Data Quality

Make better decisions with trustworthy data

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Design for Bad Data

Data quality is an essential but tricky task. CloverDX's data quality features discover and deal with bad data fast. With our platform, automate:

  • Bad data identification & correction
  • Rule definitions
  • Reports on data quality

CloverDX helps you make better decisions with more trustworthy data.


Reduce manual effort with automated data quality flows

Replicate similar data quality procedures with ease using CloverDX. Define them once in a custom CloverDX component and share them across your teams.

Automatic error detection can pick up problematic data, avoiding future manual corrections.

Data validation built into your workflows

With CloverDX's native and custom components, you can define data validation rules and build them into your workflows. The process is easy - simply drag and drop the components.

CloverDX's capabilities allow you to integrate with other sources (e.g. master data databases) for better data quality.

Keep data pipelines running with automatic error handling

Insert error-handling processes into your workflows and reduce instances of bad data disrupting your data flow.

Identify it, fix it, or remove it from the pipeline altogether. Once the data owner checks it, you're good to go. And once you fix the errors, you can feed the corrected data back in.

Watch: Data validation in data ingestion pipelines

See how to build an automated data ingestion pipeline that includes data validation steps in order to detect, monitor and fix data quality issues.

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Watch an example

Want to see how automated profiling and validation work in CloverDX? Book a personalized demo. We'll explain how you can implement your specific data quality requirements with CloverDX.

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Improve process with comprehensive error reporting

With CloverDX's error reporting, even non-technical users can resolve problems efficiently. They can see which records contain errors and what the error is pull them into an Excel file, and send them to an expert to fix.

CloverDX Data Quality Features

CloverDX has data quality functionality built in, so you can implement data quality steps into your workflows easily.

Filter data automatically and minimize the need for human interaction

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.

CloverDX enables you to build scalable solutions

It's designed to grow seamlessly and cost-effectively as more systems are added, giving you a long-term solution and long-term business impact

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Improve your data quality processes with CloverDX
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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.

Read more: Data Quality and Address Validation

Automated address validation and cleansing saves $800k

This marketing company's customer contact details were inconsistent, duplicated and dispersed. With CloverDX, they underwent 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.

Read more: Address Validation and Cleansing Saves Over $800,000

See how CloverDX can help improve your data quality processes.

Book a demo now.

Building Data Pipelines With Bad Data In Mind

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

Watch the webinar now