Enterprise Data Mesh Implementation for Optimized Business Process Outsourcing

As a leading enterprise in the Business Process Outsourcing industry, we recognize the growing need for real-time data insights to enhance decision-making and operational efficiency. We are embarking on a project to implement a data mesh architecture that will decentralize our data management and provide scalable, secure, and real-time analytics. The project involves deploying advanced tools like Apache Kafka for event streaming, Spark for big data processing, and Snowflake or BigQuery for data warehousing. We aim to develop a decentralized data architecture that supports domain-focused data product ownership. Additionally, integrating MLOps and data observability practices will ensure continuous delivery and monitoring of machine learning models, enhancing predictive analytics capabilities. We expect the project to run for 16-24 weeks, with a budget of $50,000 to $150,000, reflecting the importance of this transformation. This initiative will address the current inefficiencies in our data management processes, allowing us to respond promptly to market demands and reduce time-to-insight.