quality efforts at Deutsche Bank, AIG, and at Citi. Hear more from Cindi Howson here. Sponsored by ThoughtSpot.

Companies' decision-making is increasingly driven by data, with the quality of that data critical, especially in AI-driven applications. In this episode of the Tech Disruptors podcast, Barr Moses, founder and CEO of Monte Carlo, joins Sunil Rajgopal, senior software analyst at Bloomberg Intelligence, to discuss the intricacies of data-dependent intelligence and how data-observability solutions can be deployed to proactively monitor, identify and resolve data incidents. The two also talk about importance of data quality and testing tools, Monte Carlo's platform, its business model, competitive landscape and the importance of trusted data for generative AI use cases.

Monte Carlo is an end-to-end data observability platform that monitors pipelines for missing or inaccurate data. Last valued at $1.6B, Monte Carlo has over 150 customers, including data teams at companies such as CNN, JetBlue, Hubspot, PepsiCo, and Toast.  In this episode, Sandhya Hegde chats with Barr Moses, co-founder and CEO of Monte Carlo.  Join us as we discuss: 00:00 Preview —Early feedback from a potential customer 1:37 Why Barr and Lior decided to focus on data observability 6:28 Profile of Monte Carlo's early adopters 12:27 Common pain points among early adopters 16:05 How Monte Carlo narrowed in on their ICP 21: 33 Early product feedback and surprises 26:27 Monte Carlo's current go-to-market strategy 31:22 How AI will impact the data ecosystem 35:57 Advice for founder building data platforms Sandhya Hegde is a General Partner at Unusual Ventures, leading investments in modern SaaS companies with a focus on AI. Previously an early executive at Amplitude, Sandhya is a product-led growth (PLG) coach and mentor. She can be reached at sandhya@unusual.vc. Barr Moses is the founder and CEO of Monte Carlo Unusual Ventures is a seed-stage venture capital firm designed from the ground up to give a distinct advantage to founders building the next generation of software companies. Unusual has invested in category-defining companies like Webflow, Arctic Wolf Networks, Carta, Robinhood, and Harness. Learn more about us at https://www.unusual.vc/.

In this episode, we welcome Barr Moses, the Co-Founder & CEO of Monte Carlo, a data reliability company and creator of the industry's first Data Observability Platform. Barr and I discuss the importance of speed; not just speed for the sake of moving fast, but focused, intentional speed. We talk about how it's helped her grow and scale Monte Carlo and how she's put structures in place to help her team move at speed and take the time to reflect, reevaluate, and move forward even faster. Lastly, Barr leaves us with probably the most important nugget of all about authenticity. You'll have to listen to the end to hear that one.

“This problem was so painful and so meaningful to people that I just couldn't believe a world where a solution to this didn't exist.” When Barr Moses identified the very costly problem of what she coined “data downtime”, she knew she needed to solve it. Barr is the Founder and CEO of Monte Carlo, a data observability platform that's on a mission to eliminate data downtime, a problem that can cost companies millions of dollars each time there's an outage and the numbers — and the systems that rely on them — go haywire. And with the growth of AI, data problems are even more important to prevent. On this episode, Barr explains how she used the scientific method to home in on the problem to solve and the company to found — she actually launched three companies simultaneously before seeing the most traction with Monte Carlo, and going all in. We also learn about Monte Carlo's customer-led approach that helped them create an end-to-end solution that leaves no data stone unturned.This is Crafted from Artium: a show about great products, and the people who make them.  At Artium, we help startups and enterprises build incredible products, recruit high-performing teams, and achieve the culture of craft they need to build great software long after we're gone. Check us out at thisisartium.com

