6 key reasons to normalize data

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Most businesses are aware of the importance of customer data cleanliness. Accurate customer and prospect data is critical to segmenting your customers, feeding data into marketing automation systems, and generally delivering a better experience to those who engage with your brand.

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

But one key aspect of data cleansing that many companies overlook is data normalization, which is often even more important to maintaining a clean and organized customer database. In fact, data normalization drives the entire data cleansing process. Without normalized data, it’s very difficult to fully understand how many data errors there are in your customer database.

What is data normalization?
Data normalization involves structuring your relational customer database to follow a set of rules. This improves data accuracy and integrity, while making it easier to navigate.

Simply put, data normalization ensures that data looks, reads, and thailand country code is used consistently across all records in the customer database. This is achieved by standardizing the formats of specific fields within the customer database.

A customer database might include fields such as name, company name, address, phone number, and job title. Each of these records could be expressed in many ways in a dataset.

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. Decide 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.
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 automated sales dialing systems. Phone number formatting is critical.
Positions: CEO vs. Managing director.
Addresses: 123 Mulberry St. vs. 123 Mulberry Street New York, New York, 10013
These are standard examples of the type of fields that should be normalized.

Every company has different approaches to normalizing their data. Normalized data is critical for the systems that use it, such as marketing automation, sales automation, and reporting systems.