Data normalization helps reduce redundant data and improves data integrity. It ’s also critical for customer data onboarding. Without proper data normalization, data quality issues are plentiful so customers can't get much use out of the data they import. The result is time spent updating data to where they can’t achieve what they want out of your platform. Of course, this inevitably could lead to customer churn.
With most data importers, building data normalization functionality is labor intensive. Thankfully, it can be done automatically instead of manually, so you no longer have to put customer experience at risk in order to save time.
In this post, we dive into the definition of data normalization, why it matters, and your options when it comes to importing data:
What is data normalization?
Why is data normalization important?
Do you always need to normalize data?
The two main data normalization methods
There are lots of slightly varied definitions but when it comes down to it, data normalization is about organizing data in a database so that internal teams can easily use the database for queries and analysis. Database normalization represents a systematic approach of composing tables to eliminate data redundancy and is a multistep process that puts data into tabular form, removing the duplicate data from its relational tables.
Normalized databases make it significantly easier to create, update, and delete rows, while ensuring data consistency and decreasing redundancy.
Data normalization is especially important when importing data into a product, for example with customer data onboarding.
Is data normalization really important? Or is it okay to build a CSV importer or other data import experience without data normalization baked in?
Database normalization is important for a few key reasons:
Eliminate repeated data = more space and better performance - Having repeated data in any software system can cause major disruptions during subsequent transactions and requests. Once duplicates are eliminated, there’s more space in the database. Not only is there additional space, but without duplicate data, systems will run smoother and faster.
Get the answers you need - Once errors and redundant data are gone, the data is clean and can be organized easier. Now that a company has organized data to work with, data analysis can happen much faster.
When it comes down to it, there has to be some form of logic with how your database is organized. If you have large sets of unorganized data, it goes to waste. It can’t be merged let alone properly utilized.
If a table is not properly normalized and has redundancies, then it will not only eat up the extra memory space but it will also make it challenging to handle updates to the database.
Without data normalization, you ultimately cause problems for your customers when they try to utilize the data that they’ve imported into your system. There can be endless data anomalies that make the database too large and messy to be useful. An example of a data anomaly would be if you have an employee management system and you upload a CSV file of all employees with their managerial role written individually instead of being one form of data (role) that gets linked to the employee.
If the name of a specific role gets changed, it would need to be updated with every single affected employee record, instead of being changed just once.
All in all, it’s critical to normalize data: updates run faster, data insertion is faster, there are smaller tables, and data is more consistent. For these reasons, a fully normalized database is always worth achieving.
When it comes to importing data and data onboarding, a lack of data normalization leads to a never-ending string of data quality problems.
Is normalizing data always necessary? Though very rare, there can be cases where you don’t need to normalize data. The main reason why you might not normalize data is because your application is read-intensive, meaning that your application predominantly does database reading as opposed to writing.
When an application is read-intensive, normalized data can actually complicate the queries and make the read times slower because the data is not duplicated, necessitating table joins.
Table joins can also make indexing slower, which again makes read times slower.
If your goal is data integrity and consistency, however, a database normalization is a must!
When most software engineers think of data normalization, they assume that it’s all manual work, but there’s actually another option.
Building data normalization into your CSV importer is a lot of work. Since database normalization completely changes the way table schemas are designed, it’s a tedious process to go through if it’s not performed correctly. You need to ensure that
There are no repeating forms of data
Attributes are correctly dependent on their primary key
Teams can put the burden of manual data normalization on customers and users and require them to fix all the data quality errors if a data importer doesn’t have a data normalization feature. Having customers update data after error messages pop up as they try to import data, however, is a terrible customer experience.
To avoid this, companies too often put the responsibility on the customer who is importing the data by having them read through a lot of complicated CSV documentation or download a CSV template and then force their data to fit into it.
If engineering teams do take on creating a data normalization feature for a data importer to help with customer data onboarding, then they could end up building something that is just as time consuming to create as the core product they’re building that customers are actually paying for. The battle of manual vs automatic is usually a question of where you want engineers to spend time.
There is a new emerging technology that helps teams save huge amounts of time on quality customer data onboarding. Flatfile’s data onboarding platform is a pre-built data importer that you can drop into your web application. It includes advanced import functionality such as data normalization, CSV auto-column matching, data hooks, and more. Flatfile’s data onboarding platform can be implemented in as little as a day as opposed to 3-4 engineering sprints.
Of course it all comes down to cost. For nearly every company, purchasing a ready-to-go data importer that meets their needs and includes the required features, such as data normalization, will be far more cost-effective than paying salaried or contract engineers to build something similar from scratch.
But why spend months building a data importer that you can implement in one day?
Data normalization is critical to ensure data redundancies don’t occur and that your team can work effectively with clean data. With most data import experiences, data normalization needs to be explicitly built in to the data importer as a feature. Flatfile’s data onboarding platform offers data normalization along with other features to help customers instantly get value.
The choice: manual vs automatic is up to you!
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