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 (https://www.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 (https://www.linkedin.com/in/barrmoses/) Anna LinkedIn (https://www.linkedin.com/in/annafilippova) 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__ (https://www.pythonpodcast.com) covers the Python language, its community, and the innovative ways it is being used. The Machine Learning Podcast (https://www.themachinelearningpodcast.com) helps you go from idea to production with machine learning. Visit the site (https://www.dataengineeringpodcast.com) 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 (mailto:hosts@dataengineeringpodcast.com)) with your story. To help other people find the show please leave a review on Apple Podcasts (https://podcasts.apple.com/us/podcast/data-engineering-podcast/id1193040557) and tell your friends and co-workers Links Monte Carlo (https://www.montecarlodata.com/) Podcast Episode (https://www.dataengineeringpodcast.com/monte-carlo-observability-data-quality-episode-155) dbt (https://www.getdbt.com/) Podcast Episode (https://www.dataengineeringpodcast.com/dbt-data-analytics-episode-81) JetBlue Snowflake Con Presentation (https://www.snowflake.com/webinar/thought-leadership/jet-blue-and-monte-carlos/) Generative AI (https://generativeai.net/) Large Language Models (https://en.wikipedia.org/wiki/Large_language_model) The intro and outro music is from The Hug (http://freemusicarchive.org/music/The_Freak_Fandango_Orchestra/Love_death_and_a_drunken_monkey/04_-_The_Hug) by The Freak Fandango Orchestra (http://freemusicarchive.org/music/The_Freak_Fandango_Orchestra/) / CC BY-SA (http://creativecommons.org/licenses/by-sa/3.0/)

As companies depend more on data to improve digital products and make informed decisions, it's crucial that the data they use be accurate and reliable. MonteCarlo, the data reliability company, is the creator of the industry's first end-to-end data observability platform. Barr Moses and Lior Gavish are the founders of Monte Carlo and they join The post Data Reliability with Barr Moses and Lior Gavish appeared first on Software Engineering Daily.

As companies depend more on data to improve digital products and make informed decisions, it’s crucial that the data they use be accurate and reliable. MonteCarlo, the data reliability company, is the creator of the industry’s first end-to-end data observability platform. Barr Moses and Lior Gavish are the founders of Monte Carlo and they join The post Data Reliability with Barr Moses and Lior Gavish appeared first on Software Engineering Daily.

Barr Moses is a consultant turned CEO & Co-Founder of Monte Carlo, a data reliability company. She started her career as a management consultant at Bain & Company and a research assistant at the Statistics Department at Stanford University. Later, she became VP of Customer Operations at customer success company Gainsight, where she built the data and analytics team. She also served in the Israeli Air Force as a commander of an intelligence data analyst unit. Barr graduated from Stanford with a B.Sc. in Mathematical and Computational Science. Today, we'll talk about Barr's career journey, data reliability and observability, and what it means for data teams. If you enjoy the show, subscribe to the channel and leave a 5-star review. Subscribe to Daliana's newsletter on www.dalianaliu.com for more on data science. Barr's LinkedIn: https://www.linkedin.com/in/barrmoses/ Daliana's Twitter: https://twitter.com/DalianaLiu Daliana's LinkedIn: https://www.linkedin.com/in/dalianaliu (00:00:00) Introduction (00:01:24) How did she got into data science (00:08:26) Frameworks for data-driven decisions (00:11:20) Is customer support ticket always bad? (00:15:20) How to quickly find out what is true (00:20:17) Struggles in the data team (00:23:37) Daliana's story about lineage (00:28:00) People stressed about data (00:28:09) Netflix was down because of wrong data (00:30:40) Common issues with data quality (00:33:14) 5 pillars of data observability (00:39:14) How does Monte Carlo help data scientists (00:43:08) Build in-house vs adopt tools (00:45:48) How Daliana fixed a data quality issue (01:02:44) How to measure the impact of the data team (01:09:09) Mistakes she made (01:15:28) Beat the odds

In today's episode of Category Visionaries, we speak with Barr Moses, Co-Founder & CEO of Monte Carlo, a data observability platform that's raised over $230 Million in funding, about why quickly spotting problems in big commercial data can be the difference between a swift resolution or a costly correction later on. By providing data teams with the tools they need to keep up to date with what's going on with their data, Monte Carlo gives them a headstart in resolving, and sometimes even preventing dangerous errors before they can cause major problems. We also speak about Barr's background in the Israeli military, the lessons she learned and brought forward to the world of business, why communication with potential clients was at the heart of the Monte Carlo strategy, how happy Barr is to see the data observability category establishing itself in the marketplace, and why her biggest business inspiration might just be her own Mother. Topics Discussed: Barr's background in the Israeli military, and the lessons in analytics and dealing with challenges that she brought to the world of business How Barr's Mother became her business inspiration from a young age, and what she learned from watching her business journey Why it can be easy to end up making content only for yourself, and how important it is to communicate with your potential clients to know what's really going on Why Monte Carlo's focus is on getting as many customers as possible and making them as happy as they possibly can Why Barr spends time on podcasts, at speaking events, and writing blogs to share the concept behind his new business category The data observability category and why Barr is so thrilled to see it establishing itself

