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Barr Moses

Co-Founder & CEO, Monte Carlo · 1 appearance on the record.

computed by AI from the episodes · how this works → · full disclaimer →

founderexecutiveauthor@BM_DataDowntime ↗LinkedIn ↗montecarlodata.com ↗

Barr Moses is the co-founder and CEO of Monte Carlo, a data observability platform designed to monitor enterprise data and AI systems. She also co-authored the O'Reilly book Data Quality Fundamentals and previously served as VP of Customer Operations at Gainsight.

10statements → 5claims → 2claims resolved → 3.9/5average certainty → 2/5average debate potential → 4.2/5argument clarity · the sources → 1said about them ↓

0 supported 1 partly supported 1 contradicted 3 not checkable as stated how the 5 claims stand · each chip opens the sources

1 prediction · 4 assertions · 1 opinion · 4 insights · every statement was checked. The prediction and assertions are the 5 claims: statements the public record can support or contradict. 2 are resolved, and 3 name no date, number or outcome precise enough to check. Everything else (opinions, insights, what ifs, disclosures) can never be settled by the record, so it carries no assessment.

The record, in short

What the tape says about how Barr argues and how the claims held up. Everything they said, and everything said about them, is in the tabs below.

Their most notable contradicted claim

Assertion Contradicted
Moses: Monte Carlo uniquely offers out-of-the-box cross-system observability
“So we're actually the only sort of product and market that you can connect today to those sort of different systems and sort of automatically out of the box, get an overview of what the health of your data looks like and sort of observability for your data on …”
Barr Moses Apr 12, 2022 ▶ 13:15 Unlocking Data Observability with Monte Carlo's Barr Moses

Argument clarity: do they answer the question? how? →

4.2 / 5 directness 4.2 · coherence 4.2 · precision 4.1 · compression 3.4

redirected or did not address 1 of 8 assessed questions (13%). Watch them ▸

This is a score against a rubric. It is not a rank. Every host question → answer exchange is scored with names hidden on directness, coherence, precision and compression, 1–5 each, on meaning alone: disfluencies are ignored, and only raw unedited episodes count. This is the score that measures thought. Every scored exchange, scores shown → · The rubric and its checks →

How they sound: speaking style how? →

257 words/min while actually speaking · 67.9 um and uh per 1k words

Measured by listening to the audio itself: 3,813 words across 1 episode of raw-level tape, transcribed verbatim with every um and uh kept, each one attributed only where the alignment onto our timed stream is unambiguous. These are measurements of speaking style. We do not rank them: across this corpus, fluency and argument quality are nearly uncorrelated (ρ≈0.2), and smooth talking does not signal clear thinking. How it's measured →

Everything Barr Moses said on the MAD Podcast that made the record, most notable first. Filter by type, assessment or year in the ledger →

Assertion Contradicted
Moses: Monte Carlo uniquely offers out-of-the-box cross-system observability
“So we're actually the only sort of product and market that you can connect today to those sort of different systems and sort of automatically out of the box, get an overview of what the health of your data looks like and sort of observability for your data on …”
Barr Moses Apr 12, 2022 ▶ 13:15 Unlocking Data Observability with Monte Carlo's Barr Moses
Opinion
Moses: Traditional data catalogs are dead; long live automated data discovery
“We actually wrote a blog post not too long ago called data catalogs are dead. Long live data discovery.”
Barr Moses Apr 12, 2022 ▶ 18:26 Unlocking Data Observability with Monte Carlo's Barr Moses
Assertion Not checkable as stated
Moses: Public companies accidentally report incorrect financial numbers to Wall Street
“Companies actually report numbers to the street and accidentally report, report the wrong numbers, or about to report the wrong numbers.”
Barr Moses Apr 12, 2022 ▶ 2:42 Unlocking Data Observability with Monte Carlo's Barr Moses
Insight
Moses: Validating data at a single pipeline point is no longer sufficient
“And so making sure that your data is accurate at only one point of the pipeline is just no longer sufficient”
Barr Moses Apr 12, 2022 ▶ 9:52 Unlocking Data Observability with Monte Carlo's Barr Moses
Insight
Moses: Centralized data teams become bottlenecks as data usage scales
“Today you have like hundreds of people working with the data. It does not make sense anymore that there's one team that sort of has the keys to it and is really actually just ends up as a bottleneck.”
Barr Moses Apr 12, 2022 ▶ 11:07 Unlocking Data Observability with Monte Carlo's Barr Moses
Insight
Moses: Machines cannot infer semantic business logic without human input
“I don't think that a machine can actually infer that we can infer something without knowing that business knowledge. It's not possible, and that's also not what we attempt or attempting to do at Monte Carlo.”
Barr Moses Apr 12, 2022 ▶ 19:26 Unlocking Data Observability with Monte Carlo's Barr Moses
Prediction Not checkable as stated
Moses: Automation can resolve 80% of data downtime causes
“By introducing that level of automation, we can reduce our customer's team's work. Work from, you know 80% manual work to 20% manual work. So we can actually, you know, with the automation cover, cover 80% of reasons for why data downtime incidents happen and …”
Barr Moses Apr 12, 2022 ▶ 19:39 Unlocking Data Observability with Monte Carlo's Barr Moses
Insight
Moses: Generating actionable data quality alerts without alert fatigue is inherently difficult
“Making alerts meaningful making them ones that your team can actually act on is something that's very hard to do that we've invested a lot to do.”
Barr Moses Apr 12, 2022 ▶ 22:10 Unlocking Data Observability with Monte Carlo's Barr Moses
Assertion Partly supported
Moses cites 2022 revenues for BigQuery, Snowflake, and Databricks
“So you're seeing companies like BigQuery with 1.5 billion dollars in revenue, Snowflake with a billion dollars in revenue Databricks with eight hundred million in accelerating.”
Barr Moses Apr 12, 2022 ▶ 4:42 Unlocking Data Observability with Monte Carlo's Barr Moses
Assertion Not checkable as stated
Moses: Schema changes are a major culprit for data downtime
“So actually schema changes are a big culprit for data downtime.”
Barr Moses Apr 12, 2022 ▶ 7:41 Unlocking Data Observability with Monte Carlo's Barr Moses

The other half of the tape: Barr Moses's own voice is left out of every number here. Other people bring the name up 1 time in 1 episode on the MAD Podcast. every mention, with the transcript →

Who brings them up most Matt Turck 1

Every mention by year

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2024 1 mention in 1 episode

Appearances (1)

EpisodeDateSpeaking time
Unlocking Data Observability with Monte Carlo's Barr Moses Apr 12, 2022 18m
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