Data alone doesn’t drive decisions. User experience (UX) alone doesn’t deliver insights. But together, both can transform information into clarity, trust, and action.
This is the essence of the “UX of Data”: applying user-centered design principles to the full lifecycle of data, from collection and structure through visualization and decision-making. Let’s dig deeper into this concept and why it’s a focus of our presentation at Tech Week 2025.
For our purposes, we will be using the following definitions:
Data are the raw facts, metrics, and observations. In organizations, this often comes from transactions, customer interactions, systems logs, or research studies.
UX is the practice of designing systems, interfaces, and interactions that help people accomplish goals in intuitive, efficient, and enjoyable ways.
When we talk about the UX of Data, we are focusing on three primary goals:
Optimized UX of Data keeps information unsiloed, clearly structured, easily visualized, and within an interface that empowers decision-makers instead of overwhelming them.
Data can of course be used for a variety of purposes within any organization. The ways in which that information is presented and used can greatly influence how effective it is in meeting those purposes.
Some manifestations of data in the workplace include:
Should UX drive data, or should data drive UX?
It can be easy to fall into a “chicken or egg” mentality, but the reality is that neither comes first. Both elements must be considered together because bad UX ruins good data and bad data undermines good UX.
Data still matters in heavily UX-focused projects. Even if the focus is interaction design, relying on analytics for user behavior and designing with future metrics in mind are important.
Data gives UX teams evidence and context. Instead of designing based on assumptions, they design based on real usage patterns and outcomes.
And for data-heavy projects, even if the focus is ETL pipelines and visualization, UX still matters. Interfaces that present data in meaningful, usable ways are essential.
Data teams should rely on UX to ensure their models and visualizations are interpretable, trusted, and actionable. A technically brilliant dashboard is still a failure if nobody wants to or can intuit how to use it.
When UX and Data teams collaborate well, they:
Together, UX and Data teams can co-design dashboards and interfaces that provide real value. But that’s not to say teamwork is always easy. There can be challenges to overcome:
But with the proper approach and expectations, Data and UX teams can deliver effective collaborative projects. Let’s look at dashboards as an example.
Dashboards are definitely an art and a science. When not properly balanced in design, common dashboard complaints include:
Creating a useful dashboard starts with ground-up principles on both UX and Data sides. Design should always start with stakeholder interviews. What do the users really need to know in order to do their jobs?
Design direction should become clearer once stakeholder needs and expectations have been established. Consider:
AI is reshaping how data is used in many interesting ways:
However, that does not mean AI can create its own clarity. UX matters more than ever when it comes to user confidence and overall compliance. AI insights must be transparent to avoid black box risks. Dashboards must explain how AI is being used and why it has made a suggestion. And if humans need to challenge and validate AI outputs, the UX must provide clear and accessible means to do so.
AI is moving beyond passive assistants to agentic interactions. These systems behave more like collaborators than tools. Instead of waiting for queries, they proactively surface insights, guide exploration, and shape the analysis process.
Examples of agentic interactions include AI asking questions like:
“Sales in Region A dropped 15% last quarter. Would you like to explore why?”
“Customer churn is spiking. Do you want to see correlations with marketing spend?”
“You’ve been reviewing Midwest data. Should I show competitor benchmarks for comparison?”
More importantly, agentic interactions will be initiated by the user. Queries like “I see a downward trend in web traffic, what is the primary driver of this trend?” should return a response that clearly shows the channels that have slowed. Previously, this would require digging into Google analytics and knowing what you’re looking for – which may be outside of the skillset of the executive or director that’s accountable for this KPI.
This makes analysis dynamic and conversational, lowering barriers for non-technical users and ensuring leadership never misses emerging trends.
Again, even though agentic systems suggest a human-like nature behind them, they should still have real human guidance to avoid complications. UX teams for agentic systems should emphasize:
Platforms like ThoughtSpot are pioneering this shift, showing how dashboards can move from being static data portals to proactive collaborators in decision-making. For leaders, this means less time digging and more time deciding.
Data without UX risks being ignored. UX without data risks being ungrounded. But together, both empower leaders and teams to act with clarity and confidence.
The UX of Data is not a one-time deliverable. It is a continuous partnership between design, technology, and human judgment. The challenge for UX and Data teams is not only to make systems that work, but to make systems that help people decide and act in the moments that matter.
Join us at Tech Week 2025 for “The UX of Data: Designing for Decision-Making,” where we’ll continue this conversation on how design, data, and AI intersect to drive clarity and innovation.