Actionable Insights from Scalable Probabilistic Record Linkage

No business thrives without leveraging data for effective decision making. Without a comprehensive plan in place, data silos evolve separately across departments. Company mergers and acquisitions demand unification of databases. When organizations are unable to easily and accurately combine and analyze data from disparate sources, they don’t gain actionable insight for intended outcomes.

Many record linkage solutions don’t have the scalability to keep up with data that continues to grow at an aggressive pace. Often systems aren’t structured to generalize and rely on overly complex, cumbersome, and incomplete rules.

Our unique approach to scalable, probabilistic record linkage frees organizations from this burden of insufficiency and opens a clear path to data- based actionable insights.

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What's Included

Probabilistic vs. Deterministic Record Linkage

The traditional deterministic approach of developing rules for matching records between datasets is time-consuming, tedious, inefficient, and impractical as the quantity of records and data sources continually increases. Our probabilistic record linkage solution does not require exact value matching but instead leverages fuzzy logic to identify matches—expanding linked sets and improving useful insights.

Probabilistic Record Linkage Architecture and Methodology

The Resultant proprietary probabilistic matching algorithm utilizes fuzzy hashing to link approximately equal personal identification information (PII) records together. Detailed and customizable system architecture methodologies are discussed for either scalable on-prem server solutions or cloud-based systems.

Applications of Probabilistic Record Linkage

A probabilistic record linkage system provides a fast, accurate, and secure way to match nearly unlimited records over multiple organizations or departments. The system can be used to develop longitudinal records of a person with different points of interaction, understand behavioral patterns, and conceptualize policy and practices implications on an aggregate level.

We’re proud to help organizations thrive, and we’d love to tell you more.

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Key Facts

The traditional approach of developing rules for matching records between datasets is time-consuming, tedious, inefficient, and impractical as the quantity of records and data sources continually increases.
This configuration accounts for typos, nicknames, and partially missing PII that exact matching isn’t designed to handle.
Scalable record linkage methodology allows organizations with disparate data and varying subsets of PII to find relationships.
Hashing is a function that maps arbitrary-sized inputs to a structure with fixed-sized components; fuzzy is a way of compressing looking at byte-level similarities.
To utilize results from probabilistic record linkage in a transactional system requires an additional layer of data management implementing domain-based rules, thereby reducing false positives to that system’s tolerance level.

Actionable Insights from Scalable Probabilistic Record Linkage

What's Included

Probabilistic vs. Deterministic Record Linkage

Probabilistic Record Linkage Architecture and Methodology

Applications of Probabilistic Record Linkage

Key Facts

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Actionable Insights from Scalable Probabilistic Record Linkage

What's Included

Probabilistic vs. Deterministic Record Linkage

Probabilistic Record Linkage Architecture and Methodology

Applications of Probabilistic Record Linkage

Key Facts

Impact Stories

Healthcare AI: What’s Real, What Works, and What to Watch

Reclaiming the Data Lakehouse: A Business-Centric Approach to Modernization

Data Architecture Modernization Boosts Hospital Foundation’s Efficiencies, Collaboration

Ready to challenge your thinking?

Cookie Notice