Episode 50: Reducing Data Downtime with Barr Moses

Show Notes

• (2:23) Barr discussed growing up in Israel and serving as a commander of the Data Analytics unit at the Israeli Air Force.
• (4:10) Barr reflected on her college experience at Stanford studying Math and Computational Science.
• (7:24) Barr walked over the two career lessons learned from being a Management Consultant at Bain and Company.
• (9:51) Barr reflected on her time as VP of Customer Operations at Gainsight, which offers enterprise solutions for Customer Success and Product teams. She helped build and scale a global team covering various functions such as business operations, customer success, professional services.
• (12:32) Barr unpacked the notion of data downtime, introduced in her blog post “The Rise of Data Downtime.”
• (17:25) Barr unveiled the four main steps in the data reliability maturity curve: reactive, proactive, automated, and scalable - as indicated in “Closing The Data Downtime Gap."
• (21:09) Barr shared the founding story behind Monte Carlo, whose mission is to accelerate the world’s adoption of data by reducing data downtime.
• (24:29) Barr explained the five pillars of data observability.
• (27:45) Barr unpacked the rise of data catalogs as a powerful tool for data governance, along with the three categories of data catalog solutions that data teams are adopting - as presented in “What We Got Wrong About Data Governance.”
• (31:32) Barr discussed the benefits of using Data Mesh - a type of data platform architecture that embraces data ubiquity in the enterprise by leveraging a domain-oriented, self-serve design.
• (37:28) Barr went over a framework that looks at the business functions and the nature of the work to score the impact and allocate the ROI of the data team - as proposed in "Measuring the ROI of Your Data Organization."
• (40:39) Barr shared five practices for designing a platform that maximizes data's value and impact inside an organization.
• (43:27) Barr reflected on the key decisions to get the first cohort of customers for Monte Carlo.
• (46:31) Barr shared valuable hiring lessons.
• (48:48) Barr went over helpful resources throughout her journey as a founder.
• (50:13) Barr dropped the final advice for founders on seeking the right investors.
• (51:18) Closing segment.

Her Contact Info

• Twitter
• LinkedIn
• Medium
• Monte Carlo

Her Recommended Resources

• Stanford's "Mathematics and Magic Tricks" course (taught by Persi Diaconis)
• "The Biggest Bluff" by Maria Konnikova
• Snowflake
• DJ Patil (Former U.S. Chief Data Scientist)
• Monte Carlo's Customers

About the show

Datacast features long-form, in-depth conversations with practitioners and researchers in the data community to walk through their professional journeys and unpack the lessons learned along the way. I invite guests coming from a wide range of career paths — from scientists and analysts to founders and investors — to analyze the case for using data in the real world and extract their mental models (“the WHY and the HOW”) behind their pursuits. Hopefully, these conversations can serve as valuable tools for early-stage data professionals as they navigate their own careers in the exciting data universe.

Datacast is produced and edited by James Le. For inquiries about sponsoring the podcast, email