Barr Moses @BM_DataDowntime
Co-Founder and CEO of Monte Carlo. https://t.co/FUUJPkkyDt Joined July 2020-
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OK but actually... if I wrote this, who would want one? montecarlodata.com/blog-monte-car…
I’m so excited for my next book: Mastering Data Quality And Your ABCs, the first book on data quality designed specifically for children. Get your pre-orders now! montecarlodata.com/blog-monte-car…
The most costly mistake data teams make when building their data stack? Overcomplicating things. #dataengineering
You’ve gotta fight for better data 🥊 Check out the data quality calculator in the comments to understand the true cost of bad data: linkedin.com/feed/update/ur… #dataquality #dataobservability
Regardless of who’s at fault, it’s often the data engineers who will find themselves shouldering the brunt of the damage. That's why data teams NEED to have automation as a component of their data quality strategy.
Building a data platform from scratch? Here's a quick rubric data teams can use when assessing whether or not to build or buy.
The data mesh is a thoughtful architectural approach that facilitates the creation of domain-driven, self-service data products. BUT not every organization needs a data mesh right away—or even has the requisite resources to support it.
IMO, there are two types of data teams: those who are reactive and those who are proactive. Reactive teams are helpful, but proactive teams generate value. #dataengineering
I think the data quality maturity curve is a concept every data leader will need to get comfortable navigating. Instead of asking what data quality means generally, ask is, “what does data quality mean for my organization right now?”
Wisdom no AI can replicate. #GenAI #dataengineering
Does your team measure data quality? If the answer is no, I don’t blame you. Traditional methods, like completeness and consistency, are time-consuming, resource-intensive, and a pain to scale. Here are some alts that I think are more helpful. #dataquality #dataengineering
What do you see as the biggest challenge to your data products in 2024?
Monte Carlo isn’t creating a bunch of tools and then looking for problems to solve. We ask our customers what problems they already have—and then we get to work solving them.
Just gonna leave this here... Happy Wednesday! #dataengineering #dataengineers #dataquality
Erik Bernhardsson @bernhardsson
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» teej @teej_m
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धरमजीत �.. @dharamajit96670
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» teej @teej_m
9K Followers 2K Following » Working on Titan » https://t.co/aZwqUSdNXn » my friends call me teej
vicki @vboykis
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86K Followers 2K Following Director of Machine Learning at the Wikimedia Foundation. We host Wikipedia.
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Wisdom no AI can replicate. #GenAI #dataengineering
@BM_DataDowntime These metrics aligns well with Data ROI you shared recently.
Top 10 Data & AI Trends for 2024 by @BM_DataDowntime link.medium.com/OLXcAA8euGb
You took it (GenAI) from the right point (high-quality proprietary data, grounding LLMs in your proprietary data, retrieval augmented generation, GenAI is a team sport) and I can’t wait for Monte Carlo to have GenAI features towardsdatascience.com/5-hard-truths-… by @BM_DataDowntime
It's definitely going to be a busy Black Friday this year—and data leaders with AI experience are the must-have this season. Obviously, Microsoft was first in line for the deals. #openai #llms #dataculture #blackfridaydeals
You know you need to prioritize data quality—but what does that actually mean? Can you survive on dbt tests for the next 24 months or do you need data contracts by EOD? montecarlodata.com/blog-data-qual…
5 Generative AI Use Cases Companies Can Implement Today montecarlodata.com/blog-5-generat…. Thanks @BM_DataDowntime
The Moat for Enterprise AI is RAG + Fine Tuning — Here’s Why by @BM_DataDowntime in @TDataScience towardsdatascience.com/the-moat-for-e…
The Moat for Enterprise AI is RAG + Fine Tuning — Here’s Why by @BM_DataDowntime in @TDataScience towardsdatascience.com/the-moat-for-e…
The best data professionals are extremely opinionated / influential on the data generation process. Best practices around SQL/DS modeling bad data have an upper bound. Good data people do their best to deal with “garbage in”. Great data people go upstream and fix the problem.
🏰 Great piece on Generative AI techniques...The Moat for Enterprise AI is RAG + Fine Tuning — Here’s Why...🚀 Great share @BM_DataDowntime...😎 #TheDigitalCoach #GenerativeAI #RAG #FineTuning #EthicsMatters #Safety towardsdatascience.com/the-moat-for-e…
5 Generative AI Use Cases Companies Can Implement Today by @BM_DataDowntime in @TDataScience towardsdatascience.com/5-generative-a…
How does data downtime ACTUALLY affect your organization? 🔍 We list out 5 notable cases of data downtime and how your teams can prevent them from happening at your companies. Learn more: montecarlodata.com/blog-bad-data-…
Getting started with LLMs? Here are 5 popular applications data teams at OpenAI, Vimeo, and other companies are putting into practice today by @BM_DataDowntime buff.ly/3RJTFKE
we're live! my intro part is over so it's safe to tune in now hex.tech/hex-three-poin…
we have some sick interviews coming up on @EquityPod over the next few weeks
Is The Modern Data Warehouse Broken? by @BM_DataDowntime in @TDataScience towardsdatascience.com/is-the-modern-…
feels so good to be back on the east coast where you don't start the day three hours behind
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