Why is Data Normalization Important?

Data is the lifeblood of good decision-making. However, when you’re managing large amounts of data it’s easy to store information that’s irrelevant to what you need, filled with duplicates, or completely unstructured. This makes it harder to use, and takes up a lot of resources.

One way to reduce these problems is through data normalization. We’ll look at what data normalization is, the benefits, and how it works.

What is data normalization?

If you rely on data-driven decisions, you likely have a lot of data at your fingertips. Data normalization is a key part of making sense of this vast sea of information.

In applying data normalization, you are seeking to put your data into a usable order, while also making it consistent and error-free. The name ‘normalization’ comes from the rules the process follows: ‘normal forms’. These rules guide the creation of multiple, separate tables and the relationships between them, and there are six possible tiers – starting with 1NF, all the way up to 6NF. However, most normalization only goes up to 3NF.

Is data normalization the same in machine learning?

In short: no.

While researching data normalization, you may have noticed that there’s a process in machine learning referred to by the same name. Unlike database normalization, this is focused on transforming features to be on the same scale, rather than organizing a set of data.

How does it work?

We’ve referred to data normalization as a process, and this is because there are several tiers that must be followed in a set order. As mentioned earlier, most normalization goes up to 3NF so let’s take a look at the first three tiers.

First normal form (1NF)

This is the most basic form. It focuses on removing duplicate data, and separating it out into tables, each with a specific key. Each call should contain single, unique values (otherwise known as atomic values).

Imagine you have a dataset with a product number and a column for potential suppliers. In 1NF, you would separate out the suppliers into their own table, and then link the product numbers via a key.

Second normal form (2NF)

With the first normal form in place, you can progress to the second. Here, any data that can be used by multiple tables should be placed into a unique, single table.

Let’s say your supplier table features their address. This address is also needed in other areas of your database, including ‘invoices’ and ‘incoming orders’. Rather than have the same data repeated three times, 2NF pulls the addresses into their own table and links back to that with a foreign key.

Third normal form (3NF)

At this stage, any fields that don’t directly relate to the primary key shouldn’t be in that table. They shouldn’t have any indirect dependencies, and shouldn’t be something that could relate to multiple entries.

So, for our supplier table, you might have a column for their country, and a column for their preferred currency. Their currency is reliant on their country, creating a dependency. To achieve 3NF, we need to separate this out into its own table.

And beyond…

For complex datasets, it’s possible to separate every inch of information into its own table – but this isn’t necessarily practical. Unless you have a lot of specific dependencies and need a particular level of granular detail, 4NF, 5NF and 6NF are likely to be an inconvenience, rather than helpful. Normalizing data at these levels takes up resources and slow queries down, often for little benefit.

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What are the advantages of data normalization?

There are several advantages to data normalization. Let’s take a look at them in turn.

1. Better database organization

There are many ways to organize your data from medallion architecture to the Zachman Framework. Whatever method you choose will impact your organization, so it’s important to make sure it’s easy to follow. Data normalization helps ensure that things don’t get messy – no duplicate records, or tables that link to nowhere.

2. Reduces redundancy

As we’ve mentioned, normalization helps reduce duplicates. This is hugely important, because data redundancy clogs things up. If you have more than one instance of the same data stored in two different places, you’re using up more storage space than you need to. Multiply this innumerable times and you can have a major storage issue in terms of cloud or disk space.

The result can be higher costs, slower processing, and less informed decisions as the system has to wade through much more data than it needs to. Additionally, reducing redundancies means you’ll have fewer update anomalies, where updates only apply to one instance of a repeated item of data rather than to all of them.

3. More consistent data

One major problem for business is having teams that are working off slightly different information. This can be avoided through implementing normalized data, ensuring it’s the same no matter who is looking for it. You’ll no longer have accounts using different tables to HR – instead, they’ll all be on the same page.

4. Improves security

Security is an ever-present concern for most businesses, and data normalization can play a huge part. When data can be more accurately located, it becomes clearer where it came from, where it currently is, and who has access to it.

