Your Guide to Data Cleaning Tools

Data cleaning tools are essential for transforming raw information into actionable insights. Clean, high-quality data forms the backbone of informed decision-making, empowering organizations to make strategic and productive business choices. In addition to enhancing decision-making, employing data cleaning tools can help to streamline workflows, optimize team management, and boost return on investment (ROI), making them indispensable assets for any data-driven enterprise.

Explore six popular data cleaning tools and techniques, and several functions for each tool, and take a more detailed look at the benefits of data cleaning and its relevant roles. If you're ready to learn more, consider enrolling in the Google Data Analytics Professional Certificate, where you'll learn how to clean and organize data for analysis, and complete analysis and calculations using spreadsheets, SQL, and R programming.

What is data cleaning?

When you’re working with a data set, the process of data cleaning allows you to remove incorrect, contaminated, improperly formatted, partial, or duplicate data. Faulty data can lead to unreliable outcomes and algorithms, even though they may appear to be sound. Essentially, data cleaning—also known as data cleansing or data scrubbing—is a system for spotting and correcting errors within raw data to enhance the data set’s quality. Cleaning data properly contributes to successful data management, the adoption of artificial intelligence (AI) and automation tools, and faster decision-making within your business.

Statista estimates that the value for the global big data market will reach $103 billion by 2027, which suggests that data is a vitally important aspect of the business world [1].

Popular data cleaning tools and techniques

To ensure the vast amounts of data your organization receives promote informed decision-making, you need to remove the faulty pieces within it using data cleaning techniques with tools such as OpenRefine, WinPure, Melissa, and others. Explore these data cleaning tools in more detail.

OpenRefine

OpenRefine is a free, open-source tool you can apply to your messy data to clean it, transform its format, and expand it with web services and external data. Since it is an open-source and accessible tool, the workers at OpenRefine hope to encourage everyone to engage with data while participating in a supportive community. Take a look at a few of this data cleaning tool’s main features:

• Clustering: Once you find inconsistencies in your data, clustering allows you to correct those issues much faster than if you were to do it manually. If specific entries in your data do not quite align, clustering identifies them so you can merge the data that is meant to match.

• Faceting: With this feature, you can examine your data broadly while also focusing on specific subsets within it. For example, if you’re analyzing survey data, you can spot patterns and trends, such as the number of individuals who “strongly agree” or “strongly disagree” with a point in the survey.

• Infinite undo/redo: You can return to a previous condition of your data set before you applied certain changes. Basically, you can undo any prior change that you made to the data set. Even if you quit OpenRefine for the day, you can still access those changes when you reopen it to continue your work.

OpenRefine cost: Download for free.

WinPure Clean&Match

With WinPure Clean&Match, you can clean impure data, implement AI to remove duplicates, combine multiple records, and create master records. It is built for business and tech users but does not require coding skills or manual work. 

Examples of dirty data can be missing information, duplicated names, and outdated addresses. WinPure Clean&Match can assist you in addressing these challenges by producing faster and more precise data transformation, leading to more accurate insights, better strategies, and stronger marketing results. Additionally, Clean&Match can handle massive data sets while also identifying missing info, replacing it, and enhancing your records, contributing to data enrichment.

WinPure cost: To access pricing, you must sign up for a 30-day free trial.

Alteryx

Alteryx is an easy-to-use, AI-driven data analytics platform that offers a six-step automated process when it comes to cleaning your data. Discover these steps in more detail below:

• Dedupe: Repeated data can occupy too much server space, and the Alteryx data analytics software can remove duplicate data. For example, if a customer has submitted redundant forms, you can take out the extras.

• Remove irrelevant observations: To avoid working with data irrelevant to the problem, you can exclude this info from the analysis, speeding up processing time. For example, if you’re analyzing campaign data from a certain time period, you want to exclude information that doesn’t fall within those parameters.

• Manage incomplete data: To prevent biases or incorrect calculations, you can identify missing values in the data and then incorporate substitutes or flag the missing info.

• Identify outliers: An outlier may distort the meaning of your data, so the Alteryx automated process identifies them, which allows you to determine whether to include or exclude them.

• Fix structural errors: You can correct errors regarding the data’s typography, capitalization, abbreviation, and formatting. You can also remove extra whitespaces.

