Solving Data Quality Issues — Once and For All

Solving Data Quality Issues

There are a lot of tools out there that are great for one-time data "wrangling". You import an Excel file, do a bit of magic, and voilà, you have clean data you can take straight to your analytics, or wherever you need it. That’s effective when the clock’s ticking and your boss is waiting on you to massage the data set...

At CloverETL, we think of data quality strategy. We see it as part of a larger scheme of automated data pipelines.

CloverETL enables you to embed shareable and repeatable data quality checks, rules, and processes into automated data movements or transformation workflows, all managed within a single platform.

With this strategy, whenever you pull data from a company source, it will always be curated, clean, and ready to use.

  • Address Validation and Geo-coding
  • Reporting of Missing Data
  • Correcting Typos and Non-conforming values
  • Continuous Profiling
  • Alerts on Deviations from Usual Standards
  • Self-learning Algorithms


With rejects easily reported back, responsible parties notified, data reprocessed, and resolutions remembered for future use

Real Stories of Data Quality

Making data quality a natural and automated part of your processes is a big undertaking.

But when you finally go for it, the long term benefits far outweigh the initial investment.

With an automated address validation solution, a fast-growing logistics company replaced dozens of workers tasked with manually validating and cleansing address data.

Address Validation

Download Address Validation Case Study

Deploying an automated data quality solution, Canadian political party NDP automated & transformed the data quality of more than one hundred datasets involving millions of constituent records.

Data Quality For Migrations

Visit Data Quality Case Study page

CloverETL Data Quality Toolset

CloverETL is so much more than just Data Quality. It's a fully capable data integration platform oriented towards orchestrating and automating data pipelines that continuously connect sources and targets with properly transformed data.

Data quality plays an essential role in producing great results. As CloverETL offers a range of integrated tools, it conveniently and easily keeps all your data processing within a single platform.

Rule-based Validator

Visually design, manage, and share validation rules, and report rejects straight away


You can think of Validator as a barrier that ensures that data entering your workflows meets predefined data quality standards. If it doesn’t, your validation rules automatically generate reject reports that can be directly shared back to the originator of the data set.

Validator comes with a range of standard rules for typical field types, from integer checks to phone numbers and email addresses. You can also create a library of your own custom rules that are shareable across projects, effectively defining company-wide data quality standards.

Data Profiler and Continuous Profiling Probe

Gather statistics about your data. Inspect it visually or make it into a regular sanity check running as part of your workflows.

Data Profiler and Continuous Profiling Probe

CloverETL Data Profiler is a data profiling module that provides a fast, accurate way to assess the current condition of your data.

Data Profiler can be deployed directly inside transformations to automatically profile ingested data and report anomalies outside expected thresholds.