Data Term Definitions

Data Enrichment

Data enrichment is the process of enhancing existing, internal datasets with information from trustworthy, third-party data sources.

What Is Data Enrichment?

Data enrichment is the process of enhancing existing, internal datasets with information from trustworthy, third-party data sources. These sources could include data about organizations, people, or locations, for example, and be used for sales and marketing, risk management, supply chain management, and more.

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As a platform capability of AI-native MDM, data enrichment uses unique IDs to connect internal data with third-party sources to find additional, missing, or more up-to-date attributes to append to each record. Data quality and data enrichment build on each other to unlock greater value from an organization’s internal data.

Resources

‍Resources 

Guide to Data Enrichment

Everything You Need to Know About Data Enrichment

Why Do Businesses Use Data Enrichment?

By enriching internal data with trusted third-party sources, businesses can fill the gaps in their internal datasets and ensure that their records remain complete, accurate, and up to date.

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Using data enrichment, organizations increase business value in a number of ways—including improving customer analytics, supplying additional context to enhance decision-making, preventing bad or duplicate data from entering enterprise systems, mitigating risks, and complying with regulatory mandates.

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Why Data Enrichment Is so Important for Your Organization

What Are the Most Common Types of Data Enrichment?

The most widely used approaches to data enrichment include:

  • Firmographic enrichment: Updating or adding basic, yet critical, data to company records including name, location, industry, company size, number of locations, years in business, and ownership type. Firmographic data supports sales and marketing by enabling better segmentation and targeting of customers. Examples of leading firmographic data enrichment providers include Dun & Bradstreet, PitchBook, and ZoomInfo.
  • Demographic enrichment: Updating the attributes of people—including consumers, patients, or students—with information such as age, gender, income, education, marital status, and ethnicity. Demographic data enables organizations to tailor campaign messaging, target audiences, and better understand customer behavior. Examples of leading demographic data enrichment providers include Acxiom, Experian, and National Plan and Provider Enumeration System (NPPES). 
  • Geographic enrichment: Updating data related to addresses, postal codes, coordinates, and geographic boundaries. Geographic data supports location-based services and mapping and navigation functionality. Examples of leading geographic enrichment providers include Google Maps, HERE, and Esri.

‍Resources 

First-party vs. Third-party Data

How Does AI-Powered Enrichment Differ From Traditional MDM or ETL Tools?

An AI-powered approach to data enrichment differs from that of traditional MDM or ETL tools, such as Informatica, Semarchy, or Stibo, because enrichment is embedded at the core of the data mastering process from the start. Features such as verified match and always-on enrichment pipelines ensure that records remain accurate, enriched, and up to date in real time without the need for coding or custom builds.

‍Resources 

Master Data Management Showdown: AI-Native MDM vs. Rules-Based MDM

Key Takeaways: Data Enrichment

  • Data enrichment enhances existing, internal datasets with information that is generated from trustworthy, external data sources.
  • The most common types of data enrichment are firmographic, demographic, and geographic enrichment. 
  • AI-powered data enrichment is embedded at the core of an AI-native MDM solution.
  • Businesses use data enrichment to increase business value, improve decision-making, mitigate risks, and ensure regulatory compliance.

Data Enrichment FAQs

Which third-party data providers does Tamr integrate with for data enrichment?

Tamr’s built-in AI data enrichment capabilities enable it to integrate with numerous trusted third-party data providers including Dun & Bradstreet, PitchBook, ZoomInfo, Companies House, the National Plan and Provider Enumeration System (NPPES), and more.

How does Tamr keep enriched data up-to-date over time?

Tamr’s AI-native master data management solution integrates with verified, trustworthy, third-party data providers and delivers always-on enrichment pipelines that ensure records remain accurate and up-to-date.

How does Tamr handle conflicts when different enrichment sources return different values for the same field?

Tamr allows users to specify the logic that dictates which enrichment source should prevail for specific fields and attributes to resolve conflicting values.

Explore more FAQs 
Related Data Terms
Updates

AI-Native Master Data Management (MDM)

A modern data mastering approach built and centered around AI.

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Guides

Agentic Data Curation

Approach to data curation where AI agents analyze, explain, and resolve data issues.

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Data Products

Data Enrichment

The process of enhancing internal datasets with outside data sources.

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Data Products

Data Governance

Rules, processes, and roles that safeguard organizational data.

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Data Products

Golden Records

Unified, shared, trusted views of business entity data across systems and sources.

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Enrichment

Enterprise Knowledge Graph

Contextualized view of the connections and relationships between business entities.

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Enrichment

Entity Resolution

Data management technique that identifies and matches records across systems.

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Get a free, no-obligation, 30-minute demo of Tamr, and discover how our unique AI-native MDM solution can empower you to deliver data you can trust.

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