Monte Carlo provides end-to-end data observability to help organizations ensure the validity and quality of their data, preventing data incidents and building data trust.

Monte Carlo

life at monte carlo

Accel

GGV Capital

Redpoint

Salesforce Ventures

ICONIQ Growth

Just more proof that curated and high quality data—with a human in the loop to manage it—is the only real guardrail for consumer-ready AI. 

As GenAI inches ever-closer to enterprise adoption, getting the data quality ducks in a row needs to be priorities 1-10. 

#genai #LLMS #gemini #dataquality

Our team had a great time at the Data Dates event last night in Palo Alto, hosted by Monte Carlo, ThoughtSpot, Fivetran, and Snowflake!

Great talk by Benjamin Rogojan on the importance of data quality for GenAI initiatives. 💪 

Check out why high-quality data is the essential first step on the road to enterprise-ready GenAI: https://lnkd.in/e4MCBjab

#GenAI #dataquality #dataengineering

Data leaders: are your teams ticket takers or decision makers? 

IMO, there are two different types of data teams in this world: those who are reactive and those who are proactive. 

Reactive teams are helpful, but a cost center. 

The second? A value generator. 

Don’t get me wrong: there will always be some measure of ad-hoc ticket taking as a data practitioner. But the data teams that set metrics instead of just tracking them are the first to know when issues occur (instead of being alerted by stakeholders) and generate ROI instead of costs. 

Which would you rather be?

#dataengineering 

Back in the day, data quality management was a simpler affair. Data was smaller, slower, on-prem. It was like keeping a small garden. 🌿 

But in today’s cloud era of the modern data stack, times have changed, and that small garden has grown into a full-fledged forest. 🌳 🌳 🌳 

Data leaders need data quality management programs that are designed for the scale of the modern data stack – and they need a larger focus on the overall reliability of their dozens of data sources, hundreds of data models, and thousands of tables. 

Achieving data quality at this scale is no simple task, but this 6-step process has helped hundreds of teams achieve high-quality data across their organizations: 
✅  Step 1: Baseline current data quality levels
✅  Step 2: Rally and align the organization
✅  Step 3: Implement broad data quality monitoring
✅  Step 4: Optimize incident resolution
✅  Step 5: Create custom data quality monitors
✅  Step 6: Incident prevention

Learn more: https://lnkd.in/eZTD3Xzz

#dataqualitymanagement #dataquality

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Monte Carlo

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