Product Description
by Mario Meir-Huber (Author)
Drive value and trust from your data with purpose-built Data Products that deliver business results.
Designing Data Products demystifies one of today's most talked-about concepts in data management and makes it practical. Mario Meir-Huber, a veteran data leader who's led enterprise transformations across industries, shows how to bridge the gap between business strategy and data engineering through a product mindset. Instead of buzzwords and hype, this book offers a clear, human explanation of what a data product is, how it works, and why it's the missing link between analytics, AI, and operational excellence.
You'll discover frameworks such as GAP (Governance, Architecture, People) and DRIVE (Data Retrieval, Integration, Value Extraction) that turn theory into repeatable practice. Through relatable stories and real-world lessons, learn how to create trustworthy, interoperable, and scalable data products that combine data governance, data architecture, and human collaboration to deliver measurable business value. Whether you're an architect, product manager, consultant, or business executive, you'll find a roadmap for creating sustainable, outcome-focused data ecosystems.
Unlike dense technical manuals, Designing Data Products speaks to both the business and technical dimensions of data. It explores the shift from traditional data warehouses and data lakes to a modern decentralized approach, explaining how organizations can decentralize wisely while maintaining consistency, quality, and security. The book emphasizes the cultural and organizational side of data transformation because even the best architecture fails without engaged people.
By the end, readers will understand how to define, design, and scale Data Products that are reliable, discoverable, reusable, and truly aligned with business goals. You'll come away with the mindset and vocabulary to lead conversations about data value creation, AI readiness, and digital transformation, and the confidence to turn raw data into real-world impact.
