Streamlining Entity Resolution on AWS

Most entity resolution projects hit the same bottleneck: data mapping. Translating messy, inconsistent source schemas into a consistent entity format can take weeks or months before teams begin to see trustworthy entity resolution results. That drag shows up across use cases, including customer 360 and MDM initiatives, watchlist screening, onboarding flows, and investigations that rely on connecting records to real-world people and organizations.

In a new AWS Builder Center article, AWS Partner Solutions Architect Tina Myers explains how Amazon Q Developer and SENZING® entity resolution on AWS can streamline this bottleneck by turning mapping into a structured, repeatable workflow.

Why Data Mapping Can Create Friction

As the number of data sources grows, mapping work expands beyond field-to-field translation into a broader exercise that includes inventorying source structures, interpreting meaning, designing transformation rules, generating mapper logic, and validating outputs with enough rigor to trust what gets loaded. This is why implementations that look straightforward on paper can become slow in practice.

Data mapping is where technical integration meets business semantics, and it is also where small mistakes can cascade into match errors, operational rework, and downstream confusion. As Tina Myers puts it in the AWS post, “As schemas grow more complex, the increasing number of entity relationships and validation steps make the mapping process significantly more involved.”

Advantages of Building Entity Resolution with Senzing On AWS

AWS provides the cloud infrastructure that many organizations use to build and operate data-intensive systems. Amazon Q Developer is an AI-powered assistant for developers that works within the development workflow, helping interpret data structures and generate implementation artifacts.

In the AWS article, Myers explains how teams can use Amazon Q Developer with the Senzing Mapping Assistant to reduce the manual effort of mapping new source data into Senzing generic entity specifications (JSON) and producing validated mapper code ready to load into Senzing entity resolution on AWS.

A Structured, AI-Assisted Mapping Workflow

The article frames the Mapping Assistant as a repeatable five-stage process: INIT, INVENTORY, PLANNING, MAPPING, and OUTPUTS. Amazon Q Developer helps analyze source structures, proposes mapping recommendations aligned with Senzing specifications, and generates production-ready mapper code and supporting documentation, while developers retain control over decisions and approvals because mapping choices encode business meaning along with mapping logic.

Learn Entity Resolution Mapping on AWS in a Hands-On Workshop

The article also pointed readers to a self-paced hands-on workshop in January that guided users through the end-to-end workflow, from mapping real datasets to exploring entity resolution results. The workshop included two exercises: customer data mapping using CSV files, and watchlist data mapping using more complex JSON that includes international names and relationships. It was designed for software developers and data engineers working on integration and ETL workflows, and it runs in a cloud-based IDE with software and sample code provided. Get started with the Senzing Amazon Q Quickstart.

Independent Research on Business Impact

The post also cites a Total Economic Impact™ (TEI) study by Forrester Consulting, commissioned by Senzing, that reports outcomes for a composite organization, including a 226% ROI, a 95% reduction in time to add a new data source, payback in under six months, and a 25% reduction in incidents caused by mismatches.

Read the full article on the AWS Builder Center for a complete walkthrough of the mapping workflow and the AWS-native implementation details. If you want to try it hands-on, sign up for a future workshop (date TBD).