Product demo. 
What is data observability? 
What is a data mesh--and how not to mesh it up 
The ULTIMATE Guide To Data Lineage Monte Carlo for retail and CPG.
Improve internal analytics, shopping experiences, and increase revenue with reliable and trustworthy data.
Trusted by the data teams at
Learn how Resident decrease data quality issues by 90 percent with Monte Carlo.
Data stack
90%
Decrease in data quality issues.
90%
Dramatic reduction in time-to-detection of data quality issues.
Challenge
- Missing and duplicative data, complicated queries and inconsistent logic across pipelines causes confusion in analytics.
- Missing ingestion pipelines from marketing sources, losing visibility into increase marketing spend efficiency.
- Lack of access to fresh, up-to-date data for stakeholders to make decisions.
Solution
- Custom alerting for known business logic, such as update frequency for 3rd party data.
- Field-level lineage graphs to show downstream impact of changes.
- Automated thresholding for data quality metrics across key tables.
“Before Monte Carlo, I was always on the watch and scared that I was missing something. I can’t imagine working without it now. We have 10% of the incidents we had a year ago. Our team is super reliable, and people count on us. I think every data engineer has to have this level of monitoring in order to do this work in an efficient and good way.”
Daniel Rimon Head of Data Engineering
Use cases for e-commerce.
Unlock new revenue opportunities and better decisions with fresh, accurate data.
Prevent excess or insufficient inventory spend by staying on top of critical data issues.
Acquire more users across your digital channels by using reliable data to analyze your ad spend.
Use cases for CPG.
Set thousands of reporting checks without writing a single test
Every storefront has different thresholds for acceptable levels for their reporting metric. Establish data quality baselines across business domains.
Generate reliable sales data for more accurate forecasting.
Inaccurate data leads to missed sales and wasted time leading to potential churn when inventory isn’t quickly restocked.
Don’t leave on-prem data behind.
With online channels increasingly pointing users towards digital and physical locations, reconcile data between disparate systems to provide a seamless customer experience.
“We have 10% of the incidents we had a year ago. Our team is super reliable, and people count on us. I think every data engineer has to have this level of monitoring in order to do this work in an efficient, good way.”
Daniel Rimon Head of Data Engineering
Out-of-the-box coverage across all your data tables, opt-in monitors for key assets, and monitors-as-code.
Don’t just sound the alarm when data incidents occur. Empower your data teams to resolve incidents in minutes.
Rich insights enable your team to proactively ensure data quality, and make better infrastructure investment decisions.