Identity Linkage Data: Best Identity Linkage Datasets & Databases
What is Identity Linkage Data?
Identity linkage data refers to information that is used to connect or link different data sets or records belonging to the same individual or entity. It typically includes unique identifiers such as names, addresses, phone numbers, social security numbers, or other personally identifiable information. This data is crucial for establishing relationships and connections between various data sources, enabling organizations to gain a comprehensive understanding of individuals or entities across different platforms or databases.
Examples of Identity Linkage Data include email addresses, phone numbers, social media handles, and IP addresses. Identity Linkage Data is used to connect and link different pieces of information or data about an individual across various platforms or databases. It helps in creating a unified view of an individual’s online presence and behavior. In this page, you’ll find the best data sources for Identity Linkage Data.
Best Identity Linkage Data Databases & Datasets
Here is Datarade's curated selection of top Identity Linkage Data. These trusted databases and datasets offer high-quality, up-to-date information.
Factori Identity Data | One Billion+ Identity Linkages, Validation and resolution Data|
Stirista's Purchase Intent Data, Event Data, Consumer Behavior Data, Interest Data, Identity Linkage Data - US
Salutary Data | B2B Identity Graph Data | 72M+ Complete and Regularly Updated US Identity Profiles | Personal, Professional, and Company Data Linkages
Machintel Global Mobile Ad IDs (MAID) | Identity Linkage Data | MAIDs<>Hashed Emails | MAID<>HEM | 650MM+ Unique IDs tied to IP Address and Lat/Long
Alesco Email Database - Email Address Data - 2.3+ Billion US email records - available acquisition marketing and identify resolution!
Datastream Group Identity Graph Data USA by (MAIDs matched to PII)
BDEX Identity Graph for Identity Resolution⎢USA⎢900M Identity Linkages
Identity Linkage Data (HEM<>MAID<>IP<>timestamp) data records (US only)
Ameribase Digital - Identity Graph Resolution and Linkage Dataset - 60 MM HEMs to IP Pairs
Popular Use Cases
Identity Linkage Data plays a pivotal role in various business applications, offering valuable insights and opportunities across industries.
Frequently Asked Questions
What is Identity Linkage Data?
Identity Linkage Data is a type of data that connects different data sources with each other, allowing for the creation of a more comprehensive and accurate profile of an individual or organization. This data typically includes personal identifying information such as name, address, phone number, email address, and social security number, among others.
How is Identity Linkage Data collected?
External data providers typically compile Identity Linkage Data by using various sources, such as public records, credit bureaus, marketing databases, and social media platforms. They use algorithms and machine learning models to analyze the data and link it together, allowing for the creation of a more complete profile.
What is Identity Linkage Data used for?
Identity Linkage Data is used for a wide range of applications, including marketing, fraud detection, identity verification, risk assessment, and customer profiling. By linking different data sources together, organizations can gain a more accurate and complete understanding of their customers, and make more informed decisions based on this information.
What’s a quality checklist for Identity Linkage Data?
When evaluating the quality of Identity Linkage Data, companies should consider factors such as the accuracy and completeness of the data, the sources used to compile the data, and the compliance with relevant data privacy regulations. It is also important to assess the level of duplication and overlap in the data, as well as the relevance and timeliness of the information provided.
How is Identity Linkage Data priced?
The pricing models for Identity Linkage Data vary depending on the provider and the specific dataset being offered. Some providers may offer a pay-per-use model, where customers pay for each individual record or query, while others may offer a subscription-based model, where customers pay a recurring fee for access to a specific dataset or set of datasets. Other pricing models may include data licensing fees or revenue-sharing agreements, among others. It is important for companies to consider the pros and cons of each pricing model and select the one that best fits their specific needs and budget.