Data quality
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
Boost trust in 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.
Proactive data quality
Reduce manual effort with automated data validation
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 profiling and validation in CloverDX
See how to build profiling and validation steps in to a workflow to detect and handle errors, and ensure data quality, in this blog post.
Data validation in CloverDX
Read this post for a step-by-step walkthrough of building data quality steps into your data pipeline.
Visual interface
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
Error handling
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.
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.
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.
Watch: Building data pipelines with bad data in mind
Discover some best practices and techniques for assessing and ensuring data quality in this webinar.
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