Designing Data Products

<p><strong style=color: rgba(33 33 33 1)>Drive value and trust from your data with purpose-built Data Products that deliver business results.</strong></p><p></p><p><em style=color: rgba(33 33 33 1)>Designing Data Products</em><span style=color: rgba(33 33 33 1)> 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 </span><em style=color: rgba(33 33 33 1)>data product</em><span style=color: rgba(33 33 33 1)> is how it works and why it's the missing link between analytics AI and operational excellence.</span></p><p></p><p><span style=color: rgba(33 33 33 1)>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.</span></p><p></p><p><span style=color: rgba(33 33 33 1)>Unlike dense technical manuals </span><em style=color: rgba(33 33 33 1)>Designing Data Products</em><span style=color: rgba(33 33 33 1)> 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.</span></p><p></p><p><span style=color: rgba(33 33 33 1)>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.</span></p>