This course focuses on building a centralized data intelligence system that collects data from multiple sources, processes it using AI capabilities, and delivers actionable insights through dashboards and reports. Students will apply the Data Product Mindset, ODPS, and MLG frameworks learned in the previous Course to create a real-world data intelligence hub.
In this course, you will design and implement multi-source data integration workflows using n8n, covering ingestion patterns, scheduling, error handling, and maintainable workflow design. You will then apply AI and machine learning techniques to analyze incoming data, detect patterns, and generate reliable insights that stakeholders can act on. You will also build automated distribution flows that deliver insights to the right audiences through reports, dashboards, and notifications, with clear traceability back to source data.
A key focus is operational quality. You will implement data quality monitoring, validation rules, and exception handling to ensure trustworthiness across the pipeline. You will learn how to structure the hub as a scalable data product, with clear input and output ports, documentation, and governance controls aligned to ODPS and Minimum Lovable Governance.
By the end, you will have a working AI-powered data intelligence hub, along with professional documentation that can be reused in real projects and expanded into production-grade architecture.
