Cognizant® Data Unification Framework

A Comprehensive Solution for Data Mastering and Unification** In today's data-driven world, organizations face the challenge of unifying data from multiple sources to gain a comprehensive and actionable view of their customers. The Cognizant® Data Unification Framework is a custom-built, domain-agnostic Master Data Management (MDM) platform designed to address this challenge. Leveraging loosely coupled open-source components and cloud-enabled capabilities; this framework offers a robust solution for data unification across various industries. The Cognizant® Data Unification Framework comprises a set of microservices that handle all processes related to data mastering and unification. These microservices include Data Ingestion Service, CDC Service, Reference Data Service, Data Quality Service, Match Service, Merge Service, and Data Publish Service. Each service plays a crucial role in ensuring the accuracy, consistency, and reliability of the unified data. The Data Ingestion Service supports both batch and real-time data ingestion, utilizing Spring Batch and Spring Integration-based microservices that run on AWS Lambda and EC2 instances. AWS Glue-based data pipelines feed data from source systems to AWS S3-based raw data zones, and the ingested data is further processed using AWS Kinesis topics. This service ensures seamless data mapping, ingestion, and basic data transformation. The Data Quality Service is a Kinesis event-driven service deployed on AWS Lambda/EC2, performing data quality validations on ingested data. This service ensures that the data meets the required quality standards before further processing.** The Reference Data Service is an integral part of CDUF, also utilizing event streaming contributing to improved data accuracy and operational efficiency. The CDC Service is another event-driven service that performs change data validation on cleansed and standardized data, ensuring that any changes in the data are accurately captured and validated.** TheMatch Service is responsible for performing match operations on change data using configurable rulesets. This service ensures that duplicate records are identified and matched accurately. The Merge Service then performs merge operations on the identified merge candidates, consolidating duplicate records into a single, unified record. Finally, the Publish Service publishes the change records for client consumption, ensuring that the unified data is readily available for downstream applications. The Data Stewardship UI within the CDUF is a specialized interface designed for data remediation tasks. It is primarily used for resolving suspect cases, performing forced merges, and unmerging data. This interface is not considered an operational UI but plays a crucial role in ensuring data accuracy and integrity by allowing data stewards to manage and correct data discrepancies efficiently. The UI is part of a broader framework that includes batch and real-time data ingestion, data processing, and storage, all aimed at enhancing customer service and optimizing marketing efforts. The framework's architecture is designed to be flexible and scalable, running on top of AWS RDS managed Postgres instances for master data and AWS Redshift for data analytics. The framework also includes an Angular-based web app hosted on AWS Amplify, providing a user-friendly interface for data stewardship and basic maker/checker workflow capabilities. AWS CloudTrail and AWS CloudWatch are used for logging and monitoring the platform's health, while AWS IAM and AWS Cognito handle user and security management across the platform.Key features include: • Open-source architecture independent of any Big Data and cloud platform, providing flexibility and scalability. • Microservice-based architecture for efficient data acquisition, transformation, matching, survivorship, data delivery, and platform services. • Configurable probabilistic, deterministic, and machine learning-based matching engines, offering "Match-as-A-Service" capabilities. • Out-of-the-box custom user interface for data stewardship and basic maker/checker workflow capabilities. • Support for both NoSQL data stores and traditional RDBMS databases, making it suitable for small enterprises and large-scale systems alike. This enables organizations to achieve a unified view of their data, facilitating dynamic segmentation, journey mapping, data governance, and activation for marketing campaigns, analytics, and fraud detection. By harnessing the power of their data, organizations can improve decision-making, enhance customer experiences, and optimize operations.This is a powerful, flexible, and scalable solution for organizations looking to achieve a unified view of their data.Its open-source architecture, cloud-enabled capabilities, and microservice-based design make it an ideal choice for businesses of all sizes.