First-Party vs. Third-Party Data

Companies today have tons of valuable data. And much of that data is collected by the company through interactions with their various audiences. This is first-party data. But there is also data collected by external entities that is also quite valuable. It’s called third-party data. 

First-party and third-party data both play important, yet slightly different, roles in your master data management (MDM) strategy, providing a comprehensive and accurate view of your key business entities. By leveraging both types of data, organizations can provide everyone across the organization with the golden records they need to make better decisions and operate more effectively. Let’s take a look.

‍What Is First-party Data?

First-party data, simply put, is the data your organization collects about your audiences such as customers, prospects, partners, suppliers, etc. It can include basic demographic information such as name, job title, or email address provided via a marketing lead form. Or it could be responses to a customer survey. 

First-party data also includes data such as how many times a user visited your website, what pages they’ve visited, how long they’ve stayed, and which content they’ve downloaded. Customer purchase data, as well as the social media profiles that follow your company, also fall into the bucket of first-party data. 

First-party data is a critical part of your MDM strategy for many reasons. It helps you understand your audiences better, revealing patterns, preferences, and behaviors that inform smarter decisions across marketing, sales, customer support, and supplier relations. For example, first-party data helps your marketing team retarget users who are consistently visiting your site,  informs your sales team of potential cross-sell and upsell opportunities, and alerts your support team when a customer is struggling.

‍What Is Third-party Data?

Third-party data, on the other hand, is data collected by an organization outside of your own, which also often includes demographic, firmographic, and behavioral information. It includes publicly available information, data that an external organization collects on its own and sells, and data that a company aggregates from numerous other sources, processes, and sells.

Examples of third-party data include:

• Address data from the United States Postal Service
• Demographic data from the U.S. Census
• Intent data based on user behavior on sites other than your own
• Firmographic data about companies and contacts aggregated by a firm such as Dun & Bradstreet

Third-party data plays an important role in your master data strategy because it helps organizations to expand the value of their existing, first-party data either by adding to it or by enriching it. What’s more, many companies today also recognize that their first-party data is incomplete and incorrect, and that external sources, such as address data from the USPS, are more accurate than what their analysts—or their customers—enter themselves. 

‍The Value of First-party and Third-party Data, Together

For years, companies have relied on their first-party data to make data-driven decisions. It powers analytics, drives marketing campaigns, reveals new revenue opportunities, and helps to mitigate risks. But today, companies are realizing two things: 

1. Much of their first-party data is of poor data quality 
2. The best version of their data often lives outside of their organization

Poor data quality is a common problem. From fat-fingered data entries to inconsistent data trapped in data silos, organizations realize that they must fix their bad data in order to make it useful. Part of how they can do that is through enrichment with external, third-party sources. By enriching their data, organizations can improve data quality and increase the completeness of data. And that makes integrating data across disparate systems and data silos much easier.

This is where AI-native master data management (MDM) comes in. By combining built-in data quality capabilities —which standardize and validate data—with powerful, machine learning-driven ID linkage and enrichment capabilities—which match internal, first-party data with the best external reference data—AI-native MDM enables organizations to add additional attributes to their existing data. As a result, they can curate disparate data across data silos to deliver the best version of data for use in analytical and operational use cases. 

To learn more about how AI-native MDM improves the quality of your first-party data via third-party data enrichment, please download our ebook.