5 Best Data Quality Tools 2022: Reviews & Warnings

by Jesse Spencer | January 10, 2022

Looking For a Data Quality Tool?

If data is an important part of your growth strategy, chances are very good you have some cleaning to do. Finding the best data quality tool for your tech stack is important.

We’re often asked “If we don’t choose Grooper, who else would you recommend?” That’s why we’ve made a list of the 5 best data quality tools on the market.

While Grooper is the product of over 30 years’ experience in working with document-based data, ETL, and integration, we understand your needs out of a data quality tool might be a little different.

data quality management toolsBut we aren’t afraid to talk about our competition because we believe in transparency and that by providing helpful and honest content, you’ll find us to be a valuable resource. And – it’s just the right thing to do! We’re thrilled you’ve found us and are happy to answer all your questions.

All the solutions below offer great solutions to ETL, master data management, data cleansing, integration, and information governance.

  1. What are Data Quality Tools or Software?
  2. Data Quality Tool Features
  3. Data Quality Tools Comparison Chart
  4. Informatica Data Quality
  5. IBM InfoSphere Quality Stage
  6. SAP
  7. SAS Data Quality and Data Management
  8. Talend
  9. How to Pick the Right Data Quality Tool

What are Data Quality Tools or Data Quality Software?


Data quality tools or software are innovative technologies and approaches leveraged by businesses to enhance the consistency, accuracy and reliability of data in order to benefit end-user organizations with better decision making.

This category of tools includes:

  • Data cleansing
  • Data migration
  • Data auditing

Employing tools with these abilities creates standardized data that contains very few typos or abbreviation errors. Data quality software is integral to larger goals in the field of enterprise information management (EIM), which includes information governance and master data management (MDM).

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What Features to Look for in a Data Quality Tool

Depending on whether you are a very small company, small to medium or enterprise-level business, you will need different features. But here are several general features that most data quality software include:

data-quality-tool-featuresStandardization, Normalization and Parsing: This is a large component of organizing data as it is the process of converting various forms of data into one standard or normal format with the goal of integrating all of it into a data platform. Specifically, it involves standardizing each data element according to pre-defined rules.

data-quality-software-toolsMatching / Deduplication and Merging: This feature recognizes copies of data that appear similar, flags them as matches and then merges the data in order to prevent duplicate copies of data. As a result, another benefit is of this feature is reduction of storage sizes.

big-data-quality-toolsData Cleansing: Corrupt, erroneous or redundant data is taken out of the data source. In this feature, data with missing values or data designated as incorrect can be removed.

data-quality-validation-toolsValidation: Human interaction is involved in this step to ensure data is accurate and of the best quality. This step usually takes place after automated data entry, and checks: format, data, codes, ranges and consistency.

data-quality-and-data-profiling-toolsData Profiling / Auditing: This includes analyzing data to understand the structure, visible and hidden relationships between data elements, and looking for specific data. Data elements are meticulously examined to spot aspects such as frequency, minimum and maximum.

Data Quality Tools Comparison Chart

Vendor Software Target Biggest Benefits
Informatica Data Quality Integrates diverse forms of data; supports Microsoft, Deloitte and Accenture Wide scope of services due to vast partner system; address standardization and validation, real-time data; deduplication
IBM InfoSphere

Data science, big data, business intelligence; data warehousing; app migration and data management

Metadata management, Full IBM database stack, address validation / standardization, data quality monitoring, scorecards

SAP Data Services Enterprise resource planning; cloud-based options Rules and controls, integration among SAP tools, scorecards, metadata management
SAS Data Management

Open-source support for cloud-ready tools; simplified user experience; data cleansing for different data sources

Customization possibilities, statistic and data analysis, ArcGIS, metadata management

Talend Data Quality Ease of use; open-source tool; deep integration with other data sources Fast daily data integrations; custom Java tools; real-time information; data cleansing


Reviews of the Best Data Quality Tools and their Features:

How to Pick the Right Data Quality Tool:

After considering the comments from hundreds of users of data quality tools, there are a few considerations which will help guide you in making the right decision:

1. Do you already use other software applications from the provider?

If you already use IBM or SAP products, for example, it is a logical choice to extend the family of software to their data tools. This will often be more economical and result in better integration throughout the products.

2. What in-house data quality tool expertise do you already have?

data quality business softwareMany data tools offer deep customizations, or in the case of SAS, are almost entirely code-based. Consider what in-house expertise you already have.

Getting developers who are familiar with the back-end and coding languages required can be expensive if you don’t already have that expertise.

3. Do you have a clear use-case with intended results?

One thing is clear – the more you know about the content you’ll be working with, and the intended results, the better your selection process and future outcomes.

Consider also the volumes of information you'll be working with and how quickly you need results. Are you processing data for:

While each data quality tool does a little of everything, they don't all offer the same throughput, load balancing for hyperscaling, or support for back-end databases.

4. Understand the capabilities — and limits of data quality software

How deep are your data problems? Data quality tools can't fill in missing data fragments nor make up for old legacy software. You may have to re-examine your whole data frame work if you have missing data; a data quality software can't help much in many of those scenarios.

In addition, not all data quality tools can provide solutions for all data problems. Specialized standalone data tools, while very powerful, require a lot of knowledge to use successfully.


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