Entity resolution is the process of identifying and merging records that refer to the same real-world entity across multiple data sources. It’s used to create a single, accurate view of data.

Overview

A public sector organisation sought a robust solution to streamline identity resolution and matching across its vast datasets. They faced challenges in reconciling fragmented data sources, ensuring accurate matching, and maintaining data integrity while meeting operational efficiency demands. dataAdroit was engaged to design and implement an Entity Resolution Framework that leveraged advanced technologies, delivered probabilistic matching results with confidence scores, and ensured scalability for future needs.

Challenges

The organisation encountered the following challenges:

  1. Fragmented Data Sources: Data was scattered across multiple systems with varying formats and standards.
  2. Accuracy in Identity Matching: Traditional methods failed to achieve the desired accuracy in matching records.
  3. Scalability: The existing infrastructure struggled to process large-scale data efficiently.
  4. Integration Needs: There was a lack of a unified approach for internal and external stakeholders to consume identity resolution services.
  5. Cloud Deployment: The organization required a cloud-native solution to leverage flexibility, security, and scalability.

Solution Delivered

dataAdroit designed and implemented a scalable Entity Resolution Framework to address the organization’s challenges. The solution incorporated the following key features:

1. Probabilistic Matching with Confidence Scoring

The framework utilised advanced algorithms to deliver probabilistic matching results. Each match was accompanied by a confidence score, providing transparency and aiding decision-making processes.

2. Scalable API Integration

A RESTful API was developed, allowing seamless integration with the organization’s internal systems and external consumers. This enabled users to access the identity resolution service programmatically, enhancing operational efficiency.

3. Technology Stack

The framework was built using cutting-edge technologies, ensuring high performance and scalability:

  • Apache Spark: For distributed data processing and large-scale data transformation.
  • Apache NiFi: For seamless data ingestion, integration, and real-time data flow management.
  • Scala: As the programming language for efficient data processing and feature implementation.
  • Containerisation: Using Docker to ensure consistency and portability across environments.
  • Microservices Architecture: To enable modularity and independent scaling of individual components.
  • Graph Database (Neo4j): Used as the persistence layer, enabling efficient storage and retrieval of relationships between entities. Neo4j also laid the foundation for the organisation to explore the creation of knowledge graphs, further enhancing their data analysis capabilities.

4. Cloud Deployment on AWS

The solution was deployed in the AWS cloud environment, leveraging its scalable infrastructure. Key AWS services utilised included:

  • Amazon S3: For cost-effective data storage.
  • AWS Lambda: For serverless API execution and scaling.
  • Amazon RDS: For managing metadata and transactional data.

5. Workflow Orchestration with Airflow

Apache Airflow was used to orchestrate complex workflows and ensure smooth execution of the data pipelines and resolution tasks. It provided visibility into the workflows and allowed for easy monitoring and error handling.

6. Data Standardisation and Schema Evolution

The solution included robust mechanisms for:

  • Data Standardisation: Ensuring uniformity in data formats and enhancing compatibility across systems.
  • Schema Evolution: Managing changes in data schemas without disrupting the overall functionality of the framework.

Results Achieved

The implementation of the Entity Resolution Framework delivered significant benefits to the organisation:

  • Enhanced Accuracy: The probabilistic matching algorithms improved identity resolution accuracy by 35% compared to the previous system.
  • Scalability: The microservices architecture and containerisation ensured the system could handle increasing data volumes with ease.
  • Operational Efficiency: The API integration reduced the manual effort required for identity resolution by 50%, enabling faster decision-making.
  • Transparency: Confidence scores provided clear insights into the matching process, fostering trust among stakeholders.
  • Advanced Data Insights: The integration of Neo4j as the persistence layer enabled the organisation to visualise relationships between entities and set the stage for the development of knowledge graphs.
  • Cloud-Native Advantage: The AWS deployment improved system reliability, security, and cost-efficiency.

Conclusion

dataAdroit’s Entity Resolution Framework empowered the public sector organisation to tackle its data challenges effectively. By leveraging a cloud-native, scalable, and innovative approach, we enabled them to achieve their goals of accurate identity resolution and efficient data integration. The use of Neo4j not only optimised the persistence layer but also provided opportunities to explore advanced analytics through knowledge graphs. This project highlights our commitment to delivering Reliable, Rapid Results tailored to our clients’ unique needs.

Are you facing challenges with data integration or identity resolution? Connect with dataAdroit today to discover how our expertise can transform your data operations.