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Data Quality & Accuracy

Unparalleled data accuracy for impeccable data quality

Data Quality Matters.

Accurate, reliable data is the foundation of every successful decision, strategy and operation in today’s business landscape. Entity resolution plays a crucial role in achieving high data quality by identifying records that refer to the same entity across different sources.

Without proper entity resolution, organizations face duplicated, fragmented and conflicting data, leading to costly errors, compliance risks and missed opportunities. By ensuring that all entity data is accurate, entity resolution enhances data quality, drives better insights and helps maintain the integrity of business processes, supporting informed and effective decision-making.

Data quality & data accuracy with entity resolution

Data Quality Depends on

Data Accuracy

Don’t let small, contrived tests fool you. When tested on real data at scale, the Senzing SDK delivers higher data quality and accuracy than other data matching technologies. Our superior accuracy comes from three unique Senzing capabilities: Entity Centric Learning™, fuzzy matching with domain knowledge, and real time learning with sequence neutrality.

Air Gapped Systems?

No Problem.

Senzing SDK runs on your systems. Your data never flows to Senzing Inc. The Senzing SDK can be run on-prem or in your cloud, quickly and elegantly integrating into your technology stack and security infrastructure.  

 Svg+xml;charset=utf data quality & accuracy - Entity Centric Learning

Senzing uses Entity Centric Learning, an approach the Senzing team has been perfecting for decades. This learning technique was initially developed in our Generation 2 technology for Las Vegas casinos to find players who were obfuscating their identities. It turns out that Entity Centric Learning also does a spectacular job performing entity resolution on messy and incomplete records, discovering matches that traditional record-to-record matching can’t.  Learn More about Entity Centric Learning.

 Svg+xml;charset=utf data quality & accuracy - Feature-Specific Fuzzy Matching

Features like names and addresses are especially challenging to compare without domain-specific knowledge. Senzing entity resolution outperforms the competition and delivers higher accuracy by using feature-specific fuzzy matching. Senzing ships out-of-the-box with over a dozen specialized feature comparators, including many with domain-specific knowledge. For example, Senzing uses machine-learned multi-cultural name matching trained on more than 800 million names and an address parser trained on over one billion global addresses.

 Svg+xml;charset=utf data quality & accuracy - Real Time Learning with Sequence Neutrality

Senzing is an AI purpose-built for entity resolution that learns in real time. As data is being resolved, generic values like overused phone numbers are discovered in real time and then used to improve future decisions and instantly correct past decisions based on new data and discoveries. This unique aspect of our AI allows it to change its mind about the past. We call this sequence neutrality. With Senzing, regardless of the order the data arrives in, the entity resolution results end up the same. Systems without sequence neutrality suffer from accuracy drift, resulting in bad decisions.

If an entity resolution company publishes general or theoretical accuracy numbers, it’s marketing hype. Accuracy can only be assessed using real-world data, ideally your own data, so test your data with Senzing today.

TEST MY DATA

After very close analysis, we found real time entity resolution was delivering us more accuracy simply by being current. We canceled a batch-based technology moments later, saving us another million dollars a year.

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Watch Senzing CEO Jeff Jonas Explain how to Improve Data Quality with Entity Resolution.