In this episode of the Adopted Podcast, Deepak Anchala interviews Barr Moses, co-founder and CEO of Monte Carlo, a pioneer in data + AI observability. They discuss the importance of data quality and trust in the age of AI, the evolution of data practices, and the challenges organizations face in ensuring their data is reliable. Barr shares insights on the critical role of data observability in AI deployment, the implications of bad data, and how AI can enhance data quality. The conversation also touches on the future of AI applications versus agents and concludes with valuable advice for aspiring founders in the AI space.
Takeaways
- Monte Carlo helps organizations adopt data and AI by increasing data quality.
- Data observability is crucial for reliable AI applications.
- The stakes of using inaccurate data are significantly higher today.
- Data governance and quality issues are prevalent in many organizations.
- AI can enhance data quality and observability processes.
- Companies often struggle with data quality, governance, and semantic meaning.
- Bad data can lead to significant financial losses for companies.
- AI agents can streamline troubleshooting and data analysis.
- Listening to customers is more important than following advice.
- Speed and focus are key principles for productivity in startups.
