What Is Data Cleaning? Basics and Examples

If you want to make solid, data-driven business decisions, it’s critical that the data you base them on is accurate. Bad data can lead to bad decisions. To ensure the data you have is accurate, it’s important to take all the necessary steps to clean your data.

This article discusses data cleaning and addresses some of the most frequently asked questions about the data cleaning process.

• Why data cleaning is essential
• The data cleaning process
• Data cleaning examples
• Data cleaning FAQs

What is data cleaning?

Data cleaning is the process of removing or correcting inaccurate, corrupt, or improperly formatted data and removing duplication within a dataset. Any time data is combined or exported into a new form, it’s possible for these errors to take place.

When dealing with large amounts of data, detecting labeling or duplication errors can be very difficult. A data cleaning protocol can help reduce or eliminate these errors so you have the high-quality data you need to make data-driven decisions.

Why data cleaning is essential

The data cleaning process is essential for good, data-driven decision-making. Having a high level of data integrity is a concern for many business leaders.

According to 2021 global data management research from Experian, companies estimate that nearly one-third of customer data is inaccurate. And 95% of all companies surveyed said they had seen negative effects due to poor data quality. This lack of confidence means businesses cannot use their data to make decisions effectively.

Data cleaning is not only crucial for decision-making, but it also has other benefits.

• Saves money and reduces waste. Inaccurate data can lead to bigger problems down the road. If not detected early, a data entry error can cause repeated errors requiring more time and effort from multiple team members. Finding and fixing problems after the fact can be time-consuming and expensive.
• Saves time and increases productivity. Cleaning and updating dirty data saves your team from muddling through bad, irrelevant, or old data.
• Reduces mistakes. Bad data can also create operational issues. Sales and marketing teams often use customer databases to send personalized communications, including past purchase information, first and last names, or other personal information. Keeping data clean can help ensure that customers receive these personalized communications without errors.
• More opportunities to leverage data. An accurate and up-to-date database allows businesses to confidently search for more ways to leverage information. It’s also easier to build on a data program when you have high-quality data.

High-quality data should meet specific criteria

What are the criteria for high-quality data? From accurate information to preset validation rules, we’ve compiled a more detailed list below.

• Validity. Data conforms to your previously defined rules or limits.
• Accuracy. All values included are true and actual.
• Completeness. Includes all available data that is collected in the dataset.
• Consistency. All data is measured and expressed in the same fashion within the datasets.
• Uniformity. The same unit of measurement is used in each entry within your dataset.

The data cleaning process

The data cleaning process must follow a consistent set of steps to ensure it’s managed properly. You can use several different data-cleaning techniques to clean data. Once you know which techniques make the most sense for your business, you can move forward with your data-cleaning process. Here are a few basic steps for keeping your data clean.

• De-duplication of entries. Removing duplicate entries helps ensure that counts are correct and nothing is overcounted.
• Deleting incomplete data. Missing data can lead to invalid conclusions or incorrect estimates; deleting incomplete data helps avoid these issues.
• Remove oversamples. Oversampling introduces bias in your data; removing oversamples helps protect you from inaccurate analysis.
• Remove incomplete or irrelevant responses. It’s important to remove irrelevant responses because they’re not useful in analysis.
• Identify and review data outliers. Outliers may be errors, or they may help you identify important and relevant opportunities.
• Code open-ended data. Open-ended data—like forms where customers reply in their own words—must be normalized to be useful. Use code to find authentic replies and key messaging.
• Check data for consistency. Ensuring data is consistent in formatting, measurements, fields, and values will help avoid inaccurate analysis.

Check out this article on data cleaning steps for detailed information on handling each of these important elements of the process.

Data cleaning examples

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What is data cleaning in practice? Let’s say you’ve just gathered a large number of email addresses or SMS subscribers and you want to find specific data points—like which area codes your subscribers live in—for a marketing campaign. You can sort through that information and create more actionable lists to use in the future. But when you look through your list, you find that the phone numbers were not formatted the same way—some have dashes, some have parentheses, and some do not include an area code. Data cleaning is the process of correcting these inconsistencies.

