DATA QUALITY
Data matching and entity resolution solutions
Accelerate confident decision-making, improve customer engagement, and comply with complex regulations with an automated, intelligent data matching and entity resolution solution.
Automated data matching and entity resolution
The same customer, supplier, or asset can appear across dozens of systems, with different spellings, formats, and identifiers. That leaves you with conflicting records and less confidence in every decision built on them.
Data matching and entity resolution connect records that refer to the same real-world entity. You get one trusted view of each customer, supplier, asset, or other key entity, so you can make better decisions, uncover hidden fraud, and support compliance requirements such as Know Your Customer.
This goes beyond deduplication. While deduplication removes duplicate records within a dataset, entity resolution connects related records across systems.Precisely helps you match and resolve data across large, complex datasets, both on premises and in the cloud. Configurable matching and AI/ML give business users more control without added dependence on IT.
The use cases that benefit from our data matching and entity resolution solutions include:
Confident business decisions
Customer 360
Anti-money laundering
Fraud dectection
Why entity resolution is the foundation for trusted AI
Unreliable data rarely affects just one system. Errors spread across connected processes, and now AI raises the stakes. A human analyst may spot an unusual record and investigate it. AI, however, will quickly act on inaccurate data and repeat the same error across an automated workflow.
In agentic workflows, those errors can compound. One agent’s output becomes another agent’s input, allowing a single unresolved entity to move through the pipeline before anyone reviews it. An issue in a dashboard may be caught in a manual or human-in-the-loop process. In an automated AI workflow, that same issue can contribute to a poor procurement decision, a missed fraud signal, or another costly outcome.
Entity resolution helps stop these issues at the source. By connecting records and resolving them into trusted entities, you give AI a more reliable foundation for reasoning. You also make its output easier to trace, explain, and defend.
Cleanse and enrich data for better matching
Accurate matching starts with clean, complete data. When records contain gaps, inconsistencies, or outdated information, the resolution process carries those issues forward.
Data quality is the most common data integrity priority, cited by 51% of data and analytics leaders. Clean, reliable records give matching processes the consistency they need to produce accurate results.
Precisely solutions profile your data upfront to surface content and quality issues, apply business rules that keep key fields reliable over time, and enrich records with demographics, firmographics, and location intelligence, keeping entity resolution accurate as data volumes grow.
An accurate 360-degree customer view
Customer engagement is key for a successful business and requires a comprehensive understanding of customers and to protecti their data. Building an accurate 360-degree customer view is complicated and often key customer information is limited, flawed, out-of-date, or in silos. Putting data quality processes in place to standardize, cleanse, and enrich that data is critical to correctly match and resolve customer entity data and generate a trusted single customer view.
The perfect match, for any type of entity
Customers and people are only the beginning. The same challenge affects households, businesses, suppliers, assets, products, parts, and locations. When records are scattered across systems, you need one accurate view of each entity to put your data to work.
Precisely solutions resolve any type of entity with configurable matching. You define what a match for each domain because duplicate products, parts, businesses require different rules. The solution adapts to your requirements instead of applying the same model to every entity type.
Schwan’s experiences a 25% decrease in new customer duplicate data
“Precisely software solutions have provided our organization with efficiency improvement across sales, order fulfillment, and even in the field.”
Connie Gugel, Senior Systems Analyst
Schwan’s Food Company