Barr Moses

Yali Sassoon, Co-founder and Chief Strategy Officer at Snowplow Analytics, and Barr Moses, CEO & Co-founder of Monte Carlo, delve into the modern data stack's transformative power. They discuss how data teams can effectively showcase their value by aligning with business goals and enhancing collaboration. The duo emphasizes treating data as a product, navigating challenges with AI, and the importance of metrics to measure ROI. They also share career advice for aspiring data professionals, highlighting the necessity of curiosity and continuous learning.

Barr Moses, co-founder and CEO of Monte Carlo, shares insights on the AI readiness crisis many companies face. She reveals high-stakes data disasters, including a shocking $100M schema change. The discussion emphasizes the necessity of data quality and observability, as organizations struggle to align their ambitions with reality. Barr also highlights the transformative role of LLM agents in improving data debugging. Overall, it's a sharp critique of the disconnect between current data practices and the demands of AI.

Barr Moses, Co-Founder & CEO of Monte Carlo and a leader in data observability, dives into the pivotal importance of reliable data in AI systems. They discuss the dangers of 'data hallucinations' and how to prevent them, stressing that clean data is the true competitive edge over mere access to AI models. Moses highlights the evolution of data quality assessment and the instrumental role real-time monitoring plays in ensuring data accuracy and trustworthiness. Real-world examples reveal the impact of data failures on AI outputs and the need for modern solutions.

Barr Moses, CEO of Monte Carlo, Prukalpa Sankar, Founder of Atlan, and George Fraser, CEO of Fivetran, dive into the pressing issues of data quality in the realm of generative AI. They explore the critical need for high-quality data and the unique challenges organizations face. The discussion highlights the importance of trust and transparency in dynamic data ecosystems, along with best practices for fostering collaboration. They also present a structured three-step framework for enhancing data quality and the role of leadership in driving effective governance.

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 Monte Carlo's Website Learn more about our mission and become a member here: https://www.womenindata.org/

Barr Moses, Co-founder and CEO of Monte Carlo, dives into the pivotal role reliable data plays in AI success. He emphasizes that everyone's leveraging AI, but data quality is what sets businesses apart. Discussion includes the challenges and opportunities for smaller organizations in utilizing data effectively. Barr highlights how generative AI can transform sectors like finance and sports by enhancing data quality. He also covers the necessity of data observability to tackle issues proactively, urging businesses to treat their data as a competitive advantage.

Barr Moses, co-founder and CEO of Monte Carlo, shares her expertise on the future of data analytics. She discusses the evolution of data reliability, emphasizing the growing need for observability amidst emerging technologies. The conversation touches on the critical distinction between data quality and quantity, and the unique challenges posed by unstructured data in decision-making. Moses also highlights the role of reliable data in the age of generative AI, and how organizations can navigate this complex landscape while maintaining high data standards.

Barr Moses, Co-Founder and CEO of Monte Carlo, shares her inspiring journey from data strategy at Bain to building a pioneering data observability platform. She discusses her unique experiences in the Israeli Army and highlights the importance of teamwork and feedback in data science. The conversation dives into the critical need for reliable data, illustrated by a staggering $100 million loss due to a data error. Barr also explains the five pillars of data observability and emphasizes how data accuracy is vital for trust, especially in an AI-driven future.

Barr Moses, Co-founder and CEO of Monte Carlo, dives into the significance of reliable data in enhancing AI performance. He discusses how data observability can minimize downtime, a critical need for businesses of all sizes. Moses emphasizes that accurate data is essential for personalized AI and examines practical use cases like sports analytics and financial products. He also shares insights on the burgeoning trend of synthetic data and the importance of data governance for effective data utilization.

Guest Barr Moses discusses the importance of data reliability for pipelines and data mesh. Topics include self-serve data reliability, using Monte Carlo for data uptime, building effective data science teams, and LinkedIn Q&A.