By XanoNovember 14, 2021
These days, almost every company collects data, which is often used to analyze purchase trends or improve company processes. When storing large swaths of data, data normalization helps ensure success, prevent wasted resources, and help you track things like performance and metrics.
Data normalization can seem somewhat complicated, but in modern times it's more or less vital to running any business. Below, we'll provide a brief introduction to the most common forms of data normalization and explain why they matter.
Speaking very broadly, data normalization is a technique that organizes data to appear similar and more cohesive. Obviously, this a very broad and even somewhat circular definition, so let's take a closer look at what data normalization actually entails.
First, let’s break down the definition of unstructured data versus structured data. Structured data is collected from a known method and can be neatly arranged in a logical fashion. Unstructured data has no pre-defined format or organizational structure. The danger of unstructured data is that it makes analyzing and pulling data very difficult, which can slow down a variety of company processes.
Data organization has two main goals. First, organizing data so it looks similar across all records and fields. Second, increasing the overall cohesion of data entry types. The end result of data eliminates unstructured data and duplicates and creates a more logical – and, ultimately, more useful – data storage structure.
Once data is organized, your company can make better use of your data as you'll be able to examine and cross-examine data more efficiently. Whether you're querying data from a SaaS application or gathering data for market research, data normalization will make your life infinitely easier. Unorganized data can lead to a litany of issues from data redundancy to wasted memory to difficulties installing software updates.
At the most basic level, data normalization starts by creating a standard format for all data throughout an entire company. This is very similar to writing a style guide for a publication. Some standard rules for data normalization formatting could include:
However, this is just very basic formatting. The majority of companies will need to go beyond this during data normalization. The stages of normalization are called “normal forms,” the three most common of which are: First Normal Form (1NF), Second Normal Form (2NF), and Third Normal Form (3NF).
This is the most basic form of data normalization and it ensures that there are no repeating entries in a group. Each entry has only one single value and each record must be unique.
First normal form requires:
The primary key serves to uniquely identify each table record, but you can sometimes run into dependency issues with 1NF data normalization. This leads to the need for the Second Normal Form.
Second Normal Form (2NF) means all columns are dependent on the key. If your key is customer ID, then every column has to be something specific to that customer ID. All the rules of 1NF still apply, but you will need to eliminate partial dependencies.
In 2NF, all attributes in non-key columns depend on your primary key. For example, if you're recording customers in a database, each customer has their own unique ID that serves as the primary key. Your columns record information such as the customers' address and phone number. This information isdependent on the customer in question, so it makes sense that the customer ID is the primary key. There are no issues here.
However, what if the database also includes information like the price of an item? Is price dependent on the customer in question? No, the price is dependent on the item. This creates partial dependency, meaning that price is unnecessarily dependent on customer ID.
When you have attributes in columns that don't depend on that database's primary key, you need to separate these columns out and then add a junction column.
For example, you can move the item into its own table with its own primary key. The item type becomes the new primary key, which then connects to the price. Now, the price depends on the name of the item, not on the customer ID, which makes more logical sense. Then, create a junction table to connect your two primary keys (customer ID and item name).
In Third Normal Form (3NF) everything is dependent on the primary key, but not dependent on anything else.
Going back to the above example, a 2NF table could have customer ID, purchase time, phone number, zip code, and state. Everything is dependent on the primary key (the customer ID). The reason this example isn't 3NF is that everything is dependent on the customer, but there's actually a double dependency. Why is that? The state is also dependent on the zip code. Either the zip code or the customer ID is enough to determine the state, which can lead to redundancy.
In order to make this example 3NF, you would need to keep the zip code in your original table, but add a separate table listing zip code and state.
This is probably the biggest benefit of data normalization. It helps prevent unnecessary duplicate data, which can eat up massive amounts of memory space. When you normalize your data, it makes it much easier to merge and match duplicates. The less duplicates, the more space you have in your database.
Lead segmentation is vital to growing a business. Data normalization helps you categorize groups based on factors such as title, industry, rank, and so on. This allows you to create detailed lists based on what is valuable to a specific lead, improving the overall organization of your marketing campaigns.
An unstructured database makes analyzing and evaluating data an absolute nightmare. When your data uses a single standardized organization approach, you spend less time parsing through information when tracking metrics and performance. This saves your marketing team heaps of time and gives you more accurate insight as to how campaigns are performing.
While the above information may be a lot to take in, it does make more sense as you get used to normalizing company data. There can certainly be a harsh learning curve when it comes to data normalization, but it is well worth the benefits. When done properly, data normalization creates a stronger business overall by keeping vital information cohesive and well-organized.
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