You could be sitting on a goldmine of valuable business insights. But if your data isn’t clean, how will you find them?

As organizations both large and small take a keener interest in data-driven decision-making, they are collecting greater amounts of information than ever before. However, inaccurate, out-of-date, or misleading data can do more harm than good. A study of B2B businesses showed that on average, “dirty” data causes 40% of company objectives to fail.

Data cleansing and normalization is the process of identifying and correcting inaccuracies and inconsistencies in your data. An essential step in any data analysis strategy, it ensures your results are accurate and meaningful.

In this article, we’ll discuss how to approach cleansing and normalizing your data in a way that is efficient and effective.

5 Ways to Cleanse Your Data

Keeping your data clean takes a strategic, long-term mindset. Before getting started, spend a good amount of time thinking through your data management objectives and how they relate to data quality.

When you’re ready to start setting up a data cleansing and normalization plan, here’s how to get started.

1. Identify the data sources

If your organization is like most, you have data scattered across a variety of different systems. This can make it difficult to get a complete and accurate picture of your business.

To cleanse your data, you need to first identify all of the data sources. This includes everything from customer databases and financial systems to social media and web analytics. 

Create a map of all the data sources in your organization. This will help you to understand where the data is coming from and how it flows between different systems. Include information on who owns the data, who is responsible for maintaining it, and how often it is updated.

2. Identify the data types

As you map out your data sources, be sure to consider the types of data featured in each system. Data “types” can broadly be classified into several categories:

  • Categorical: Data that can be placed into categories, such as gender, product type, or geographic region
  • Continuous: Data that can take on any value within a range, such as temperature, sales volume, or latitude
  • Discrete: Data that can only take on certain values, such as whole numbers or dates
  • Text: Data that comprises complete sentences or other free-form text
    Scrabble tiles spelling order and chaos

 3. Create a system of rules

Once you’ve identified the types of data in each system, you can better understand how the data should be cleansed and normalized. Together with your data governance team (or, the internal stakeholders who create and use the data), you will need to decide on rules for how each data type should be formatted.

For instance, categorical data may need to be consolidated into a smaller number of categories (e.g., “M” and “F” could be consolidated into “Male” and “Female”). You might decide that continuous data should always be rounded to a certain number of decimal places. Discrete data may need to be converted into a different format (e.g., instead of “MM/DD/YYYY” you may choose to use “YYYY-MM-DD”). Finally, text data may need to have certain words removed (e.g., profanity).

When making decisions about rules, be as specific as possible. Vague rules (e.g., “remove all profanity”) are difficult to implement and can often result in data that is not truly clean.

4. Find the right tools for the job

Now that you’ve agreed on your data logging standards, it’s time to put them into practice. This is typically done through some kind of automated process, such as a script or software program. 

Some organizations attempt to manually cleanse and normalize their data, but this is generally not feasible for large or complex data sets. Not only is it time-consuming, but it’s also prone to error.

If you don’t have the resources to develop your own software solution, there are a number of commercial and open-source options available. These tools work by checking data against the set of rules you have established. Many are capable not only of finding and flagging errors, but of correcting them for you.

Free Book: Practical Guide to Implementing Record Linkage

Interested in implementing an in-house record linkage solution with your own development team without using any outside vendors or tools?

As you compare options, consider the following factors:

Amount and type of data

Some data cleansing and normalization software is limited as to the quantity, types, and sources of data it can work with, so read the specs carefully.


Your software’s interface should be easy to learn and use. Before settling on an option, be sure to consult the team members who will actually be using the tool. Have them watch a demo of the software and share feedback.


Do you need 24/7 access to reliable, up-to-date data? Or would daily or weekly cleanses suffice? Some higher-end data cleansing software works in real time, while other programs process data in scheduled batches.


Your software should be able to grow with you. If you’re anticipating increasing the volume of data you collect, adding new users, or expanding to additional data types, make sure the program will be flexible.

Customer support

Any software that handles your data should have a reliable customer support team behind it, just in case anything goes wrong. Before purchasing, get in touch with a rep to gauge how supportive and accommodating they would be.

5. Make data cleansing and normalization part of your process 

Cleaning and normalizing data is an ongoing process, not a one-time event. As your data changes over time, your rules will need to be updated to reflect those changes. 

If you haven’t done so already, creating a data governance body is a good way to ensure that data cleansing and normalization are given the attention they deserve. Data governance is the process of governing data throughout its lifecycle, from acquisition to archival. 

A data governance body is typically responsible for establishing and enforcing data quality standards, such as rules for cleansing and normalizing data. By making data governance part of your organization, you can ensure that data quality is given the attention it needs to deliver insights that are accurate and actionable.

Colleagues looking at business infographics

Keep it Clean

Data management is an active, strategic undertaking. If ungoverned, data has a tendency to become messy, fragmented, and unreliable.

Don’t let dirty data prevent your organization from becoming efficient and well-informed–or worse, allow it to lead you astray. If you’re invested in collecting data, you should be putting just as much effort into making that data work for you. 

Data cleansing and normalization is a crucial part of any data management strategy. By taking the time to understand your data and establish a set of rules for processing it, you can ensure that your data is clean, consistent, and ready for deeper analysis.

RecordLinker uses Machine Learning to normalize records across your data systems

Interested in improving the quality of your data, but don’t have the time or resources to create a master data management program from the ground-up?  

RecordLinker is here to help. Our data integration and management platform can quickly connect your disparate data sources, identify and deduplicate records, and keep your data clean and up-to-date.