Just Launched: AI Monitor Recommendations for Proactive Data Quality Management

You don’t know what you don’t know, said a data analyst, definitely… and this is certainly true when it comes to the millions of ways your data can break. And even if you could predict all of these issues, you wouldn’t want to manually define and write data quality rules to cover all of them.

Wouldn’t it be great if your data quality and observability solution provided recommendations for these rules, and then – with the push of a button – created them? Or even better, if it also suggested machine learning monitors so your thresholds were automatically updated as your data evolves? 

We think so, too. That’s why today Monte Carlo is excited to announce the release of Monitor Recommendations, a native feature that leverages the power of data profiling to suggest deep one-click data quality monitors users can deploy across their most critical data products.

From column-matching for email formats to Null rates for ID fields, Monitor Recommendations leverages historic data patterns to simplify the discovery, definition, and deployment of field-specific data monitors.

With Monitor Recommendations, we’re making it easier than ever for anyone on the data team to define and scale new critical data rules—regardless of their SQL proficiency. 

Want to learn more about how Monitor Recommendations take data profiling to the next level? Let’s dive in.

Scale data quality coverage with Monitor Recommendations

At Monte Carlo, we don’t just build for our customers, we build with them. That means we don’t build features for theoretical use-cases—we build products our customers need today.

One of the best things about Monte Carlo is the depth and breadth of monitors we provide to ensure data analysts have the coverage they need across their data products, but sometimes the more options you have, the harder it can be to get started. 

For instance, you may want uniqueness as a general principle across a given table, but asking for a customer_state column to be 100% unique would be silly. You don’t want a spike of nulls, but you probably don’t want ANY nulls on a column that is near 100% complete like an ID key. Or, you may want to alert on future values that exceed certain thresholds, but it would take you hours pouring through an Excel spreadsheet to define these.

Monitor Recommendations is the solution to this problem, helping analysts identify and deploy the right monitors at the right time to ensure quick and proactive data quality coverage.

How to use Monitor Recommendations

Built on top of Monte Carlo’s data profiling solution—Data Explorer—Monitor Recommendations takes data profiling to the next level by not only displaying the content and characteristics of a given field, but also by suggesting appropriate monitors based on this rich metadata. 

Leveraging Monte Carlo’s validation and metric monitors, Monitor Recommendations profiles tables to detect patterns in the data that may be valuable to monitor, then suggests monitor rules and thresholds to deploy to programmatically identify future anomalies.

For example, maybe the values in a particular column match typical email formatting—Monte Carlo can recommend a Validation Monitor to alert when new values arrive that break format for an email address. 

Or, perhaps a column consists of mostly unique values. Monte Carlo can recommend a Metric Monitor for anomalous changes to the uniqueness rate and then programmatically determine the best threshold to deploy across that field. 

By leveraging data profiling, Monitor Recommendations makes it easier for data teams to immediately configure monitors for known issues while also simultaneously discovering monitor opportunities for the issues they don’t.

That means that anyone on the data team can quickly deploy the right monitors for their tables—regardless of their familiarity with the data that feeds them.

Learn more by checking out our documentation.

Democratizing data quality across the enterprise

Monte Carlo’s data observability platform allows data teams to democratize data quality by empowering you to deploy monitors however you like to work, whether that is automated across a data product, via code during the CI/CD process, using natural language with a chatbot, or with a simple no-code UI that provides AI-powered suggestions.

Now with Monitor Recommendations, analysts can quickly and automatically deploy new rules directly in the Monte Carlo UI suggested by AI-powered recommendations trained on historical patterns in your data. 

We’re excited to continue building new product capabilities that make the lives of analysts easier – and we’re just getting started! 

Interested in learning how Monitor Recommendations can scale data quality management across your organization? Connect with our team to find out how data observability can deliver the right monitors faster for your data.

Our promise: we will show you the product.