Customer Data & Experience · Data & Analytics
Should you build or buy Entity Resolution?
Entity Resolution software identifies and links records across datasets that refer to the same real-world entity, such as a customer, organization, or product, even when those records contain inconsistencies, misspellings, or missing fields. It applies probabilistic and deterministic matching algorithms to deduplicate records and produce a clean golden record that downstream analytics, fraud detection, and personalization systems can rely on.
The build-vs-buy decision for Entity Resolution turns on how proprietary your matching logic and data model are to your specific domain and how far production-grade open-source tooling has come in making that logic buildable without vendor expertise; the specifics of your record types and team's data science capability decide it.
Build it, buy it, or bridge?
When building makes sense
Building entity resolution makes the most sense when your matching logic is genuinely specific to your domain and you have the data science capacity to own it. The open-source tooling is production-grade: Splink is used by the UK Office for National Statistics for population-scale matching, and Zingg provides another well-documented OSS path. Self-built Splink solutions cover an estimated 50 to 70 percent of Senzing's functionality for teams that define their own record types and blocking strategies. The strategic case for building is that a high-quality customer golden record is increasingly the substrate for AI personalization, fraud detection, and 360-degree analytics. The matching logic that defines your customer identity is proprietary, and owning it means owning a data asset competitors can't replicate. AI tooling has recently lowered the expertise barrier further: LLMs are becoming useful for generating blocking functions and comparison logic from data samples, making a hybrid approach, Splink as the engine with AI-assisted configuration, more accessible than it was two years ago.
When buying makes sense
Buying entity resolution earns its keep when your entity types match the pre-built industry data models that vendors like Senzing and Reltio have invested years in building. Healthcare organizations matching patient records and financial services companies reconciling account holders across products get real value from vendor schemas that encode domain-specific field weights and edge cases. Reltio's multi-domain MDM capabilities extend well beyond entity resolution into governance workflows, lineage tracking, and data stewardship that a custom Splink solution wouldn't cover. AWS Entity Resolution's pay-per-use model at $0.01 per record makes the vendor option financially tractable for organizations that need clean records at smaller volumes without committing to a full platform license. Buying also makes sense when you lack a data scientist who can tune probabilistic match thresholds and you need a reliable golden record on a shorter timeline than a custom build allows.
The desk read
A high-quality customer golden record is increasingly the prerequisite for AI personalization, fraud detection, and customer 360 analytics. The matching rules themselves, which fields to block on, what thresholds define a match in your domain, how to handle edge cases specific to your data, are genuinely proprietary. Splink is a production-grade open-source probabilistic matching library, used by the UK Office for National Statistics for population-scale matching, and Zingg provides another OSS path. The build case gets serious when your record types are well-defined, when your data science team has matching experience, and when you need to own the logic that defines your customer identity.
Vendors like Senzing and Reltio carry extensive pre-built industry data models for healthcare and financial services that represent real value for organizations where those schemas apply. Reltio's multidomain MDM capabilities go well beyond entity resolution into governance workflows that a custom Splink solution wouldn't cover. AWS Entity Resolution's pay-per-use pricing makes the vendor option financially tractable at smaller volumes. The AI shift is that LLMs are becoming useful for generating blocking functions and comparison logic from data samples, which lowers the expertise barrier on the build path and makes a hybrid approach, Splink for the engine, AI tooling for accelerating the configuration, more accessible than it was two years ago.
Frequently asked
What is Entity Resolution?
Entity Resolution software identifies and links records across datasets that refer to the same real-world entity, such as a customer, organization, or product, even when those records contain inconsistencies, misspellings, or missing fields. It applies probabilistic and deterministic matching algorithms to deduplicate records and produce a clean golden record that downstream analytics and personalization systems can rely on.
When does building Entity Resolution make sense?
Building makes sense when your matching logic is specific to your domain and you have data science capability to own it. Production-grade open-source tools like Splink and Zingg cover the full matching pipeline, and LLMs are now lowering the expertise barrier for configuring blocking functions and comparison logic.
When does buying Entity Resolution make sense?
Buying makes sense when your entity types match vendor pre-built industry models, particularly in healthcare and financial services, or when you need governance workflows and data stewardship beyond what a custom matching engine provides. AWS Entity Resolution's pay-per-use pricing is also tractable for lower-volume needs.
What are the main Entity Resolution vendors?
Representative vendors include Senzing, AWS Entity Resolution, Profisee, Quantexa. B4 Pro scores the full set.
What is the difference between entity resolution and customer identity resolution?
Entity Resolution is the broader category covering matching of any record type (customers, products, organizations, locations). Customer Identity Resolution is a specific application focused on matching customer records across marketing channels and touchpoints. Entity resolution platforms like Senzing serve regulated industries matching patient records and financial accounts; customer identity platforms like LiveRamp and Amperity are purpose-built for marketing and activation use cases.