3 Reasons to Normalize Your Data

Valuable business data can come from a wide variety of sources, each with its own quirks and pitfalls. Whether it’s a list of web form submissions, event attendees, or target accounts, merging multiple data sets can be a time-consuming task prone to inconsistencies.

To get the most out of their investment, sales and marketing operations leaders should ensure that any data they collect is normalized before it’s put into action.

Here are three of the most important reasons to normalize your data, and some scenarios and examples that show the power of data normalization in action.

What is data normalization?

Data normalization is the systematic process of grouping similar values into one common value, bringing greater context and accuracy to your marketing database. Basically, data normalization formats your data to look and read the same across all records in a database.

For example, you may want all phone numbers to include dashes (2345678910 becomes 234-567-8910) or all states to be abbreviated (California becomes CA). Another example of data normalization is capitalizing proper nouns like contact names and street names.

Normalizing your data ensures that your database is clean, organized, and primed for use in your go-to-market actions.

Why is data normalization important?

1. Reduce duplicate data

One of the biggest impacts of normalizing your data is reducing the number of duplicates in your database. Duplicate contact and account records can create a range of problems in your database, including misrouted leads and misaligned teams. Normalizing your data is the first step in a quality data management workflow.

2. Improve marketing segmentation

Normalizing your data will help marketing teams more accurately segment leads, particularly using job titles, which can vary greatly among companies and industries. Data normalization can apply common tags or labels across a large list of these values to help segment and prioritize outreach.

3. Enhance performance and metrics

Databases that are poorly maintained and not standardized can cause major headaches when it comes time to analyze performance. Standardizing your data formatting makes this analysis significantly easier. For example: Say you want to know how many contacts with a job title of “director” were collected in your most recent campaign. If you’re not controlling for variations such as “sr. director” and misspellings such as “dirrector,” your analysis could be way off.

When should you normalize data?

Every company uses different factors to normalize data. Ensuring multiple data sources are standardized as they join your database is important for critical go-to-market activities such as customer segmentation, territory planning, and prospecting.

Here are three common scenarios where data normalization is needed:

Web Forms

Many organizations collect prospect and customer data through web forms. Two prospects who have the same job responsibilities might fill out the form differently, where one enters “Sales Manager” and another uses “Manager, Sales.” Without a system to normalize this title data, the values will lack uniformity, causing problems with sorting and segmenting.


When registering for a webinar or other event, attendees typically use some kind of online form. Some of those people may use all lowercase as they type in their information, while others might use sentence case. When it comes time to personalize an email campaign — or, in the case of live events, print name tags — data normalization ensures your collateral is on point.

Manual or “batch” uploads

As sales reps do their own outbound prospecting, they connect with qualified leads and enter or upload a list of prospect data into a database. Applied across an entire sales team, this batch process leaves plenty of opportunities for variation in a number of important fields.

Data normalization examples and benefits

Any field can be normalized. The common ones are job title, company name, URL, address information, and phone number. Here are some examples:

Raw DataNormalized DataBenefit
123456789123–456–789Prevent misdials and make dialing easier.
VP SalesVice President of SalesTitles will conform to allow for marketing segmentation.
RingLeadRingLead, Inc.Helps reduce duplicates if matching requirements include company name.
https://www.zoominfo.com/about/awardswww.zoominfo.comHelps reduce duplicates if matching requirements include the website address. Also improves requirements to link leads to accounts.
200 Broadhollow Rd200 Broadhollow RoadHelps reduce duplicates if matching requirements include address.
STEVESteveImproves email deliverability.

Learn more about how to normalize your data with ZoomInfo Data as a Service.