Our guest today is Barr Moses, Co-Founder & CEO of Monte Carlo, the first end-to-end data observability platform. In our conversation, we first talk about how Barr got into the field and the early influence of her parents. Barr shares her previous experiences working with data in the Israeli Army and working on data strategy at Bain. We then dig into Monte Carlo and the new field of DataOps along with data observability and its 5 pillars . Barr explains how and why she founded this company and walks us through the key challenges she faced. If you enjoyed the episode, please leave a 5 star review and subscribe to the AI Stories Youtube channel.To learn more about Monte Carlo: https://www.montecarlodata.com/Follow Barr on LinkedIn: https://www.linkedin.com/in/barrmoses/Follow Neil on LinkedIn: https://www.linkedin.com/in/leiserneil/  ————(00:00) : Intro(01:18) : How Barr got into Data Science(03:09) : Data in the Israeli Army (08:24) : Influence from her parents(11:54) : Data Strategy and consulting at Bain(19:10) : How to quickly become an expert(25:30) : What is Monte Carlo(32:00) : DataOps & 5 pillars of data observability(43:54) : Challenges when building a tech company(49:57) : Mistakes and career advice

Overview Barr Moses, Co-Founder and CEO of Monte Carlo, joins on the podcast. Prior to founding Monte Carlo, Barr was the VP of customer success operations at Gainsight and holds a bachelors of science in mathematics and computer science from Stanford. In today's episode Barr shares her inspiration for founding Monte Carlo, the cost and harms of poor quality data, the five principles of data observability, and her top predictions for data trends in 2023. About Barr Moses Barr Moses is CEO & Co-Founder of Monte Carlo, a data reliability company and creator of the data observability category, backed by Accel, GGV, Redpoint, ICONIQ Growth, Salesforce Ventures, IVP, and other top Silicon Valley investors. Previously, she was VP Customer Operations at customer success company Gainsight, where she helped scale the company 10x in revenue and, among other functions, built the data/analytics team. Prior to that, she was a management consultant at Bain & Company and a research assistant at the Statistics Department at Stanford University. She also served in the Israeli Air Force as a commander of an intelligence data analyst unit. Barr graduated from Stanford with a B.Sc. in Mathematical and Computational Science. Social Handles LinkedIn Barr's Twitter Monte Carlo's Twitter Learn more about our mission and become a member here: https://www.womenindata.org/ --- Support this podcast: https://anchor.fm/women-in-data/support

