A traditional digital product typically has a somewhat linear customer journey.

Imagine using a streaming service like Netflix. When users open the app, they are guided to browse content, make a selection, and watch a show. This journey is relatively linear users are directed towards choosing and consuming content in a fairly predictable sequence. The path is clear and has a defined beginning, middle, and end.
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Digital data products, on the other hand, often involve non-linear journeys. Users interact with the product based on data analysis needs, leading them down varied paths. For instance, a dashboard tool for business intelligence allows users to explore, filter, and visualize data.

Consider Google Analytics. Users may log in to analyze traffic, explore different segments, set up new reports, or even export data for further use. There is no single defined path, they can jump between different sections based on their specific needs at that moment, making the journey highly non-linear.
The journey here isn’t predictable; users take multiple entry and exit points depending on what they're trying to achieve. This requires thoughtful design to handle the unexpected, ensuring every touchpoint still makes sense, even when users are at different stages.

Designing for a traditional product generally means focusing on a targeted user persona. We have a clear understanding of what skills or pain points they have, allowing us to tailor the experience accordingly.
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With digital data products, user personas vary greatly. Users may include data-savvy analysts who need in-depth control over complex datasets, or business stakeholders who want a simple snapshot of key metrics.

Think about a tool like Tableau. Analysts may use it to build complex, interactive dashboards, while executives may only use it to quickly review key performance indicators. The challenge lies in ensuring that both user groups feel comfortable—providing powerful capabilities without overwhelming less technical users. This range of personas demands a flexible design that can accommodate a deep understanding for experts while also being accessible for non-experts.
Designing with a range of skill levels in mind requires balancing powerful features with simplicity—something that, as designers, we need to carefully orchestrate to avoid alienating any group.

Another critical distinction lies in the analysis of outcomes. Traditional digital products often focus on conversion rates or engagement metrics, success is measured by clear, action-oriented KPIs.
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Digital data products, however, are about deriving insights, making decisions, and influencing actions based on data. It’s not enough to measure usage; we need to understand how the data itself is being used, whether it’s generating insights, and how those insights are driving decisions. In many cases, the feedback loop also involves improving the data: collecting better inputs, refining models, or improving visualizations to aid understanding.

Consider a data product like Looker. Its success isn’t just about how often users log in, but about whether users are able to derive meaningful insights that lead to business actions. The focus is on how well users can utilize the data presented, make informed decisions, and ultimately drive business outcomes.
As designers, we must embed analytical capabilities into the user journey, enabling ongoing iteration and improvement.

While the end goal of all digital products is to create value for users, designing digital data products requires a more flexible and analytical approach.
Non-linear pathways, diverse user skills, and the emphasis on insight rather than conversion all mean that our design strategies need to evolve. It's not about steering the user toward a specific action—it’s about empowering them to find value in their own journey, however complex it might be.
By understanding these differences, we can create more intuitive, effective digital data products that cater to diverse user needs and generate actionable insights.
This approach lets us design not just for interaction, but for deeper understanding and informed decision-making.

What do you think? How do you navigate these challenges when designing data products?