data science

Data cleaning: What it is, examples, and how to keep your data clean in 7 steps

As an industry, we rely on data to separate the signal from the noise, unearth insights, and make better decisions. Inconsistencies in data entry, incorrect or missing values, and extraneous information all muddy the waters, making it difficult to get accurate insights and eventually eroding trust, even for organization’s with mature business intelligence initiatives. As they say, garbage in, garbage out. 

If your users do not trust the data, then it doesn't matter if you have empowered them to analyze the data themselves with self-service analytics tools. They simply won’t adopt them. 

That’s why data cleaning is critical to getting the most value possible from the modern data stack.

What is data cleaning?

Data cleaning is the process of identifying and correcting errors and inconsistencies in data sets so that they can be used for analysis. It is a crucial step in data preprocessing and is essential for ensuring the quality and reliability of the data used for analysis or machine learning applications. Thorough data cleaning helps you get a clearer picture of what is happening within your business, deliver trustworthy analytics, and create efficient processes. 

So, why is data cleaning so important?

In a word: accuracy. The more accurate your data set, the more accurate your insights will be. And as research from Harvard Business Review points out, when it comes to making business decisions, whether by executives or frontline decision makers, every insight matters. That's why data cleaning should be at the top of your list of priorities if you want to get the most out of your data. In this post, we will discuss the top five benefits of cleaning your data, real-life data cleaning examples, and seven steps to follow to clean your data properly.

Top data cleaning benefits

Data cleaning is an important part of data management that can have a significant impact on data accuracy, usability, and analysis. Through data cleaning techniques such as data validation, data verification, data scrubbing, and data normalization, businesses can ensure the accuracy and integrity of their data. Data cleaning is an essential data management task that can provide many benefits to organizations including: 

Improved data accuracy

By regularly cleaning data, especially as part of an automated data pipeline, it is possible to reduce the risk of errors and inaccuracies in data records. This data integrity is essential for data analysis and allows organizations to make data-driven decisions with greater confidence.

Increased data usability

Clean data can be trusted in a wider array of use cases by data professionals like analytics engineers, making data more accessible and valuable across different areas of the business and to different kinds of users. By cleaning data, organizations can ensure that data is in a consistent format and can be used for a variety of data-driven tasks.

Easier data analysis

Clean data provides the foundation for data analysis, making it easier to gain insights from data. It is important to ensure data records are accurate and up-to-date in order to deliver reliable data analytical results.

Ensure data governance

The right data governance program, where data is secure and only accessible by the right individuals is an essential component of any data strategy. With proper data cleaning, organizations can adhere strictly to data governance initiatives that protect privacy. 

More efficient data storage

Data cleaning can help to reduce data storage costs by eliminating unnecessary data and reducing data duplication, whether you’re using a cloud data warehouse or a traditional on-prem solution. By consolidating data records, organizations can minimize data storage requirements and optimize the use of data resources.

Real-life examples of data cleaning 

Data cleaning is a crucial step in any data analysis process as it ensures that the data is accurate and reliable for further analysis. Here are three real-life data-cleaning examples to illustrate how you can use the process: 

Empty or missing values

Oftentimes data sets can have missing or empty data points. To address this issue, data scientists will use data cleaning techniques to fill in the gaps with estimates that are appropriate for the data set. For example, if a data point is described as “location” and it is missing from the data set, data scientists can replace it with the average location data from the data set. Alternatively, organizations can find a data point from another data source to fill this gap.


In data sets, there could be data points that are far away from other data points in terms of value or behavior. This can skew the results of data analysis and lead to false results or poor decisions. That’s why understanding outliers and anomalies is very important for ensuring accuracy. To address this issue, data scientists can use data cleaning techniques to identify and remove outliers in data sets. 

Data formatting 

Data formatting includes changes such as converting data into a particular data type, changing the structure of the data set, or creating the right data model. Inconsistent data types and structures can lead to errors during data analysis, so data scientists should use data cleaning techniques to ensure data sets are formatted correctly. For instance, data scientists can convert categorical data into numeric data or combine multiple data sources into one data set.

How to clean your data?

Creating clean, reliable datasets that can be leveraged across the business is a critical piece of any effective data analytics strategy, and should be a key priority for data leaders. To effectively clean data, there are seven basic steps that should be followed: 

Step 1: Identify data discrepancies using data observability tools

At the initial phase, data analysts should use data observability tools such as Monte Carlo or Anomalo to look for any data quality issues, such as data that is duplicated, missing data points, data entries with incorrect values, or mismatched data types. 

Step 2: Remove data discrepancies 

Once the data discrepancies have been identified and appropriately evaluated, data analysts can then go about removing them from the existing dataset. This may involve removing data entries or data points that are irrelevant, merging data sets together, and ensuring data accuracy. 

Step 3: Standardize data formats 

After data discrepancies have been removed, standardizing data formats is essential in order to ensure consistency throughout the dataset. For example, one data set may contain dates formatted differently than another data set. Data analysts should ensure that all data is stored in the same format, such as YYYY/MM/DD or MM/DD/YYYY, across all data sets. 

Step 4: Consolidate data sets 

Then, different data sets can be consolidated into a single data set unless data privacy laws prevent them from doing so. Often, this requires breaking down silos between datasets and bringing them together. Many organizations rely on emerging data architectures, whether they’re using or considering a data lake, data warehouse, or data lakehouse, to do so. Consolidating data sets makes data analysis more efficient as it reduces data redundancy and streamlines the data processing process. 

Step 5: Check data integrity 

Data professionals should then check for data integrity by ensuring that all data is accurate, valid, and up-to-date before proceeding to data analysis or data visualization. This is done by running data integrity checks or data validation tests on the data. 

Step 6: Store data securely 

Then, data professionals must store data securely in order to protect it from unauthorized access and data loss. This includes encrypting data at rest, using secure file transfer protocols for data transmissions, and regularly backing up data sets.

Step 7: Expose data to business experts

Finally, the last step is exposing data to business users. These domain experts have deep knowledge, and can quickly help identify data that’s inaccurate or out of date. This mutual partnership between data and business teams requires the right self-service business intelligence solution, so business users can focus on exploring data to find data cleanliness issues. 

By following these seven data cleaning steps, data analysts can ensure data reliability and integrity while also reducing data redundancy. This in turn allows data scientists to make trustful insights from their data and improve the overall accuracy of data-driven decisions. 

Make quicker and better decisions from your data

It’s crucial that you take the time to clean your data before turning it into insights. The benefits of data cleaning are numerous and can save you a lot of time and effort in the long run. This is especially true for organizations taking a modern approach to data cleaning, where business users are brought into the process earlier. 

If you’re looking for an easier way to get insights into your clean data, or bring business users to your data, ThoughtSpot offers a 14-day free trial so you can see how to find valuable insights with self-service analytics yourself.

With ThoughtSpot, you can easily drill down into your data and get accurate insights instantly – without any headaches. So what are you waiting for? Sign up today!