• Validate: Even though tests for accuracy and consistency occur throughout the automated data cleaning process, you can test a sample of your data after the scrubbing process to ensure its validity.

Alteryx cost: Sign up for a 30-day free trial, then consider the best edition for your needs. Choose between one for your PC or one for other devices.

Astera Centerprise

Astera Centerprise does not require code and provides data profiling and data cleaning options. Like some previously mentioned tools, this one offers error identification, duplicate removal, incorrect information resolution, and data validation according to a set of rules. Learn more about this data cleaning tool’s features:

• Error identification: You can ascertain the error count, blank count, data type, and duplicate count within your data set.

• Duplicate removal: To control data quality, the platform identifies and removes duplicate data.

• Incorrect information resolution: Using the data cleanse transformation feature, you can remove whitespaces, unwanted letters, numbers, and punctuation. It also offers a “find and replace” option to help you establish consistency throughout the data.

• Data validation based on rules: You may define a set of rules to ensure the data adheres to a certain quality level. 

Astera Centerprise cost: Astera also offers a free trial. Contact sales for additional pricing information.

Melissa

For four decades, Melissa has helped businesses improve data quality to gain a more complete understanding of their customers, which can lead to lower costs and an increase in revenue. A few of this tool’s features include:

• Profile and monitor: Identify locations where bad data is infiltrating your system while maintaining the quality of your data.

• Cleanse and standardize: Melissa uses machine learning and AI with complex rules, which you can customize depending on your needs.

• Verify and enrich: You can verify names, phone numbers, global addresses, emails, and IP addresses while adding information missing from your database.

• Match and consolidate: Combine duplicate records to create one fully complete record for each customer, providing a 360-degree view.

Melissa cost: This platform provides three options [2].

1. Pay as you go: Starts at $40 per address.

2. Subscription: Starts at $5,145 for one million records per year.

3. Unlimited: Starts at $16,000 for unlimited records per year.

Data cleaning vs. data transformation

While data cleaning is the process that extracts faulty data from your data set, data transformation occurs when you convert data from one format to a different one. Other names for data transformation are data wrangling or data munging. For example, if you convert a Microsoft Word file to a PDF, you are performing a basic type of data transformation. Regarding a more complex example, you may have stored data in the comma-separated values (CSV) format, but you need to work with the data in extensible markup language (XML) format. To accomplish this, you would need to transform the data from CSV to XML.

Benefits of data cleaning

Cleaning your data offers several benefits, such as error removal, function mapping, and more satisfied clients. Using clean data in your organization can increase productivity and strengthen decision-making. Review these benefits in more detail:

• Error removal: If you’re using multiple sources for your data, cleaning helps remove errors.

• More satisfied clients: Fewer errors tend to make your clients happier. Also, your employees will likely find more satisfaction in their work.

• Function mapping: You can more effectively map what you are trying to accomplish with your data. 

• Improve marketing and sales: Clean data makes your marketing campaigns more productive. Studies have shown that organizations with clean data boast a higher conversion rate.

• Revenue increase: Organizations using clean data can identify new ways to make money. Companies can analyze customer behavior and more effectively target their products.

Data cleaning jobs

Within the field of data analytics, several different types of professionals, such as data analysts, data scientists, and data architects, clean data as one of their responsibilities. These professionals also perform other duties. Uncover a little more about each role.

Data analyst 

Median total US pay: $91,000 [3]

Data analyst duties include collecting data, employing statistical tools, presenting their insights, and building data models.

Data scientist 

Median total US pay: $151,000 [4]

Data scientist responsibilities include identifying useful data, creating and updating algorithms, using data visualization software, and making recommendations to stakeholders.

Data architect

Median total US pay: $173,000 [5]

Data architect duties include designing data structures, developing data security standards, integrating data sources, and collaborating with other departments.

The salary information above is the median total pay from Glassdoor as of July 2025. These figures include base salary and additional pay, which may represent profit-sharing, commissions, bonuses, or other compensation.

Learn more about data cleaning on Coursera

Discover more about data cleaning and analytics with courses, specializations, and professional certificates on Coursera. Whether you want to develop a new skill, get comfortable with an in-demand technology, or advance your abilities, keep growing with a Coursera Plus subscription. You’ll get access to over 10,000 flexible courses.

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