AWS has unveiled AWS Entity Resolution, an analytics service fueled by machine learning (ML) technology. This service facilitates seamless analysis, matching, and linking of interconnected records stored across different applications, channels, and data repositories. AWS Entity Resolution employs adaptable workflows, combining rule-based and ML techniques to effectively connect related consumer, business, and product data. This enables organizations to gain valuable insights and a comprehensive understanding of their data, enhancing their ability to make informed decisions and improve overall data management efficiency.
AWS Entity Resolution offers business analysts and developers the ability to enhance data accuracy swiftly through preconfigured workflows or customization to align with their organization's specific requirements. By leveraging AWS Entity Resolution, businesses can gain a comprehensive understanding of data relationships, matches, and links, resulting in deeper customer insights, clearer supply chain data for improved operations, targeted marketing campaigns, and informed financial investment decisions.
“With a few clicks, AWS Entity Resolution makes it easy for organizations to match records and link workflows that are flexible, scalable, and easily connect to existing applications, enabling faster and more comprehensive views of their data to unlock its value and improve business outcomes. Together with the broad AWS portfolio of analytics solutions, AWS Entity Resolution makes matching and linking data from multiple data lakes and data stores easier, helping customers keep their data where it lives, and contributing to a zero-ETL future,” said Dilip Kumar, Vice President of AWS Applications.
Additionally, AWS has announced upcoming entity resolution partner integrations with LiveRamp and TransUnion, along with compatibility with the Unified ID 2.0 open-source framework. These integrations will help customers translate or enrich their data records more efficiently while safeguarding sensitive information and minimizing data movement.
Streamlining the matching and linking of scattered records
The problem of data silos is becoming increasingly prevalent, with customer, business, and product information scattered across numerous applications, channels, and data repositories in various organizations. For instance, companies may want to connect recent customer interactions with a unique identifier to understand shopping patterns across different platforms.
The current methods to link these records involve complex and time-consuming data pipelines, either developed in-house or with the help of external partners. Reconciling records with disparate or incomplete information poses significant challenges. Incorporating machine learning for more accurate matching adds even more complexity, requiring specialized expertise and extensive data preparation, training, testing, and deployment of ML models.
Alternatively, some companies opt for external solutions, relying on vendors to match their records. However, this approach comes with its own set of difficulties. It entails building and maintaining custom data pipelines for each partner, involving data transfer to external platforms, which can increase the risk of data leakage.
AWS Entity Resolution provides companies with a powerful solution to simplify the matching and linking of related records scattered across various applications, channels, and data repositories. Using customizable workflows, companies can achieve this in a matter of minutes, even when dealing with records that have missing or incomplete information.
In other news, Amazon Web Services (AWS) and Twilio have extended their longstanding partnership to bring powerful artificial intelligence (AI) capabilities to Twilio customers.