This allows for better informed security testing, as well as the creation of clear access policies. For specific types of data, it can also make breaches easier to spot. For instance, imagine you track every failed login attempt. In an unstructured dataset, these attempts will be spread out and difficult to see. In normalized data, they’ll be very clearly linked and much easier to track – so if there’s one internal email address that someone is attempting to bruteforce, you’ll notice.

5. Reduces cost

The more data you store, the higher your costs. Reduce the need for server space by optimizing your data, and you can save money.You can also cut down the time spent finding the right information, which frees up your employees to focus on more important, profitable tasks.

Additionally, you can streamline your training requirements. With everyone using the same processes, and the same data, you can run sessions for larger groups instead of having to provide specialized training sessions targeting specific ways of dealing with certain data groups.

6. Data becomes easier to work with

When there are fewer errors and duplications, you have a more manageable collection of data. This can be a major advantage when dealing with big data sets. It also ensures your data analysis and big data visualization tools produce clearer outputs, and you can find more meaningful insights.

When data is easier to work with, fewer errors are made. This means you’ll be better placed to avoid insertion anomalies (where you can’t add more data because you’re missing other items of data) and deletion anomalies (where you inadvertently lose data you need for a routine because of a deletion elsewhere in the database). Overall, this makes your data easier – and more trustworthy – to work with.

Who should normalize their databases?

Anyone who uses large amounts of data for data-driven decisions.

As you can see from its many benefits, data normalization allows greater ease of use and accuracy. It also cuts the likelihood of anomalous data having an unrepresentative effect.

Particular examples of industries that should implement data normalization include finance and retail. In these areas, the volume of data is huge, so it requires structuring in a way that makes it easier for meaningful and consistent processing to take place. One example is online transaction processing (OLTP). This requires high levels of data quality and recoverability in order for it to work effectively.

All businesses are different, of course, but, in general, retailers should definitely consider normalizing their data, whether it be on a product table, customer table, or their entire database. Data-heavy processes such as lead generation and call center analytics can hugely benefit from a degree of normalization, especially in terms of eliminating irrelevant data.

In both finance and retail, the consequence of mistakes is huge and usually very expensive, so any approach that can help with minimizing error should be looked at.

Are there any drawbacks to data normalization?

There are three main disadvantages to data normalization.

1. Data duplications aren’t allowed, so you have to join between tables where the same data is needed across more than one location. These joins can reduce query speed.
2. Indexing isn’t quite as efficient with table joins. This reduces query efficiency even further.
3. The process of normalization takes time, especially with enormous data sets. Add in additional steps, like utilizing data enrichment tools before you start, and you’re looking at a huge time investment.

However, in the right application, normalization is worth doing. Here are some of the ways you can help it succeed.

Best practices in data normalization

1. Spend sufficient time analyzing the data to know what you’re dealing with, including being sure where minority data should remain intact, and that you’re only dealing with high-quality data
2. Use levels of normalization incrementally – start gradually and build up. You have to use the different levels of normalization sequentially in any case, but it’s worth pausing between each to assess how it is working for you.
3. Define relationships between databases accurately, with clear key and column names.
4. Document the normalization process so that it’s clear to others what’s happened to the data and the purpose behind it
5. Validate and verify the normalized data to ensure its accuracy and reliability
6. Follow good database management practices and regularly review and update as necessary
7. Use appropriate tools and techniques so that processes such as data streaming to ensure it flows properly across normalized database structures across the business.

Being clear with your data

When there are lots of collections of data in varying forms across a business, it can be very difficult to get valuable insights and discern any key patterns. Data normalization can assist in this regard by making tables consistent, thus rendering everything easy to access and understand. Value can therefore be more readily determined, and decisions can therefore be made.

Normalization can bring tremendous benefits in terms of data accuracy and cost savings, so it is certainly worth investigating. As long as you’re aware of the drawbacks, such as longer query times.

So, let’s return to our original question: why is data normalization important? Because, done right, it can transform the fortunes of a data-rich organization.