Level: Beginner to Intermediate
Prerequisite: None
Pradeep Karpur
Partner
Technology Consulting - Data and Analytics (EY)
As organizations continue to invest heavily in data and AI, many struggle to translate these investments into sustained business value. Traditional, project-centric data approaches often result in siloed solutions, duplicated effort, limited reuse, and slow time to insight. At the same time, growing demands for self-service analytics, AI enablement, regulatory compliance, and cross-domain insights are placing unprecedented pressure on data platforms and governance models. In this course, you will learn how to address these challenges by shifting toward an operating model that treats data as a product and enables scalable, governed, and democratized data and AI solution development.
Data fabric is a modern architectural pattern that unifies data access, integration, metadata, governance, and automation across heterogeneous environments and positions data products as the primary building blocks that deliver business value on top of that fabric. Students will learn how data fabric leverages metadata intelligence, automation, and self-service capabilities to simplify data access while preserving governance, and how data products apply product management principles to data—ensuring accountability, quality, security, and continuous value delivery.
Through practical frameworks, real-world examples, and hands-on concepts, the instructor will walk through how to design, build, govern, and scale data products within a data fabric architecture. The course will also explore how these capabilities come together to enable a data product marketplace, empowering producers, consumers, and governance teams to collaborate effectively and accelerate the democratization of analytics and AI across the enterprise.
You Will Learn
- How data fabric differs from—and complements—other architectural patterns such as data lakes, lakehouses, and data mesh
- The role of metadata intelligence, automation, and active metadata in enabling scalable data integration and governance
- What a data product is, how it differs from traditional data assets, and why a product mindset is critical for data and AI success
- The core components of a data product, including data sets, metadata, data quality controls, security, and consumption interfaces
- How to design and operationalize a data product lifecycle, from discovery and design to delivery and continuous improvement
- How to establish an operating model that supports federated, business-led data product development
- How to enable and govern a data product marketplace to support self-service analytics, AI, and cross-domain reuse
- Practical considerations, challenges, and best practices for implementing data fabric and data products at scale
Geared To
- Data and analytics leaders responsible for modernizing data platforms and operating models
- Data architects, data engineers, and analytics engineers designing scalable data and AI ecosystems
- Data product owners, analytics managers, and domain leads accountable for delivering business value from data
- Governance, privacy, and risk professionals enabling trusted, compliant data democratization
- Business and IT leaders seeking to accelerate self-service analytics and AI adoption across the organization