Trust your datasets with
Data Testing

BEEM’s dataset tests allow data teams to sleep at night knowing the automated transformations they’ve put in place only result in positive outcomes.

Automate Your Data Testing Now
Visual data testing with patterns

Data reliability is an ability

Data Reliability

With BEEM’s automated dataset testing features your business reap benefits far and wide, from data accuracy in decision making to data enriching your operational tools.
Icon report chart
No more untrustworthy reports or broken dashboards
Ability to show last valid update of data / staleness level of data
Icon action item
Easy action items that can be shared across teams
Icon security
Always on duty data quality and integrity guardian

How we keep your datasets fresh and always valid

How it works

Visual data testing diagram
Icon sync

Dataset refresh

Your datasets run automatically based on their refresh schedule.

Custom tests run

Your custom dataset tests run automatically against the new version of results your dataset refresh produced.
Icon deploy

Deploy only valid results

Based on the results of your tests, the new dataset version is invalidated if any test results with blockers arise.

Tests as unique as your data

Be it logical or analytical tests, you can have full control over the tests that are run on each dataset refresh. Endlessly customizable, tests are written in SQL making them extremely easy to setup and simple to understand by the whole team.

Customized tests

Visual customized dataset test

Not every incident is equal

Blockers and Warnings

Tests can be configured to your every need and criticality.
Set blockers on the most critical tests that should prevent the new data from being propagated to your business users or from syncing erroneous results to operation applications.
Think of warnings as flags that let your team know that some values don’t follow your rigorous criteria and need to be addressed but aren’t showstoppers for your downstream processes.
Visual dataset tests with status blockers and warnings
Visual dataset results with tags to control data quality

Master your data incidents

Take action on results that don’t meet your level of quality in one quick view.
With our consolidated test results view, you can have a comprehensive list of the rows that did not pass one or multiple tests that were run including the values, the test name and the criticality for each instance.
‍
You can leverage this list as an action plan for your team to start investigating and cuts down significantly the time required to identify the source cause of the issue.

Data Quality

Get reliable data with dataset tests now.

You can start modelling your data within minutes instead of months.

Ready to dive into data insights?

Get a demo

You got questions, we got answers!

FAQs

How do we catch data quality issues before they hit our dashboards?

Automated tests run after every data refresh. Define rules like "revenue can't drop 50% day-over-day" or "customer count must be positive." Get alerts before your CEO spots wrong numbers in the Monday meeting.

Can we test data across multiple sources to catch integration problems?

Yes, cross-source validation catches issues like mismatched customer IDs between your CRM and billing system. Find reconciliation problems automatically instead of discovering them in quarterly audits.

What happens when a test fails - does data still load or does it stop?

You choose. Block questionable data from reaching dashboards, or let it through with warning flags. Most companies start permissive then tighten rules as they define quality standards.

Do we need to write code to set up data tests?

No-code test creation for common checks. Click to add tests like "check for duplicates" or "validate email format." Power users can write custom SQL tests for complex business logic.

How much time does data testing save compared to manual spot-checks?

Companies spending 10-15 hours weekly on data verification reduce that to under an hour. One finance director caught a $2M invoice duplication error that would've taken 3 weeks to surface manually - found it in 10 minutes with automated testing.

Can we test historical data to catch issues that crept in over time?

Run tests across any time range. Compare this month's data patterns against the past year. Spot gradual data drift that's hard to notice day-to-day but obvious when measured systematically.

What ROI do companies see from data testing features?

The first time you catch a major error before it reaches executives, the feature pays for itself. One company avoided a $500K inventory write-off by catching duplicate shipment records. Another prevented a marketing budget overspend when testing flagged a vendor reporting error.

How do you handle false positives without creating alert fatigue?

Tune sensitivity for each test. Set warning thresholds vs. hard failures. Most companies start with 20-30 tests and refine over 2-3 months to find the sweet spot - catching real issues without crying wolf.