Always-on data validation

Make data quality an integral part of your pipelines with automated data validation and error-handling

Make better decisions with trustworthy data

Create pipelines that let you sleep well with proactive data quality 

Building data validation into your pipelines prevents errors and inconsistencies, and improves trust in your data.

Quickly identify and resolve data quality issues with CloverDX's built-in data profiling, cleansing and validation features.

CloverDX helps you make confident decisions based on reliable and trustworthy data.


Reduce manual effort with automated data validation built into your workflows

Always-on validations are the best way for preventing data quality issues that impact trust. With easy automation in CloverDX you can make validations part of every step of the process.

Automatic error detection can proactively detect, flag, and even repair bad data throughout your pipeline.

And it's easy to define shared validation rules and use them across multiple teams and projects.

data validation built in to a workflow in CloverDX
Data profiling and validation in CloverDX

See how to build profiling and validation steps into a workflow to detect and handle errors, and ensure data quality

Bring transparency to your data pipelines

CloverDX's visual interface brings transparency to data processing. Easily understand and track data transformations, so it's easier to troubleshoot and maintain data workflows.

Speed up troubleshooting and shorten downtimes
With clear visibility into data workflows, you can quickly identify and resolve issues, minimizing downtime and ensuring reliable, accurate data.
validation steps

Resolve problems efficiently with comprehensive error reporting

With CloverDX's error reporting, even non-technical users can resolve problems. Everyone can easily see which records contain errors and what the error is. 

CloverDX also makes it easy to automatically pull errors into an Excel file, and send them to an expert to fix. 

White paper - Architecting systems for effective control of bad data
Architecting Systems for the Effective Control of Bad Data
Download the white paper and discover the best practices for identifying and managing bad data in your pipelines.

CloverDX data quality features

Build data validation steps into your workflows easily

Filter data automatically and minimize manual effort

Data filtering components check for invalid records as they come in and filter out any that don't meet your defined rules. This ensures better data quality further down your pipeline.

Customize validation rules to share and reuse

CloverDX's Validator component contains pre-built and customizable rules. These repeatable and shareable rules ensure your data meets your quality standards.

Profile data across even complex workflows

Instantly analyze your data as it flows through the CloverDX ProfilerProbe component. These measures make profiling accessible throughout even the most complex data workflows.

Data validation in CloverDX workflows

See how profiling and validation steps can be built into a data pipeline in CloverDX in this video clip.

If you want to see how CloverDX could help your specific data quality requirements, book a personalized demo.

Build scalable solutions with CloverDX

The CloverDX Data Integration Platform is designed to grow seamlessly and cost-effectively as more systems are added, giving you a long-term solution and long-term business impact, with predictable costs.

Building pipelines with bad data in mind thumbnail

Watch: Building data pipelines with bad data in mind

Discover some best practices and techniques for assessing and ensuring data quality in this webinar.

Data quality and validation case studies


Removing manual bottlenecks with an automated data quality process

An expanding logistics company struggled with its address data. Different languages, alphabets and address structures were hard to work with. So much so, a dedicated team needed to manually verify the addresses to meet the company's delivery deadlines. But as the volume grew, the team couldn't keep up.

A data validation and cleansing framework, built on CloverDX, resolved their issue. The framework interfaces with third-party systems (e.g. Google Maps) to verify and repair 90% of addresses automatically. The team can now modify rules to support additional countries without the need for more coding.


Automated address validation and cleansing saves $800,000

This marketing company's customer contact details were inconsistent, duplicated and dispersed.

With CloverDX, they undertook a data quality audit. Email addresses and phone numbers were automatically verified, deduplicated and enriched with external sources.

The end result was a clean database, reduced in size but increased in quality. This change led to more efficient targeting, resulting in more orders and huge cost savings.

Make data quality an integral part of your data pipelines 

See how you can build always-on data validation into every step of your workflows with CloverDX