The Data Product Playbook: Designing and Delivering Data Products that Power Decisions, Analytics, and AI, by Willem Koenders
Turn scattered datasets into trusted, reusable data products that power AI, analytics, and measurable business results.
1.1. What is a Data Product?
1.2. Key Properties of Data Products
1.3. Types and Examples of Data Products
2.1. Data Product Lifecycle
2.1.1. Planning
2.1.2. Analysis
2.1.3. Design
2.1.4. Build
2.1.5. Deployment
2.1.6. Usage
2.1.7. Maintenance
2.1.8. Demise
2.2. Value Quantification and Business Case
2.2.1. Data Product Description
2.2.2. Benefits
2.2.3. Costs
2.2.4. Financial Summary and ROI
2.3. Data Product Blueprints and Architectural Guidelines
2.3.1. Reference Data Architecture
2.3.2. Architecture Guidelines
2.3.3. Regulatory, Privacy, and Compliance Guidelines
2.3.4. Certification of Data Products
3.1. Data Product Teams, Roles, and Responsibilities
3.1.1. Core Roles
3.1.2. Supporting Roles
3.1.3. Periphery Roles
3.2. Building a Portfolio of Data Products
3.2.1. Step 1: Use Cases and Impact
3.2.2. Step 2: Required Data
3.2.3. Step 3: Data Sources
3.2.4. Step 4: Use Cases versus Sources
3.2.5. Step 5: Data Product Evaluation
3.2.6. Step 6: Data Product Prioritization
3.2.7. Step 7: Portfolio of Data Products
3.2.8. Anecdote from the Marketplace
3.3. Maturity Models
3.3.1. Maturity Framework for Individual Data Products
3.3.2. Maturity Framework for the Organization
3.4. Organizational Change Journey
3.4.1. John Kotter and the 8-Step Change Model
3.4.2. Step 1: Create a Sense of Urgency
3.4.3. Step 2: Build a Guiding Coalition
3.4.4. Step 3: Form a Strategic Vision
3.4.5. Step 4: Enlist a Volunteer Army
3.4.6. Step 5: Enable Action by Removing Barriers
3.4.7. Step 6: Generate Short-Term Wins
3.4.8. Step 7: Sustain Acceleration
3.4.9. Step 8: Institute Change
See why so many expensive data initiatives stall after go-live, and then what works when you treat data like a real product with real users. With the surge of generative and agentic AI, know that the models aren’t the bottleneck; it’s the data. The winners are the organizations that can deliver data that’s well designed, trusted, easy to find, and consistent across the business.
You’ll get a clear, practical definition of a data product as a “well-defined, reusable, governed, and user-oriented data asset,” plus identify the core properties that separate a true data product from a database table, one-off extract, or dashboard. Be able to explain the keywords teams actually wrestle with, such as data governance, metadata management, data catalog, data quality, interoperability, stewardship, and discoverability.
Next, thinking operationally, see how to apply an end-to-end data product lifecycle (planning through demise), including reusable templates to move from idea to execution, covering project charter, business requirements, design blueprints, and ROI.
Finally, you’ll learn how to scale beyond one heroic team: architectural blueprints (including a reference architecture and an AWS mapping), certification criteria (“compliance by design”, catalog and metadata, incident response, and adoption and impact), and the people side (roles like data product owner and governance specialists), and how to drive adoption with a structured change approach (including Kotter’s 8-step model).
If you’re building a data product strategy, modern data platform, data mesh-style operating model, or a portfolio roadmap that leadership will gladly fund, this book gives you the vocabulary, the structure, and the approach to get from assets to impact.
Willem Koenders is a data strategy leader with over a decade of experience helping organizations turn data into measurable business value. Over his career, he has worked with more than 100 organizations across sectors, supporting everything from data strategy definition to operating model design and large-scale execution. Willem works at the intersection of business and technology, with a strong focus on making data assets usable, governed, and durable. He is a certified practitioner in AWS, GCP, DAMA-DMBOK2, and Informatica, and a strong advocate of “data governance by design.”
Please complete all fields.
