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Data Quality Tools & Criteria for Right Tools [2024 update]

Data Quality Tools & Criteria for Right Tools [2024 update]Data Quality Tools & Criteria for Right Tools [2024 update]

Data cleaning is part of a greater effort to achieve the highest data quality possible in used in business decisions and operations. It requires organizational effort and participation throughout a business and when done correctly, can help to provide valuable insights and analytics for decision making. A few additional benefits associated with data cleaning include:

  • Streamlined business practices
  • Increased productivity
  • Faster sales cycle
  • Better analytics

Given the ever growing quantity of data for many businesses, automation is required in data cleaning. The right data tool can fill in these gaps and manage a number of issues automatically before they have a chance to become truly problematic. This can ultimately help businesses to become more efficient and more profitable in their efforts.

Choosing the right data cleaning tool for your organization is essential to getting the most utility for your investment. To help in your decision making, this post answers the following:

Criteria for Choosing a Data Quality Tool

The right data cleaning practices can have a huge positive impact across an organization, so it’s worthwhile to take the time to choose the right tools to support it. In the case of large or complicated datasets, outsourcing the entire process to a third party could also be considered.

Some of the criteria that should be included when choosing a tool are:

Price: Is it a subscription or one-time fee? Are there add-ons that will cause the price to inflate?

Support: A strong support team can be a big factor in decision making.

Usability: Not only in terms of analytical uses/IT users that are working for setup/implementation, but will business users need this?

Scalability: Whether or not your tool will be able to keep up as your data sources grow and evolve; and how easy it will be to make upgrades and changes down the line


  • Auditing capabilities: Being able to see when and where changes were made to a record is important for internal and external auditing and compliance concerns.
  • Compatibility/integrations: Having a tool that can work with all the data sources that your business utilizes for daily activities.
  • Cloud vs on-premise: A cloud based option opens up many more choices for smaller businesses with limited hardware resources.
  • Metadata support: Metadata is important for avoiding ‘insight gaps’ where valuable data is that could be used for analysis becomes separated from data scientists and other business users
  • Compatibility with different sources: How many, and what sources, can data be taken from? How long does it take to run any processes or to prepare for them?
  • Batch processing capabilities: Being able to program ahead of time regular data cleaning practices can help to ensure the ongoing quality of your data

Considerations for Different Sized Businesses

The size of your business will play a major role in helping you to choose the right tool. There are three general categories that will have differing needs:

  1. Small businesses with 10 employees or less: Businesses of this size generally do not have a need for extensive data cleaning tools.
  2. Medium businesses with 10-100 employees: At a midsize level, businesses begin to encounter an interesting problem where there’s enough data to need the tools and effort to keep it clean, but putting together an entire team isn’t realistic. Subsequently, it is important to choose a robust tool that can help to fill in the ‘gaps’.
  3. Large businesses with 100-500 employees: At this level, the volume of data going in and out of an organization will generally mandate a dedicated team to ensure data quality. However, choosing a high quality tool can help to simplify their jobs and allow them to focus on key quality related tasks.

Common Functionalities of Data Quality Tools

No matter what tool you ultimately choose for your organization, there are several common functionalities that can be found in a wide range of tools:

  • Data profiling: Scanning through data to find patterns, missing values, character sets and other essential characteristics. This will enable to tool to later identify data as irregular.
  • Data elimination: The removal of duplicate data and also data that doesn’t meet the desired profile.
  • Data transformation: For erroneous data that is valuable, it can be transformed into ‘good’ data through correcting typos, standardization, and normalizing numeric values to fall between minimum and maximum values.
  • Data standardization: Putting data into a common format for easier analysis.
  • Data harmonization: Similar to standardization, this practice takes data from a range of sources and puts them into a common format. Unliked standardization, which is about conformity, harmonization is about consistency.  

Data Quality Tools Overview

Every day the number of data cleaning tools available on the market grows. Some of common vendors include:

NameFoundedStatus Number of Employees
OpenRefine2012Open sourceN/A
Trifacta Wrangler2012Private11-50
TIBCO Clarity1997Private1,001-5,000
IBM Infosphere Quality Stage1911Public10,001+
Symphonic Source Cloudingo2010Private11-50
Quadient Data Cleaner2014Public1,001-5,000
Data Ladder2006Private11-50
Nmondal Solutions Datamartist2008Private2-10
Talend Data Preparation2005Public1,001-5,000

Choosing a data quality tool can seem intimidating, but with some careful research and the advice of a trusted 3rd party, it can ultimately be one of the most effective methods of achieving high quality data. Don’t forget to check our data-driven list of data quality software for more.

Note: This article was initially written by Atakan Kantarci. It is now managed by the AIMultiple team.

Access Cem's 2 decades of B2B tech experience as a tech consultant, enterprise leader, startup entrepreneur & industry analyst. Leverage insights informing top Fortune 500 every month.
Cem Dilmegani
Principal Analyst
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Cem Dilmegani
Principal Analyst

Cem has been the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per similarWeb) including 60% of Fortune 500 every month.

Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE, NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and media that referenced AIMultiple.

Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised businesses on their enterprise software, automation, cloud, AI / ML and other technology related decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.

He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.

Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.

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Fan Yang
Feb 22, 2021 at 18:22

Hi Atakan,

I very much enjoyed your article, but would like to add to your criteria in reviewing data quality tools.

I work for BaseCap Analytics, as a data analyst and consultant. We have helped large banks and investment firms address their data issues.

One of the problem in dealing with data issues is that the issues become silo’d, and often times the person with the domain knowledge is not the same person that is working to remediate data issues.

Therefore, it is important that the data quality tool is intuitive and collaborative so that all the key stakeholders can be involved if needed. The ability for organization-wide access to the same data quality dashboard/platform allows for transparency and one source of truth.