“I had one job, which was to get the data right. Why was that so freaking hard?” asked Barr Moses, co-founder and CEO of Monte Carlo, the world's first data observability platform, discussing what motivated her to create the product. Having worked with data for 15 years, she realized so many people across the industry couldn't seem to get it right, nor did they have a systematic, scalable way to make sure data was accurate. In the world we're living in, where so many people have access to data, just a few minutes of inaccurate data can lead to poor customer experience and millions of dollars in lost revenue. It's a problem Barr says will only get worse over time, as data becomes more important to infrastructure.  Barr explains what it was like to create a whole new category, something from nothing, even when some people were telling her it would never work and that she was throwing her career away. She knew there was a company to be built there, and she wanted to be the one to do it and be proud of the journey along the way—which she admits is a lot of hard work. Category creation is really solving customer problems, and in so doing, the customer becomes co-creator of the category because they have the answers. Customer happiness is at the heart of the whole operation. Barr expands upon this and other codified values that make up the foundation of Monte Carlo. Barr reveals what the two main rules any business should have, from the beginning and forever. Find out why it's important that people around you pass “The Mom Test,” what the odds are that data will ever be 100% accurate, and what it's like to be married to your co-founder.  Quotes: “The idea of data being wrong would get a really strong reaction. It resonated. I think that was the first ‘aha' moment. People that I didn't even know would say, ‘Hell, yes, I have that problem, please help me solve it now. So that was the very first lightbulb moment.” (9:52-10:17 | Barr) “We're not looking for someone to say, ‘Hey we have 100 percent confidence.' We're looking for someone to say, ‘Hey, this data is important enough for us to invest something in making sure that it's accurate.' It's about treating the issue with the diligence it deserves." (15:53-16:07 | Barr) “Think about application reliability: A couple of decades ago, nobody cared if your app was up or down. But then Netflix is down for 45 minutes in 2016 because of duplicate data. Netflix being down is a hell of a problem.” (16:07-16:26 | Barr) “Customers don't give a shit about you creating a new category or not. They literally don't care. They care about, ‘Are you solving a real problem for me?' Helping people and solving their problem is way more important.” (32:58-33:20 | Barr) “Our measure of success isn't years or weeks, it's literally minutes. Every minute that you're spending on something should be high-value.” (39:50-40:00 | Barr)  Connect with Brendan Dell:  LinkedIn: https://www.linkedin.com/in/brendandell/ YouTube: https://www.youtube.com/c/BrendanDell Instagram: @thebrendandell TikTok: @brendandell39 Buy a copy of Brendan's Book, The 12 Immutable Laws of High-Impact Messaging: https://www.indiebound.org/book/9780578210926    Connect with Barr Moses: LinkedIn: @barrmoses barr@montecarlodata.com Check out Barr Moses recommended books: The Mom Test by Rob Fitzpatrick https://www.indiebound.org/search/book?keys=The+Mom+Test   Daring Greatly: How the Courage to Be Vulnerable Transforms the Way We Live, Love, Parent, and Lead by Brene Brown  https://www.indiebound.org/book/9781592408412   The Score Takes Care of Itself by Bill Walsh, Steve Jamison and Craig Walsh https://www.indiebound.org/book/9781591843474 Please don't forget to rate, comment, and subscribe to Billion Dollar Tech on Apple, Spotify, or wherever you listen to podcasts! Use code Brendan30 for 30% off your annual membership with RiverSide.fm  Podcast production and show notes provided by HiveCast.fm

Barr Moses, CEO of Monte Carlo explains the difference between data quality and data observability, and how to make sure your data is accurate in a world where so many different teams are accessing it.

Barr Moses, CEO of Monte Carlo explains the difference between data quality and data observability, and how to make sure your data is accurate in a world where so many different teams are accessing it.

Mona Akmal, CEO of sales intelligence platform Falkon, is the outspoken co-founder behind an emerging leader in a hot space. Mona migrated to the United States at age 20 with a CS degree and little else. She had an impressive 12-year run as a product leader at Microsoft where she helped scale OneDrive and Office. She subsequently led product and technology organizations at places like Code.org and Amperity. Two decades later, Mona's the CEO of Falkon AI, an intelligence platform for go to market teams. Falkon recently raised $16M from a group of A-list investors that includes Greylock and Madera among others.Listen and learn...Why Mona's philosophy revolves around two words: "efficiency" and "excellence"What makes a standout sales rep great.How do find signal in noisy sales and marketing dataHow many touches are required from stage one to closing a B2B dealHow to fix the CRM data hygiene problemWhy econometrics approaches perform better than machine learning to solve the "small data problem"Why "everyone needs to be coached and nobody needs to be managed"Mona's (legendary) mental health advice to entrepreneursReferences in this episode...Barr Moses from Monte Carlo on AI and the Future of WorkDerek Steer from Mode on AI and the Future of WorkPeter Fishman from Mozart Data on AI and the Future of WorkStephen Messer from  Collective[i] on AI and the Future of Work Kamal Ahluwalia on AI and the Future of WorkLeading scientists fear AI could lead to nuclear war by the end of the century

Welcome Barr Moses CEO & Co-Founder of Monte Carlo, a data reliability company.  We talk detection, resolution, prevention and the 5 pillars of data observability.   Great dialog.Show Notes03:53 : Zero defect data06:02 : Customer success learnings 10:42 : Delivering value with data14:00 : Monte Carlo, the name18:20 : Data observability23:38 : Detection, resolution, prevention31:12 : Duplicate data brings Netflix down34:22 : 5 pillars of data observability36:22 : "Building data like a product"39:37 : Monte Carlo's differentiationFind Barr : linkedin.com/in/barrmosesWebsite : https://www.montecarlodata.com/Want to be featured as a guest on Making Data Simple?  Reach out to us at almartintalksdata@gmail.com and tell us why you should be next.  The Making Data Simple Podcast is hosted by Al Martin, WW VP Technical Sales, IBM, where we explore trending technologies, business innovation, and leadership ... while keeping it simple & fun.

