Everything You Need to Know About Data Enrichment

Over the past 13 years, data has grown at a rate of 74x, from just two zettabytes in 2010 to more than 147 zettabytes in 2024. And it’s predicted to grow even more—surpassing 180 zettabytes by the end of 2025. It's not just data's size that has expanded; its complexity has increased, too. More systems and more sources are capturing data, making it difficult for data to remain clean and consistent.

Further complicating matters, much of an organization’s data is incomplete, which wreaks havoc on reporting and analytics. Incorrect, incomplete data leads to incorrect, incomplete insights, which, in turn, lead to bad decisions. It’s a vicious cycle, one many organizations can't seem to stop…at least not without taking some decisive action. 

What Is Data Enrichment?

Data enrichment is a foundational data management process where organizations enhance their existing, internal datasets with information that is generated from reputable, generally external data sources. These sources could include data about organizations, people, or parts, or data that could be used for sales and marketing, analytics, risk management, and more.

Using data enrichment, businesses can standardize and validate their data, match internal and external data, and add additional attributes to it. Here’s how it works in Tamr’s AI-native MDM solution: 

1. Businesses use data quality capabilities to tap into external data sources so they can establish data standards, verify firmographic details, and normalize information across their systems. 
2. Then, using unique IDs, they link their internal data with external data to find the sources that best meet their unique business requirements.
3. Finally, the unique, linked IDs match the business with trusted vendors so they can choose and add new, relevant columns based on selected external sources and attributes.

How Are Businesses Using Data Enrichment to Enhance Their Processes?

Data enrichment enhances data cleansing and adds value to data management processes in many different ways, including:

• Sales prospecting: With data enrichment, companies can better understand and target the right prospects. For example, by enriching prospect data with firmographics, employment trends, and technographics (such as the software and tools a company uses), businesses can better identify ideal prospects and lookalikes, supporting more targeted outreach and sales strategies.
• Strategic decision making: Data enrichment helps organizations to identify major organizational improvement opportunities by benchmarking external data. In particular, by conducting peer analysis on talent profiles and job postings, businesses can determine how to better retain top talent.
• Operations forecasting: Using data enrichment tools, organizations can forecast key metrics and economic factors in the business for use in operational decision making. For instance, based on demographic shifts and other private company data, a business can forecast which customer segment is most likely to grow.
• Risk management: Data enrichment enables businesses to identify key operational, financial, and reputational risks by identifying the people and businesses they are dealing with. As an example, by identifying parent companies and seeing if they are sanctioned or in certain geographic locations, businesses can avoid supply chain disruptions and exposure to risks.

What Are Common Types of Data Enrichment?

The goal of data enrichment is to improve data quality by augmenting existing data with additional or missing data from trusted sources. While there are many ways that businesses can enrich data, the following are three of the most common:

Firmographic data enrichment

Firmographic data enrichment updates or completes basic—yet critical—company fields including name, location, industry, company size, number of locations, years in business, and ownership type. B2B companies use firmographic data for sales and marketing, allowing them to better segment and target their customers.

Demographic data enrichment

While firmographic data enrichment focuses on updating company attributes, demographic data enrichment focuses on updating attributes of people, including consumers, patients, and students. Using demographic data enrichment, companies can correct or complete fields such as age, gender, income, education, marital status, and ethnicity. Companies use demographic data enrichment to tailor campaign messaging, target relevant audiences, or better understand customer behavior.

Geographic data enrichment

Geographic data enrichment refers to the enrichment of data related to addresses, postal codes, coordinates, and geographic boundaries. Companies use geographic data to deliver location-based services as well as mapping and navigation functionality.

What Are the Benefits of Data Enrichment?

Companies benefit from data enrichment in many ways. Not only does data enrichment enable them to improve the accuracy of their data, but it also helps them to gain clearer, more complete insights that drive better decisions. Further, when organizations enrich their data using capabilities such as machine learning-driven referential matching, they are able to uncover hidden matches and relationships that are otherwise obscured without external data. Finally, data enrichment tools allow organizations to tap into rich, third-party data sets that exist outside their company walls, enabling them to add color and context unavailable to them.

Through data enrichment, companies can seamlessly embed trusted, high-quality external data insights into their data pipelines, enabling better, more confident decision making, improved operations, and greater business value.

Tamr’s AI-native MDM solution delivers data enrichment capabilities that enable companies to tap into third-party data as a key part of their data mastering process. Using Tamr, companies can validate and standardize common attributes, match internal records to a vast corpus of third-party data, and take advantage of pre-built integrations with leading data providers, enabling them to provide all users with trustworthy golden records that support both analytical and operational use cases. 

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