Data Downtime Podcast
When Good Data Goes Bad: Barr Moses on How to Make Sure Your Data Is Accurate and Reliable


isten to my podcast with Barr Moses, CEO & Co-Founder of Monte Carlo, a data observability company. In this podcast we discuss how to truly become a data driven company: what it takes to be data driven, how many companies are falling short, data downtime, how data downtime impacts a company, how data downtime impacts decision making, how good data and costumer success are directly related, how good data goes bad, and finally, how data engineering is the new customer success. Give it a listen.

Read The Transcript: Know Thy Data With Data Observability


[01:58] It's incredibly hard for companies to actually adopt data because data is often wrong and it often breaks, and it's really hard to know if you can trust it. 

[02:04] One of the realities that I faced personally was waking up every Monday morning to find out that something new was broken with the data. Some report that our marketing executive was using was sort of out of date. And, you know, they didn't know what decision to make based on that. 

[03:32] Data downtime refers to periods of time when data is missing, inaccurate or otherwise erroneous. And this affects all data driven companies, really, regardless of size and industry. 

[04:22] Netflix was down for 45 minutes because of duplicate data, right? That is significant impact. And so we're going to need to get a lot more diligent on how we measure data downtime 

[04:55] Data is being used to make everything from the most strategic decisions in an organization to powering the most important digital products. I would argue that data is the new software, right? 

[05:32] US companies cumulatively spend $3 trillion per year dealing with bad data, data quality issues. 

[06:25] One out of five companies reported that they lost a customer due to inaccurate data. 

[11:51] Data Observability refers to an organization's ability to fully understand the health of their data in the system in the same way that observability proves that to DevOps. 

[15:02] So in data, who owns data? When a particular report is broken and you can't make a decision based on that is that the analyst who was responsible for the report? 

[16:05] Just like every customer success organization has ways to develop trust and trust in knowing that you can retain your customers and grow them, just like every engineering team has a solution for observability, I cannot imagine a world in which we don't have something like that for data engineers, right? 

[16:51] Data reliability is within our reach with the right approach with focusing on automation and observability. 



Monte Carlo

Data got you down? You are not alone. We’ve met hundreds of data teams that experience broken dashboards, poorly trained ML models, and inaccurate analytics — and we’ve been there ourselves. We call this problem data downtime, and we found it leads to sleepless nights, lost revenue, and wasted time.



  1. The Rise of Data Downtime
  2. Data Observability: The Next Frontier of Data Engineering
  3. How to Calculate the Cost of Data Downtime

Barr Moses

Barr Moses (@BM_DataDowntime), founder of Monte Carlo, a data observability company backed by Accel 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.

Peter Schooff | Dec. 17, 2020