Welcome Barr Moses CEO & Co-Founder of Monte Carlo, a data reliability company.  We talk detection, resolution, prevention and the 5 pillars of data observability.   Great dialog.Show Notes03:53 : Zero defect data06:02 : Customer success learnings 10:42 : Delivering value with data14:00 : Monte Carlo, the name18:20 : Data observability23:38 : Detection, resolution, prevention31:12 : Duplicate data brings Netflix down34:22 : 5 pillars of data observability36:22 : "Building data like a product"39:37 : Monte Carlo's differentiationFind Barr : linkedin.com/in/barrmosesWebsite : https://www.montecarlodata.com/Want to be featured as a guest on Making Data Simple?  Reach out to us at almartintalksdata@gmail.com and tell us why you should be next.  The Making Data Simple Podcast is hosted by Al Martin, WW VP Technical Sales, IBM, where we explore trending technologies, business innovation, and leadership ... while keeping it simple & fun.

This bonus episode features conversations from season 2 of the Open||Source||Data podcast. In this episode, you'll hear from Barr Moses, Co-founder and CEO at Monte Carlo; Einat Orr, Co-founder and CEO at Treeverse; and Shinji Kim, Founder and CEO at Select Star.Sam sat down with each guest to discuss data observability. You can listen to the full episodes from Barr Moses, Einat Orr, and Shinji Kim by clicking the links below.-------------------Episode Timestamps:(00:35): Barr Moses(01:21): Einat Orr(02:07): Shinji Kim-------------------Links:Listen to Barr's episodeListen to Einat's episodeListen to Shinji's episode

Highlights from this week's conversation include:Barr's background and career journey (2:12)Trust: a technical or human problem? (9:47)Behind the name “Monte Carlo” (15:41)Defining data accuracy and reliability (17:36)How much can be done with standardization (22:27)How to avoid frustration when generating data about data (25:49)Defining “resolution” (28:59)Understanding the concept of SLAs (33:25)Building a company for a category that doesn't exist yet (37:40)What it looks like to use Monte Carlo (44:07)The best part about working with data teams (47:28)The Data Stack Show is a weekly podcast powered by RudderStack, the CDP for developers. Each week we'll talk to data engineers, analysts, and data scientists about their experience around building and maintaining data infrastructure, delivering data and data products, and driving better outcomes across their businesses with data.RudderStack helps businesses make the most out of their customer data while ensuring data privacy and security. To learn more about RudderStack visit rudderstack.com.

Eric and Kostas preview their upcoming conversation with Barr Moses of Monte Carlo.

Some of our topic highlights include:The history and background of Monte CarloWhy the planning process is so important for data teamsHow to set (and crush) a data goalThe difference between being obsessed with systems and being obsesses with goalsHow data teams waste timeHow often data downtime and data issues are tied to a lack of knowledge transfer and process within an orgHow we can continue to improve the corporate culture around dataAs always, I'd love to hear your thoughts on the episode over on Twitter @borisjabes.Want to discuss the best practices we covered in this episode? Come hang out in The Operational Analytics Club, where all your favorite data leaders gather. Know someone that you think would be an awesome guest on The Show (hint: you can totally nominate yourself)? Reach out to our content and community team. Resources:Barr Moses on LinkedIn Barr Moses on Twitter Barr Moses on MediumMonte Carlo on Twitter Monte Carlo on LinkedIn Monte Carlo's websiteMusic by the talented Joe Stevens. 

Monte Carlo's Co-Founder and CEO, Barr Moses, joins Coruzant Technologies for the Digital Executive podcast. She shares that her driving force is her curiosity. She loves to take on big goals and making big accomplishments. This path has led her to founding her data reliability company, Monte Carlo, and pioneering the data observability category. 

