Insight from Disparate Data: Scalable Probabilistic Record Linkage

Before data can be used to provide actionable insights, the barriers that prevent data from becoming actionable insights must be broken down. A primary barrier to actionable insight is combining and analyzing data from disparate sources. Data is often stored in silos within various business departments, across government agencies, or commonly as a result of a merger or acquisition.

Even after the sometimes cumbersome procedural, organizational, and legal issues have been addressed, the technical challenges associated with combining data remain. This challenge requires record linkage.

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What's Included:

Defining Probabilistic Record Linkage

The objective of record linkage is to develop a comprehensive view of all relevant information pertaining to the same entity, whether a person, business, or event.

Applications of Large-Scale Probabilistic Record Linkage

Large-scale record linkage can be used in various use cases where data silos evolve and grow separately, such as state government agencies with separate funding sources, or through business mergers and acquisitions.

Record Linkage Considerations

When linking records, there are three common problems that arise: scalability, thresholds, and flexibility.

“When combining data silos, the goal is to link tens of millions of records that live in numerous tables and span multiple silos.”

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

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

Key Facts

  • As data continues to grow at an exponential pace, many record linkage solutions fail to scale to hundreds of millions of records or be readily maintained. The systems are incapable of generalizing as new data silos are incorporated and often rely on a complex set of business rules.
  • When probabilistically linking records, thresholds must be set in order to constitute a probabilistic match, or a basis for how similar records relate. It is imperative to have an automated process in place to minimize error both within and across data silos.
  • The Resultant data analytics team developed a unique system to probabilistically link records. This methodology allows businesses with disparate data and varying subsets of PII to find relationships across the data by refining the system to accurately match typos, transpositions, and missing information.

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