Cleaning data might also include removing duplicate contacts from a merged mailing list. A common need is removing or correcting email addresses that don’t use the correct syntax—like missing a .com or not having an @ symbol. Data cleaning is needed in any situation where collected data is irrelevant, has missing information, or includes structural errors that don’t meet the requirements for use.

Data cleaning FAQs

We’ve answered a few of the most common questions related to data cleaning below. Check out the following links if you’d like to learn how to classify and manage different types of data, discover some of the best data analysis techniques, or dig deeper into data security for your business.

What is the difference between data cleaning and data scrubbing?

People often use the terms data scrubbing and data cleaning interchangeably. However, data scrubbing is actually a component of data cleaning.

Scrubbing is specifically related to removing old, bad, unnecessary, and duplicate data. Data scrubbing may also reference an automated process for checking computer storage systems for disk reading issues. Data cleaning involves removing unnecessary data, but it also involves fixing or replacing data that you can adjust and use.

What is the difference between data cleaning and data transformation?

Data cleaning refers to the process of removing or adjusting unnecessary or out-of-place information from a dataset. Data transformation refers to the process of converting data formats from one to another (e.g., taking data from an Excel spreadsheet and bringing it into a customer relationship management, or CRM, database).

What are common data-cleaning tools?

Microsoft Excel and Python are useful tools for data cleaning. However, you can now choose from numerous companies that can help make the data cleaning process easier and offer additional functionality.

A few of the most popular data cleaning tools include:

• OpenRefine. Formerly known as Google Refine, OpenRefine is an open-source (free) data cleaning tool. The software allows users to convert data between formats and lets you clean and explore your collected data. You can also use the tool to parse online data and work locally with your collected data.
• Winpure Clean and Match. Winpure will clean, remove duplicates, and cross-match data in an easy-to-use interface. Clean and Match is a desktop application, which may be beneficial if you want to keep your data locally. The software is also interoperable with Oracle, Salesforce, SQL Server, spreadsheets, and CSV files, among other formats.
• Trifacta Wrangler. Wrangler is a connected desktop application that allows you to produce data visualizations, carry out analyses, and transform data. Trifacta’s data wrangling technology uses machine learning to help improve processes over time.
• TIBCO Clarity. TIBCO Clarity software cleans data from multiple sources, including XLS and JSON formats. Clarity adds data mapping, data profiling, sampling, batch editing, and standard duplication removal to its toolset. Clarity is a cloud-based service.
• Melissa Clean Suite. If your company uses Salesforce and Microsoft Dynamics CRM software, Melissa Clean Suite offers both data cleaning and built-in marketing features you may find helpful. Melissa supports Salesforce objects and will integrate with most forms in MS Dynamics.
• IBM Infosphere Quality Stage. IBM's data cleaning offering supports full data quality. IQS helps clean a company’s management databases (e.g., customers, vendors, products, or locations). IQS is a good tool for companies using big data.

What are outliers?

In data cleaning, an outlier is any abnormal data compared to the values of the rest of your dataset. For example, let’s say you’re analyzing data regarding product sales per customer and the average data points range between 10 and 20 purchases per year. However, you find one or two data values of, say, 200. These would be outliers.

In some cases, this outlier points to an issue with the data. However, it’s important to take an additional step to see if this is an error or simply a very valuable customer.

What are cross-dataset errors?

Cross-dataset errors occur when two or more values in a dataset contradict each other (e.g., if your total does not match the sum of all data included in your dataset).

Ready for some data cleaning? Upwork’s here to help

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Upwork is not affiliated with and does not sponsor or endorse any of the tools or services discussed in this article. These tools and services are provided only as potential options, and each reader and company should take the time needed to adequately analyze and determine the tools or services that would best fit their specific needs and situation.