Peter Fishman ("Fish"), co-founder and CEO of Mozart Data, had a vision for making it easy for any business to unlock the value of their data via a modern data stack. He and his co-founder believe rote data engineering work shouldn't require teams of in-house data engineers. Fish turned his PhD in Economics and passion for statistics into a successful, venture-backed YC company that is defining the future of data analytics.Listen and learn...Why Fish believes "not every business gets value out of their data... but every business can."The role of data pipelines in automating the cleaning and transforming of data.Fish's prediction for where humans will be needed for data analysis in a decade.What Fish learned working with David Sacks at Yammer.How bacon hot sauce inspired the founding of Mozart Data.References in this episode:Barr Moses from Monte Carlo  on AI and the Future of WorkDerek Steer from Mode on AI and the Future of WorkFivetran for simplifying data integration

Do you want to learn more about Data Observability? In this episode Barr Moses, Co-Founder and CEO at Monte Carlo, talks about her journey, data observability, data quality, building data teams, data mesh architecture, and much more! #data #datascience #python #machinelearning #analytics #moderndatastack #ai #bi #artificialintelligence #dataobservability

Hashmap hosts, Chinmayee Lakkad and David Hrncir take a look back at some of our favorite moments from the past year of Hashmap on Tap episodes. This show is a great way to sample a variety of our podcast episodes with special guests exploring a wide range of data and cloud topics. Clip timestamps with Links to the Guest's Full Episode: TS 1:50 Tyler Wheatley from Waste Management TS 5:20 Richie Bachala from Sherwin Williams TS 10:10 Scottie Bryan from Hashmap TS 13:50 Boris Jabes from Census TS 17:20 George Fraser from Fivetran TS 22:30 Barr Moses from Monte Carlo TS 27:10 Tristan Handy from dbt Labs TS 32:13 Erik Jones from Hyperscience TS 37:25 Swaroop Jagadish from Acryl Data TS 41:22 Danielle Beringer from Gretel.ai TS 48:30 Ajay Bidani from Powell Industries TS 52:53 Anastasia Leng from CreativeX Show Notes: On Tap for today's episode: Peet's French Roast Organic & Honey and Chamomile Tea Contact Us: https://www.hashmapinc.com/reach-out

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

Key takeaways: Good data pipeline, “bad data” problem Investing in data observability - it will cost you one way or the other Being proactive vs reactive Data engineering and data product manager would be best to own observability Meet: Barr Moses is CEO & Co-Founder of Monte Carlo, a data reliability company backed by Accel, GGV, Redpoint, and other top Silicon Valley investors. Previously, she was VP Customer Operations at Gainsight, a management consultant at Bain & Company, and served in the Israeli Air Force as a commander of an intelligence data analyst unit. Barr graduated from Stanford with a B.Sc. in Mathematical and Computational Science. If you have any questions for Barr, please feel free to reach out via: https://www.linkedin.com/in/barrmoses/ https://twitter.com/BM_DataDowntime I hope you enjoyed the episode, the best place to connect with me is on Linkedin - https://www.linkedin.com/in/amirbormand (Amir Bormand). Please send me a message if you would like me to cover certain topics with future guests.

In this episode of DataFramed, Adel speaks with Barr Moses, CEO, and co-founder of Monte Carlo on the importance of data quality and how data observability creates trust in data throughout the organization. Throughout the episode, Barr talks about her background, the state of data-driven organizations and what it means to be data-driven, the data maturity of organizations, the importance of data quality, what data observability is, and why we'll hear about it more often in the future. She also covers the state of data infrastructure, data meshes, and more. Relevant links from the interview:Connect with Barr on LinkedInLearn more about data meshesCheck out the Monte Carlo blogDataCamp's Guide to Organizational Data Maturity

On this episode of DM Radio, Eric Kavanagh delves into Data Continuity with guests: Barr Moses, Monte Carlo Michael Ferranti, Portworx at Pure Storage Ranga Rajagopalan, Commvault JG Heithcock, Retrospect

With passion and authenticity in the foreground of any brand content, a business can resonate and engage its audience in a fruitful way. Our guest today, Nisha Baxi, was fuelled and captivated by the CEO of Monte Carlo, Barr Moses. As Barr exuded a genuine interest in data observability Nisha joined the team to increase brand visibility and leverage the founder's excitement in their content strategy. Monte Carlo is a data reliability software that connects all your data for quality control, data communication, and security for your peace of mind. Nisha joined the team ready to expose the brand and promote its growth. As the business had traction without a website, Nisha and the team wanted to review what role their website would play. With the CEO's digital content increasing customer reach there was no doubt that their content strategy would play a large role in client onboarding. With the audience's pain points addressed in a genuine format, prospects were eager to join this community. With a content strategy fuelled by the business's passion, prospects are encouraged through the customer journey, while their website can be used as a tool to indicate what works for the audience to create an optimal experience.

