The Best Data Cleaning Techniques for Preparing Your Data

Data cleaning is an essential process in data science because it helps ensure data quality and purity. As organizations become increasingly data-driven, access to accurate data and quality data is fundamental in making informed decisions.

Data cleansing allows you to identify unnecessary files, values, duplicate records, typos, corrupted values, and incorrect data and modify or remove them. You can also use the data cleaning process to convert data to appropriate data formats or consistent terminologies and search for, fill, or remove missing values through imputation, and perform data transformation to ensure consistency.

This article discusses data cleaning methods businesses can use to enhance data quality.‍

1. Remove unnecessary values

In the data preparation phase, you’ll likely end up with unnecessary and irrelevant data points. For example, if you’re analyzing Fortune 500 companies, your dataset may contain information on small businesses or organizations that aren’t on the Fortune 500 list. These are unnecessary values that you should eliminate during the clean-up process.

Unnecessary values can slow down the data analysis process and distract you from achieving certain goals. Other irrelevant values to watch out for include boilerplate text, URLs, and HTML tags. You should also eliminate personal information like names, signatures, and PINs if they aren’t connected to your data analytics.

2. Remove duplicate data

Duplicate records are also common in a dataset. They can be caused by human error or by collecting and combining information from multiple data sources. A good example is when you scrape data from different websites.

If unchecked, duplicate data can lead to overly large datasets that are time-consuming to process. Perhaps worse, an entry that appears more than once can significantly skew results, leading organizations to inappropriate business decisions. Deduplication is a crucial step in ensuring high-quality data.

3. Avoid typos

Typos usually occur as a result of human error and can affect the data analysis process. For instance, software like Excel may fail to analyze certain values because a column or row name was misspelled. This can impact the overall results and create inaccuracies.

To avoid typos, pay close attention to detail during data entry. Do a spell check on all entries to prevent errors. Remember to use consistent uppercase or lowercase letters, especially when dealing with text data. Although two words (like True and true) can have the same spelling, machine learning models may process them differently based on capitalization.

4. Convert data types

A data type represents the information users will process and access. Common data types include boolean (true or false), numbers, and strings (text).

You can end up with different types of data during data collection. For instance, you can find some numbers written as text, while others are numeric. When analyzing such data, numbers in text format are exempt from statistical calculations.

Consider converting data types to specific formats to enhance the data analysis process. This is particularly important when dealing with date formats and categorical data.

5. Search for missing values

Missing values can affect how data analysis algorithms process data. So, always scan and identify missing or null values in your dataset. Whenever you identify missing data, you can deal with it in a couple of ways.

One option is to go through your dataset, observe the data structure, and use the source information to replace missing values. This can be time-consuming, and your manual search may end up with null results—after all, there’s a reason the data wasn’t pulled in the initial scrape. It also carries the risk that you have to make assumptions that affect data quality.

Another option is to drop all entries with missing values. An issue with this approach is that—as any experiment scientists will tell you—sometimes a lack of data is good data, and you may not want to lose this information.

Which approach you choose will depend on what you hope to learn from the data, the type of data it is, and your overall goals and business strategy.

6. Use a clear format

While you can use a number of formatting styles to structure raw data, keep in mind that they can affect how data analysis software extracts and processes information.

If you store your data in different document formats, consider importing the information into a particular file. Using a specific document format for data analysis can prevent confusion and errors, especially during data processing. Normalization of data formats is crucial for maintaining consistency across your dataframe.

7. Translate language

Use consistent terminologies and language in your dataset. For example, using terms like NA, No, Not, and False interchangeably may cause confusion, especially when the data passes through data analysis software.

Some algorithms can’t convert or process multiple languages. This may cause you to spend a lot of time modifying your model to work with the dataset.

8. Remove unwanted outliers

Outliers are unusual data values found in your dataset. They’re caused by sampling errors, natural variation, and data entry errors.

Identifying outliers is quite easy since they usually differ from other values. Outliers can affect statistical formulas and calculations. At the same time, outliers can provide crucial information regarding a particular subject area.

Be careful when removing outliers. Only eliminate outliers likely to affect your data’s reliability and validity. Data visualization techniques can help in identifying and assessing the impact of outliers.

Best practices for data cleaning

Best practices in your data cleaning process can improve the efficiency and effectiveness of your efforts. These practices help ensure data quality, maintain consistency, and streamline your data management approach.

Some key best practices to consider include:

• Work with a copy of your original dataset. This ensures you have a clean, unaltered version to fall back on if needed. It also allows you to compare your cleaned data with the original to verify the effectiveness of your cleaning process.
• Document all steps in your data cleaning process. Detailed documentation serves as a valuable reference for future projects and helps maintain consistency in your approach. It also facilitates knowledge transfer within your team and can be crucial for regulatory compliance in some industries.
• Automate where possible, but maintain human oversight. Automation can save time and reduce the risk of human error, especially when dealing with big data. However, human oversight is still crucial to catch nuanced issues that automated processes might miss.
• Validate your data cleaning results. Data validation involves cross-checking a sample of cleaned data against the original source or using statistical methods to verify the integrity of the cleaned dataset. This helps ensure your cleaning process is effective and hasn’t introduced new errors.
• Standardize your data cleaning approach. Develop a standardized approach to common data issues. This could include creating a library of cleaning scripts or establishing a set of rules for handling specific types of data inconsistencies.

By following these best practices, you can enhance the effectiveness of your data cleaning efforts, leading to more reliable data and better decision-making based on your analyses.

Popular data cleaning tools

Data cleaning tools can streamline your data preparation process, making it easier to handle big data and ensure data quality. Here’s an overview of some popular data cleaning tools that can help automate various aspects of your data cleaning workflow.

OpenRefine

OpenRefine is an open-source tool for working with messy data. It’s particularly effective for tasks like data exploration, cleaning, and transformation. OpenRefine allows you to load data in various formats, including CSV, TSV, and Excel spreadsheets. Its strength lies in its ability to handle large datasets efficiently, making it an excellent choice for big data cleaning tasks.

Key features:

• Faceted browsing for easy data exploration
• Clustering algorithms for finding and merging similar data
• Regular expression support for advanced text manipulation

DataCleaner

DataCleaner is an open-source data quality and data cleansing software based on the Pandas dataframe. It offers both a community edition and enterprise versions, making it suitable for a wide range of users from data analysts to large organizations dealing with complex data management needs.

Key features:

• Data profiling and analysis tools to identify data quality issues
• Wide range of data cleansing and transformation components
• Support for various data sources, including databases, CSV files, and Excel spreadsheets
• Ability to create reusable data quality rules and metrics
• Scheduling capabilities for automated data quality monitoring

KNIME

KNIME is a comprehensive, open-source data analytics platform that includes powerful capabilities for data cleaning and preprocessing. It offers a visual workflow for various data science tasks, making it accessible to users with different levels of technical expertise. KNIME’s modular architecture allows for easy integration with other tools and languages, including R and Python, enhancing its flexibility and extensibility.

Key features:

• Visual workflow editor for designing data cleaning processes
• Extensive library of data manipulation and cleaning nodes
• Support for various data sources and formats
• Integration with external tools and scripting languages
• Scalability for handling big data

Get help from a data cleaning expert

Online tools can make data cleaning accessible even to people who aren’t experts. If you try to clean your own data and experience challenges, or if you’re too busy to tackle it on your own, access the help you need on Upwork, the world’s work marketplace.

You can find and hire data cleaning experts, data scientists, data analysts, and other professionals who can help boost your productivity.

If you’re a professional looking for work, start selling your services and meeting prospective clients on Upwork today.

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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.