Data Quality

Make better decisions with trustworthy data

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Design for Bad Data

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

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

CloverDX gives you the ability to make better decisions with more trustworthy data.


Reduce manual effort with automated data quality flows

It's easy to replicate similar data quality procedures on 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

Transform and validate incoming data right from the source, before bad data affects your pipeline.

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

Don't let bad data stop your data pipelines. With CloverDX, you can build an error handling process into your workflows. This defines how you handle bad data while keeping your good data flowing at the same time.

Identify it, fix it, or remove it from the pipeline altogether. Once it's checked by the data owner, 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

See how profiling and validation work in CloverDX to automatically monitor and validate incoming data.

If you want to learn more, book a personalized demo and we'll show you how you can implement your specific data quality requirements with CloverDX.

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HubSpot Video

Improve process with comprehensive error reporting

Often, code errors are unintelligible to non-technical users. With CloverDX's error reporting, non-technical users can resolve problems efficiently. They can see which records contain errors and what the error is. It's simple to pull these into an Excel file and send to an expert to fix.

When everyone has a bird's eye view of data errors, it makes fixing systemic problems easier.

CloverDX Data Quality Features

CloverDX has data quality functionality built in, so you can implement data quality steps into your workflows quickly and 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 and ultimately more reliable results.

Customize validation rules to share and reuse

CloverDX's Validator component contains pre-built and customizable rules. These rules ensure your data meets your quality standards and allow you to validate against third-party sources. With CloverDX's repeatable approach, you can share and reuse these rules across your business.

Profile data across even complex workflows

Get an instant check on your data. Simply analyze it 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

A logistics company struggled with their address data as they expanded internationally. 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. So 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