The First Step to Data Trust? Data Reconciliation

Data discrepancies kill business confidence. Your customer database shows 10,000 active users while your analytics warehouse reports 9,847. Your payment processor records $2.1 million in monthly revenue, but your BI dashboards display $2.3 million.

These inconsistencies compound quickly across your organization. A 2% discrepancy in customer counts can lead to incorrect retention calculations, misguided product decisions, and executives questioning every dashboard and metric.

As one of our customers put it: “Trust comes like a bicycle, and goes like a Ferrari.”  

That’s why we’re excited to announce Comparison Monitors, our new low-code solution to monitor data across sources and teams and catch anomalies before they do damage.

Let’s dive in.

When Data Anomalies Become Compliance Risks

Data reconciliation between sources is imperative for risk management. For highly regulated industries, data discrepancies aren’t just minor issues – they’re major compliance risks. 

For a leading biotechnology company, research pipelines ingest critical clinical data from Oracle systems into Databricks, where every record matters for regulatory submissions.

The problem: Records were getting lost during ingestion, creating gaps in research datasets that could impact clinical trials and regulatory filings.

The solution: Comparison Monitors enabled them to automatically track record counts between Oracle and Databricks, catching data loss immediately without pipeline modifications. 

This isn’t just about catching errors. It’s about enabling a multi-billion dollar pharmaceutical company to maintain the data trust essential for drug development.

What Are Comparison Monitors?

Monte Carlo’s Comparison Monitors are your early warning system for data trust.

Comparison Monitors compare metrics from two data sources to find deltas. They detect anomalies for dozens of available metrics, or for custom metrics defined by the user. They can also be easily segmented, allowing you to isolate anomalies that could otherwise be missed.

The best part? You don’t need to write a single line of SQL. 

With a few clicks, anyone – from data engineers to business analysts – can set up monitors that detect and flag discrepancies between source and target data sources before they reach business stakeholders.

Getting Started

Comparison Monitors are available now for all Monte Carlo customers. To access the new low-code experience, create a new Comparison Monitor, and the workflow will guide you through choosing source and target data sources, defining alert conditions, and configuring notifications.

Ready to scale data + AI trust across your organization? Request a demo today.

Our promise: we will show you the product.