6 Important Reasons for Data Normalization

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Most companies are well aware of the importance of keeping customer data accurate. Accurate data about your customers and prospects is critical for segmenting customers, feeding data into marketing automation systems, and generally providing a better experience for those who engage with your brand.

When you hear the term “data cleanliness,” you probably think of missing data, data with typos, or duplicate records that can grind the wheels of your marketing and sales operations to a halt.

One important aspect of clean records that many companies overlook is data normalization, which is often even more important to keeping a customer database accurate and organized. Data normalization is the foundation of the entire data cleaning process. Without normalized data, it is very difficult to figure out how many data errors exist in your customer database.

What is data normalization?

Data normalization means structuring your relational customer database to follow a set of standards.

This improves the accuracy and integrity of your thailand code number data and makes your database more manageable.

In simple terms, data normalization ensures that your data looks, is readable, and is used consistently across all records in your customer database. It does this by standardizing the formats of certain fields in your customer database.

A customer database can contain fields such as first names, company names, addresses, phone numbers, and job titles. There are many ways each of these entries can potentially be expressed in a record.

Here are some examples:

Names: James vs. james, James A. vs. James, JAMES vs. james. Make sure all names are capitalized correctly.

Company names: Acme inc. vs. Acme. Determine whether to include company registration terms such as "inc," "ltd," or "LLC" in the field name. You may want to forego these suffixes for marketing automation purposes.
Phone numbers: 1234567890 vs. 123-456-7890. Make sure your phone numbers are easy to read and compatible with the systems that use them, such as automatic dialing systems. Phone number formatting is important.
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 their data. Normalized data is critical for the systems that use that data, including marketing automation, sales, and reporting systems.