Barr Moses discusses with Sam about bringing DevOps into Data Engineering, building a data startup, and letting joy guide your way to creating impact. Learn how being data-driven depends on systems of people and trust. See omnystudio.com/listener for privacy information.

Barr Moses, Co-Founder & CEO of Monte Carlo, joins Hashmap on Tap host, Kelly Kohlleffel, for a chai tea and a discussion on data observability and its immense value. Monte Carlo is an ML-based data observability engine that helps enable greater trust in data by preventing and eliminating data downtime and increasing data reliability. Barr and the Monte Carlo team are helping customers solve this painful issue and she shares her experiences from when she first noticed the need for data observability to where the company is today. Show Notes: Monte Carlo: https://www.montecarlodata.com/ Check out Barr's blog post on data observability Read Barr's article on Forbes Connect with Barr on LinkedIn: https://www.linkedin.com/in/barrmoses/ Start your own conversation around modernizing your data stack: https://www.hashmapinc.com/workshop-dataintegration On tap for today's episode: Chai Tea!

We covered: Barr’s background Market gaps in data reliability Observability in engineering Data downtime Data quality problems and the five pillars of data observability Example: job failing because of a schema change Three pillars of observability (good pipelines and bad data) Observability vs monitoring Finding the root cause Who is accountable for data quality? (the RACI framework) Service level agreements Inferring the SLAs from the historical data Implementing data observability Data downtime maturity curve Monte carlo: data observability solution Open source tools Test-driven development for data Is data observability cloud agnostic? Centralizing data observability Detecting downstream and upstream data usage Getting bad data vs getting unusual data Links: Learn more about Monte Carlo: https://www.montecarlodata.com/ The Data Engineer's Guide to Root Cause Analysis: https://www.montecarlodata.com/the-data-engineers-guide-to-root-cause-analysis/ Why You Need to Set SLAs for Your Data Pipelines: https://www.montecarlodata.com/how-to-make-your-data-pipelines-more-reliable-with-slas/ Data Observability: The Next Frontier of Data Engineering: https://www.montecarlodata.com/data-observability-the-next-frontier-of-data-engineering/ To get in touch with Barr, ping her in the DataTalks.Club group or use barr@montecarlodata.com Join DataTalks.Club: https://datatalks.club/slack.html

Barr Moses is the Co-founder & CEO of Monte Carlo, a company on a mission to accelerate the world's adoption of data by improving data reliability and reducing data downtime. Prior to founding Monte Carlo, Barr built the customer data and analytics team at Gainsight, where she helped grow the company 10x in revenue. From a silent retreat to building a robust community of data leaders, Barr is setting an impressive example for aspiring founders.Barr LinkedIn: https://www.linkedin.com/in/barrmoses/The Data Standard LinkedIn: https://www.linkedin.com/company/the-data-standard/

The Drill to Detail Podcast returns for a new series with Mark joined by special guest Barr Moses to talk about data-driven customer success, data quality and the story behind her new data reliability startup Monte Carlo.Monte Carlo - Data reliability delivereddbt Data Source Freshness Delivering End-to-End Data Observability with Looker and Monte Carlo Data Observability: How to Build Your Own Anomaly Detectors Using SQLWhy Testing Your Data Is Insufficient

The Drill to Detail Podcast returns for a new series with Mark joined by special guest Barr Moses to talk about data-driven customer success, data quality and the story behind her new data reliability startup Monte Carlo.Monte Carlo - Data reliability delivereddbt Data Source Freshness Delivering End-to-End Data Observability with Looker and Monte Carlo Data Observability: How to Build Your Own Anomaly Detectors Using SQLWhy Testing Your Data Is Insufficient

Data lakes and data warehouses store high volumes of multidimensional data. Data sources for these pieces of infrastructure can become unreliable for a variety of reasons. When data sources break, it can cause downstream problems. One company working to solve the problem of data reliability is Monte Carlo Data. Barr Moses and Lior Gavish are founders of Monte Carlo and join the show to talk about data reliability and the overall landscape of data infrastructure.