Why Develop Data Products?
Companies typically pursue data products for three reasons:
Improve Customer Value – Retain customers and strengthen the value proposition, increasing lifetime earnings
Improve Company Valuation – Develop proprietary logic and leverage unique data to create differentiation
New Business Development – Enable expansion into new markets and deliver value to new customers
Below, we outline how data product R&D drives value creation and what design considerations make prototyping more sustainable.
How Data Product R&D Increases Customer Value
R&D encourages customer-focused thinking, pushing companies to examine gaps in their products and services. Customers always have unmet needs or complementary demands. Human-centered design shifts the question from “What should we optimize?” to “How might we help our customers do X?”
Reducing customer effort creates positive value, while eliminating cost-driving activities removes negative value. A major source of negative value is the effort required for customers to understand and gain insights from their own history. People want clear narratives and visuals to make better decisions.
R&D outputs—databases, dashboards, visualizations, and models—organize raw data into usable insights. Improving automation and analytical skill around these activities speeds internal decision-making and enhances services. Some visual assets can also be repurposed for customer-facing applications, giving customers both insight and a sense of continuity with the company.
Improving Company Valuation By Developing Proprietary Data Assets
Companies often believe they “don’t have enough data,” but this mindset blocks progress. Instead, value emerges from transforming existing data into proprietary assets.
The process involves labeling, aggregating, and visualizing data in ways that support business-friendly metrics. By encoding unique business logic into in-house algorithms, companies can process raw data into structured insights and new applications.
Processing Data Through the Data Stack Creates Value
This resourceful approach resembles “alchemy”—creating value from overlooked materials. Simply processing and applying the data already at hand can make a company more valuable.
Architecting Data Products to Sustain New Businesses
While prototyping focuses on validating value, data R&D should also explore lean, reusable options that lower the cost-to-benefit ratio of future initiatives. Ideally, data products should help create new business lines with stronger economics than the initial venture.
Sustainable development requires thoughtful design choices:
Modularity – Build code, databases, and APIs in modular ways to reduce incremental costs for new features or services.
On-Demand Access – Ensure core features are accessible for common needs, while custom features can be packaged as premium offerings.
Learning Loops – Use lessons from serving niche or premium needs to guide the next wave of feature development.
Seamless Releases – Enable easy rollouts of new features that address diverse or specialized customer demands.
Conclusion
Data product R&D is not just about technology—it’s about systematically creating customer value, strengthening company valuation, and opening new business opportunities. By combining human-centered design with modular technical architecture, companies can ensure their data products provide lasting and scalable impact.
