85: You Can Stop Doing Data Fire Drills with Barr Moses of Monte Carlo
May 4, 2022
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Barr Moses discusses her background, the challenges of data trust, and the concept of data observability. The story behind the name 'Monte Carlo' is revealed. The importance of accuracy, reliability, standardization, and automation in data management is explored. The value of standardizing processes and concepts in building a business is emphasized.
Accurate and reliable data is essential for building trust in data and making informed decisions.
Monte Carlo focuses on data observability and brings concepts from software engineering to eliminate data downtime.
Monte Carlo provides out-of-the-box functionality for the five pillars of data observability, reducing data fire drills and improving data quality.
Deep dives
Data Observability Space
Barr from Money Carlo, co-founder and CEO, talks about their mission to accelerate data adoption and eliminate data downtime. They focus on data observability and its importance in building trust in data.
Challenges in Data Quality and Reliability
Data teams often struggle to trust the data they use, leading to reliance on gut-based decision making when data is inaccurate. Money Carlo aims to solve this problem by providing tools to ensure accurate and reliable data.
Starting Monte Carlo
Monte Carlo, founded by Barr, aims to eliminate data downtime by bringing in concepts used in software engineering to the data space. They started the company three years ago and have seen the data observability category grow.
Barr's Military Experience Influencing Company Building
Barr's experience in the military, where she became a young commander responsible for a team, taught her about leadership, motivating people, and building a cohesive team aligned on a mission. She applies these lessons to building Monte Carlo.
Implementation and Benefits of Data Observability
Getting started with Monte Carlo is easy, with out-of-the-box functionality for the five pillars of data observability: freshness, volume, distribution, schema changes, and lineage. Data engineers and analysts can integrate it into their workflows, reducing data fire drills and improving data quality and reliability.
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.
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