Over the last five years, we've developed a practical framework for building good data products, and this post walks through it.
In a recent talk on data literacy, we used the idea of a hero's journey to make the concept easier to grasp. One slide mattered most: it showed where communication tends to break down between specialists and stakeholders.
Without going too deep into the topic, the issue is the abstract mental models that shift at each step, from data collection and processing through to actual use in, say, a dashboard. (Evelyn Münster is a great resource for going deeper here, and she inspired this visual.)
After the talk, someone asked: "Do people actually understand what we mean by data product?" More often than not, the answer is no, especially when data literacy and organizational maturity are still new ground for them.
We see data products not as isolated deliverables like dashboards or datasets, but as tangible outcomes that use data to solve the real problems of their users. When done right, they create measurable impact, whether by improving a process, enabling better decisions, or supporting an entirely new service.
Too many data initiatives get stuck in pilot phases or never deliver results. We've seen it time and again: well-meant efforts that never made it into production, burning resources and eroding trust in data and tech. That's why we focus not just on building things right, but on building the right things.
A data product is more than a dashboard or a dataset. It's a solution designed to address a specific problem using data. Whether it's a recommendation engine, a predictive model, or an interactive report, the goal is always the same: to turn raw data into something useful, actionable, and meaningful for the people who use it.
A good data product is also more than a technical artifact. It's FAIR(ER):
Together, these principles keep data products not just functional, but sustainable and valuable over time.
We've heard the reasons over and over. You only have to open LinkedIn to see examples of them. And yet too many data initiatives still fail to deliver the outcomes people expect. The common pitfalls are the following:
At FELD M, we tackle these challenges by working with our clients to hunt down the problems that actually keep stakeholders up at night, so our solutions are both relevant and useful.
In his seminar on designing human-centered data products, Brian O'Neill put it well: ask who gets a sleepless night if this problem isn't solved. Those are the problems worth working on.
Beyond those properties, whether a data product is worth building comes down to three qualities that all good products share:
Think of data products as a pyramid. Data products take many forms, but they generally fall into three categories based on their purpose, complexity, and how directly they support decisions or action. We think of them as building blocks that grow in sophistication and in perceived value as you move up the pyramid.
These are the datasets and services that make everything else possible. Foundational products don't deliver insights directly, but they're critical enablers of reliable, scalable, efficient data use across the organization.
Examples: Cleaned and curated datasets; standardized APIs or data pipelines that provide consistent access to key sources; master data services or semantic layers that keep business terms and metrics clearly defined.
Why they matter: Without a strong foundation, advanced use cases become brittle. Foundational products ensure consistency, data quality, and reusability, so teams can build without starting from scratch each time. They cut duplicated effort and build trust in the data landscape.
These products support internal decision-making by turning raw data into understandable, often visual formats. They're typically used by business teams, analysts, or domain experts to monitor performance, spot trends, or investigate issues.
Examples: Interactive dashboards for marketing performance; self-service reporting tools that let teams explore KPIs and drill into specific areas.
Why they matter: Analytical products give teams timely, relevant insight without making them dig through raw data. Done well, they bring clarity, speed up decisions, and support more strategic thinking. But they only work when they're aligned with real business questions and backed by strong foundational data.
Smart data products are where insight turns directly into action — often automatically. They embed data-driven intelligence into business processes, tools, or customer-facing systems.
Examples: personalization engines that tailor content or recommendations in real time; predictive models that forecast customer churn or demand; optimization tools that automatically adjust their output based on changing inputs.
Why they matter: smart products don't just inform decisions — they make or shape them. Because they integrate directly into workflows, platforms, and user experiences, often in real time, they call for close collaboration between technical, business, and legal stakeholders to ensure feasibility, compliance, and effectiveness.
Creating a data product isn't a one-off event — it's a journey:
Managing this lifecycle well is what keeps data products delivering value and adapting as business needs change.
We combine expertise in data engineering, data science, and business intelligence to create data products that are:
We ground our work in design thinking and agile principles:
At FELD M, data products aren't just about technology, they're about creating measurable business value by solving the right problems with the right solutions.
We bring structure, transparency, and a relentless focus on outcomes to every engagement. Whether you're technical or business-focused, we speak your language and respect your experience.
Ready to turn your data into real results? Let's talk.
Further reading
Case studies
- Developing a propensity score model
- Data integration with a modern data stack for seamless analytics
- A unified platform for all social media dashboards
- Machine learning for efficient document classification
- Campaign performance dashboards
- Pricing optimization for an international retailer
- How a web-app operator monetized its platform (Plan.One)
- Rapid prototyping for a Swiss retailer
- Connecting online interaction with offline buying
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