Welcome
Welcome to the AI Foundation Data Products Playbook#
In the pharmaceutical world, our success hinges on precision, quality, and the relentless pursuit of discovery. Just as we meticulously manage the lifecycle of our therapies, we aim to apply that same rigor, discipline, and product-centric mindset to our most critical asset: our data.
This playbook is our strategic guide for transforming raw data into high-value, trusted, and reusable Data Products. It is the path for every team member involved in conceiving, building, and maintaining the data assets that will power our next generation of analytics, AI, and operational excellence.
What is a Data Product?
A Data Product at Novo Nordisk represents a curated, governed dataset that combines raw data with comprehensive metadata, quality indicators, contracts, and documentation. The primary objective is making data discoverable, understandable, and trustworthy for specific business or analytical purposes while maintaining clear ownership and accountability throughout its lifecycle.
AI Foundation is in the process of updating the Data Product Definition by EOY 2025. Read the current Data Product definition as outlined in the image below 
Data Product Lifecycle#
Creating Data Products follows the Path to Production, from Ideation to Deploy and Operate. This playbook is split into sections according to the product lifecycle.
Core Playbook Sections#
💡 Ideation#
This section summarizes best practices, methods and tools to transform a business use case statement into business requirements with the goal of building a data solution.
👥 Target Audience: Product Owners, Line of Business Stakeholders, Solution Delivery Managers
🔍 Feasibility#
Outlines best practices, methods and steps for conducting technical feasibility assessments and determining if an existing data product matches the business use case or if a new data product is required.
👥 Target Audience: Solution Delivery Managers, Product Owners, Solution Architects assisted by Data Engineers and relevant stakeholders and SMEs
🛠️ Design#
Establishes the design principles, data architecture patterns, and technical specifications required to create robust Data Products. Covers data modeling approaches, API specifications, security frameworks, and scalability considerations to ensure solutions meet business requirements and technical standards.
👥 Target Audience: Delivery Teams, Data Engineers, Solution Architects
🛠️ Build & Deploy#
Outlines the engineering tools, processes and techniques required to create Data Products while working through the Build and Deploy phases of the delivery model. References engineering and architecture materials including code snippets, design patterns, and data governance.
👥 Target Audience: Delivery Teams, Data Engineers, Solution Architects
Other Relevant Guides
⚙️ Technical Resources#
Various guides for setting up environments for Data Engineers working on Databricks (NN DataCore).
🎯 Master Data Management#
Comprehensive guide to building MDM solutions in Novo Nordisk
🌟 Learning Objectives#
In today's data-driven landscape, successful data initiatives require structured methodology and clear role definition. This playbook provides:
- ✅ Role-specific guidance tailored to your responsibilities
- ✅ Proven frameworks tested in production environments
- ✅ End-to-end coverage from ideation to deployment
- ✅ Technical excellence with engineering best practices
We're Just Getting Started#
This Playbook is still in its early days. Your feedback is very much needed. Please reach out to ZBAN, TFSN, or use the Feedback function below, as well as submit your thoughts through our survey. Thank you in advance!