Data products: Closing the gap between technical excellence and business value

2026-04-16 · Source: Thoughtworks Insights · Field: Technology & Digital — Data Science & Analytics, Artificial Intelligence & Machine Learning, Project & Product Management · Depth: Intermediate, medium

Summary

Amy Raygada, a Thoughtworker, is authoring a book series on Data Products to address the persistent gap between technical data platform execution and actual business value. Despite significant investments, organizations often see adoption rates as low as eight percent, with business users still relying on basic tools. Raygada highlights that data products are uniquely challenging because users struggle to articulate needs, data quality issues are often invisible, and accountability spans multiple organizational boundaries. Her approach emphasizes a product thinking framework, shifting incentives, and a "four-layer governance stack" that centralizes infrastructure automation and computational policies while allowing federated decision-making for standards. The series' first volume, published late 2025, focuses on strategic foundations, with subsequent volumes covering technical execution and long-term sustainability. Raygada stresses that timeless principles include prioritizing user problems, measuring adoption, and enabling governance.

Key takeaway

For AI Product Managers evaluating data platform investments or launching new AI initiatives, recognize that technical excellence alone does not guarantee business value. Your focus must shift from pipeline improvements to understanding specific user decisions and measuring adoption. Prioritize user interviews to define needs, establish a data product manager role, and tie success metrics directly to business impact and ROI. This approach ensures your data investments genuinely support AI initiatives and avoid becoming costly, underutilized assets.

Key insights

Data products bridge the gap between technical data excellence and business value by focusing on user needs and adoption.

Principles

• Start with user problems, not technical capabilities.
• Measure success through adoption and business impact.
• Build governance that enables rather than constrains.

Method

Implement a "four-layer governance stack" centralizing infrastructure automation and computational policies, while federating standards and conflict resolution. Embed governance checks into development tools.

In practice

• Conduct user interviews before building data products.
• Tie success metrics to adoption and ROI.

Topics

• Data Products
• Data Governance
• Data Strategy
• Product Management
• User Adoption
• AI Initiatives

Best for: Product Manager, Director of AI/ML, AI Product Manager, Consultant

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Editorial summary, takeaway, and curation by AIssential. Original article published by Thoughtworks Insights.