Product demo. 
3 Steps to AI-Ready Data 
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 a Fortune 500 CPG Leader scales supply chain reliability with Monte Carlo
Challenge
- Missing and duplicative data, complicated queries and inconsistent logic across pipelines causes low pipeline integrity
- 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.
- Visibility into the legitimacy of source data for sustainability and supply chain uses cases.
“Now, imagine a world where all of this is in one place, with no code, no support needed, with AI on top of it. That’s what data observability is. If an organization wants their data to be reliable, communicable, and faster to be fixed and resolved, and they think building all these separate pillars is not worthwhile, then they should look for data observability tools.”
Sanchit Srivastava Senior Manager of Data Analytics at Fortune 500 CPG Brand
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.