6 Critical Reasons to Normalize Data

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Most businesses have some awareness of the importance of customer data cleanliness. Accurate data on your customers and prospects is critical for segmenting customers, injecting data into marketing automation systems, and generally providing a better experience for those who engage with your brand.

When you think of data cleanliness, you probably think about missing data, data with typos and errors, or duplicate records that can clog up the gears of your marketing and sales operations.

But one key aspect of clean data that so many companies usa phone number code overlook is data normalization, which often is even more critical for keeping a customer database clean and organized. In fact, data normalization drives the entire data cleaning process. Without normalized data, it makes it very difficult to fully understand how many data errors are in your customer database.

What is Data Normalization?
Data normalization is structuring your relational customer database to follow a series of standards. This improves the accuracy and integrity of your data, while making your database easier to navigate.

Put simply, data normalization ensures uniformity in how your data looks, reads, and can be utilized—across all of the records in your customer database. This is done by standardizing the formats of specific fields within your customer database.

A customer database might include fields like first names, company names, addresses, phone numbers, and job titles. There are many ways that each of these records could potentially be expressed in a data set.

Here are some examples:

Names: James vs. james, James A. vs. James, JAMES vs. james. Ensure that all names are properly capitalized.
Company Names: Acme inc. vs. Acme. Determine whether company registration terms like “inc,” “ltd,” or “LLC” will be included in the field name. You may want to get rid of these appendages for marketing automation reasons.
Phone Numbers: 1234567890 vs 123-456-7890. Make sure that your phone numbers are easy to read and compatible with systems that use them, such as sales auto-dialing systems. Phone number formatting is critical.
Job Titles: CEO vs. Chief Executive Officer.
Addresses: 123 Mulberry St. vs. 123 Mulberry Street New York, New York, 10013
These are standard examples of the type of fields that need to be normalized.

Every company has different criteria when it comes to normalizing its data. Normalized data is critical for the systems that use that data, including marketing automation, sales, and reporting systems.