Building trust in enterprise data and analytics.

Datagaps focuses on the End-to-End data journey from the point of ingestion through to consumption in your data analytics platform. This takes the shape of observing the quality of data in motion or at rest, validating your ETL and Data Analytics processes to ensure that decisions are made on trustworthy data.

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Shift left the right way.

Datagaps and Qualitest take a shift left approach to data validations testing as costs increase significantly the further data assets have progressed through your data pipeline. With a shift left approach, data quality is checked at ingestion, as the data passes through transformations, and finally at the point of consumption. We have found that the cost can be reduced five-fold when anomalies are located in the early stages.

Your Benefits

Shift left testing, make better decisions.

Our shift left testing philosophy helps reduce the cost of correcting issues by catching problems earlier in the data movement cycle. More importantly, by detecting these issues early, they don’t show up in your data analytics platform that you rely on to make critical business decisions.

Calculate your cost of bad data.

Feel free to plug your data into our cost calculator here to see the impact on your organization.

100% data testing.

Automated Data Testing allows you to test 100% of the data, not just a fraction covered by manual testing.

Reduction in the cost of manual testing.

Manual testing is expensive and often results in missed issues. Data Automation Testing resolves these issues through a framework to reduce the time to develop and execute the test.

Our Features

Empowering you with trustworthy business intelligence and data.

Datagaps test the ETL and data movement processes to ensure the transformations and data transfers have been properly executed. As your data analytics platform consumes data we perform various tests such as Performance, Stress, Graphical comparisons, Functional, and Report Data to Query Data comparisons. We ensure that the decisions made are based on trustworthy data.

End-to-End Data Testing

To perform valid data testing issues need to be found throughout the data pipeline from ingestion to consumption. Anything short of this introduces anomalies in your data analytics processes.

Access to All Your Data Sources

Automate Testing of Relational, Flat Files, XML, JSON, NoSQL, Cloud, and Modern data sources such as Snowflake and Databricks.

Production Application Data Validation Testing

Often it is critical to tie your data analytics metrics back to the source production systems to validate that the metrics have been formulated properly.

Data Quality Testing

Provides a data model driven interface for defining data rules to verify that the data conforms to quality standards and range of values.

Data Observability

Profiles data assets and compiles statistics from the past. Predicts expected values and detects deviations ahead of time.

Data Quality Score

Computes Data Quality scores for data assets and displays trend reports on a Data Quality Dashboard.

Data Reconciliation

Check for data integrity by matching data from various sources to ensure that data is consistent across systems.

Synthetic Test Data Generation

Increase test coverage by leveraging powerful synthetic data generation mechanism to create the smallest set of data needed for comprehensive testing as well as for specific business case scenarios.

Let us know how we can help you.

We’re ready to talk, how can we make your life easier?