If you’ve ever taken in a bird’s eye view of your organization’s data management habits, you’ve probably realized that there’s a lot of duplicate data floating around, much of it logged in different formats. 

Using messy data to inform insights and decision-making is at best inefficient; at worst dangerous, because you risk being misled. As you collect greater amounts of data, it can become increasingly difficult to “see the forest through the trees”.

Cleaning up and standardizing (“normalizing”) your data is a project well worth considering. With accurate, easily accessible, and clearly formatted records, you can put your business firmly on the path to becoming more data-driven.

Following a manual process to normalize your data may be achievable if your organization is small and collects only a few types of data, but most organizations find this process extremely tedious and error-prone. 

Fortunately, a number of software solutions exist to help with this crucial aspect of data management. In this article, we’ll take a look at what data normalization software has to offer, and how to choose the right solution for your organization.

What Does Data Normalization Software Do?

Data normalization software is designed to help organizations improve the accuracy, accessibility, and completeness of their data. At the most basic level, it includes a range of tools for cleansing and enriching data.

Data cleansing is the process of identifying and correcting inaccuracies and inconsistencies in data. Data quality software does this by comparing your data to a set of known rules or standards. When it finds an issue, it will flag it for you so that you can take action to fix it. The software can also be configured to automatically correct certain types of errors.

For example, data quality management software can be used to detect and correct spelling mistakes in customer names or addresses. It can also be used to standardize data formats, such as dates and phone numbers.

Data enrichment refers to the process of adding missing data or correcting incomplete data. For example, let’s say your marketing team is trying to create a list of all the companies in a certain industry in a given country. They may want to include details such as each company’s size, headquarters location, and founding date. However, they may not have all of this information for every company. Data quality management software can be used to fill in missing values by looking up the information in other sources, such as company websites or public records.

A simple version of these tools may be sufficient if your organization only stores data in one type of system. But what if you have data spread out across multiple departments, stored in different applications, and governed by completely different management practices – or maybe not governed at all?

If your organization is struggling with data silos, you’re not alone. Isolated stores of data evolve naturally in organizations that lack effective data governance infrastructure. In fact, 47% of marketing professionals cite silos as the main barrier to gaining meaningful insights from their data. Breaking through silos is a daunting challenge, and the sooner you start, the better. 

Your best bet for tackling silos is data integration software. This type of software is focused on combining data from multiple sources, making it easier to view and analyze.

Data integration software can also help you move data from one system to another. For example, you might use it to migrate data from an on-premises database to the cloud. 

Data integration software typically includes the following components:

  • Extract, transform, and load (ETL) tools – These tools extract data from source systems, transform it, and then load it into a central repository, such as a data warehouse or data mart.
  • Data quality tools – These tools help you cleanse and standardize the newly loaded data.
  • Data management tools -This functionality helps you manage data within the target system.
    Employee visualizing data on computer monitor

How to Choose Data Normalization Software

Data is everything in today’s business landscape, which means there is a wealth of software options available for organizations looking to improve their data management practices.

Before choosing a software package, consider the following elements of data management.

Ease of use

The software should be easy to set up and use. Integration software should also have a graphical user interface that makes it easy to visually map out the data flow.

Software base

Data normalization software can be either on-premises or cloud-based. Cloud-based solutions are often more flexible and easier to set up and use. However, on-premises solutions may be a better fit if you have sensitive data that you don’t want to store in the cloud.

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Data types

Your software should be able to support the type(s) of data you work with. Some data normalization software is designed for specific data types, while other programs have a broader scope. 

Are you handling structured data, such as relational databases; unstructured data like text files; or semi-structured data like XML files?

Transformation capabilities

The software should be able to handle the types of transformations you need to perform on your data. Some common transformations include: 

  • Joining data from multiple sources 
  • Splitting data into multiple files 
  • Filtering data based on certain criteria 
  • Sorting data 
  • Aggregating data

Your transformation needs will depend on your data goals as well as the current state of your data.

Data sources

Is your data currently stored on-premise, in the cloud, or in mobile sources? How many different sources will your software need to handle? There are limits to the types and numbers of sources many off-the-shelf data normalization programs can integrate.

Even if you only have a few data sources today, consider whether this is likely to change in the foreseeable future. You don’t want to end up outgrowing your software and having to migrate to a new solution.

Update frequency

Do you need real-time access to updated data, or would near-real-time or batch updates be sufficient?

Most basic data normalization software tends to process new data in batches. If you need up-to-the moment information (for example, on financial data) you may need to invest in a higher-end program.

Privacy and security

How sensitive is your data? What level of data encryption, masking, or anonymization should the software provide? 

At minimum, your data normalization software should encrypt data while in transit and at rest.


How quickly will your data volume grow? Will you be adding new types of data regularly? Your software should be able to evolve alongside you.

Access to expertise

Do you have the in-house expertise needed to manage the software? What kind of support does the company provide? 

Try getting in touch with representatives before purchasing the software to gauge how responsive and helpful they are. Remember that issues with your data normalization software could have significant consequences, so you need to be able to trust the company.


Of course, cost is always a consideration when choosing software. But be sure to balance cost with the other factors on this list. The most expensive software isn’t necessarily the best option for your organization. 

Vertical green lines of computer code

Get Started with Data Normalization Software

When it comes to data, collection is only half the battle. If that data is not organized, it can be difficult to discern any patterns or meaning. Data normalization software can be a major asset in your organization’s journey to becoming more efficient, drawing deeper insights, and making wiser decisions.

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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.