Why UX Research Matters: Insights from a Deep Dive into Data Observability
Dec 19, 2024
Why UX Research Matters: Insights from a Deep Dive into Data Observability
If you’ve ever worked on a product and wondered, “Are we building the right thing?” then you already understand the value of user research. Good UX research helps teams move beyond assumptions and guesswork to build products that solve real problems. In a recent research study on data observability, we uncovered major gaps in existing tools, identified opportunities for innovation, and learned just how essential intuitive design is—especially for non-technical users.
This article walks through why this kind of research is valuable, how it helps multiple teams make better decisions, and how we measure the success of a research study. Plus, I’ll share some of the most eye-opening feedback from real users.
The Value of UX Research: Why Bother?
User research isn’t just about gathering feedback; it’s about understanding the underlying needs and frustrations that users might not even articulate themselves. In our study, we focused on data observability workflows, looking at both product gaps (features that were missing or broken) and user gaps (which personas struggled to use the tools effectively).
Here’s what we learned:
Data observability tools often fail non-technical users.
Many users wanted no-code solutions because their teams weren’t all technical.
Business users and data stewards needed better usability and simpler workflows.
As Aparna Vaidya, Vice President at Franklin Templeton, put it:
“Our business analysts don’t need SQL—they need answers.”
Data lineage is crucial—but often unusable.
Users wanted a Google Maps-like interface for navigating data lineage, instead of clunky diagrams.
100% of users interviewed referenced “stoplight” colors (red, yellow, green) as a mental model for data status.
Nathan Caplan, Director of Data Governance at Kenvue, emphasized the need for better tracking:
“For every critical data element in the company, I want to see how that data flows—from the moment it enters our ecosystem to when it leaves.”
Data engineers need better tools for profiling and monitoring.
Many teams rely on manual workflows to clean and validate data before monitoring.
90% of the time, users relied on Virtual Tables and Deltas, but these didn’t integrate well into lineage tools.
Philipp Leufke, Tech Lead of Data Platform at Vay, explained how automation could help speed up migrations:
“Profiling should help us shorten the effort of migrating to a new system, not add to it.”
These insights weren’t things we could have guessed. They only emerged through structured, focused research.
How Research Helps Teams Prioritize and Make Better Decisions
One of the biggest challenges in product development is deciding what to build next. Every team has competing priorities—engineering wants scalability, product wants differentiation, sales wants features that close deals. So how does research help?
It clarifies what actually matters to users.
We analyzed hundreds of interview transcripts and feature requests to identify patterns.
This helped product managers cut through the noise and focus on the most impactful pain points.
It aligns teams on the "why" behind decisions.
Instead of debating which features “feel” important, teams could point to real user data.
One example: our research showed that business users wanted a unified data catalog, not just an observability tool. This completely shifted the roadmap.
A user at Super, a fintech company, put it best:
“We don’t want a dozen different dashboards—we want one place where everything connects.”
It helps justify investments to leadership.
Research findings make it easier to secure buy-in for new features.
Instead of pitching “we think this is important,” teams could say, “80% of users requested this exact functionality.”
In short, research ensures we’re building what users actually need, not just what seems like a good idea internally.
How Do You Measure the Success of UX Research?
Running a research study is one thing—proving its impact is another. So how do we know if research was successful?
1. Did it lead to real product changes?
The biggest indicator of success is whether the insights influenced the roadmap.
In this case, our research directly led to:
A redesigned data lineage UI with a stoplight color system.
A new focus on no-code workflows for business users.
Improvements to issue resolution and integration of AI.
2. Did it help teams make better decisions?
We looked at how often teams referenced research findings in decision-making.
Product managers, designers, and engineers used the insights in planning meetings, design reviews, and prioritization discussions.
Mike Dunn, Senior Data Engineer at Freedom Mortgage, highlighted how valuable the findings were:
“Before this research, we were flying blind. Now we know exactly where the pain points are.”
3. Did it impact key business metrics?
We tracked how changes inspired by research affected user engagement and retention.
For example, after simplifying the UI for non-technical users, adoption rates among business users increased by 23%.
4. Did stakeholders find it useful?
The final research report was shared with leadership, product teams, and engineering.
A C-suite executive from BCBS summed it up perfectly:
“This research is a goldmine. We need more of this.”
Final Thoughts: Making UX Research a Core Part of Product Strategy
This study reinforced a simple but powerful lesson: when you talk to users, you build better products. The insights we gathered helped align teams, prioritize the right features, and ultimately create a more user-friendly data observability experience.
If you’re working on a product and haven’t invested in user research, ask yourself:
Are we making decisions based on assumptions or real user feedback?
How often are we talking to our users?
Do we have a process for incorporating research into our roadmap?
UX research isn’t a nice-to-have—it’s a necessity for building products that people actually love to use.
Why UX Research Matters: Insights from a Deep Dive into Data Observability
If you’ve ever worked on a product and wondered, “Are we building the right thing?” then you already understand the value of user research. Good UX research helps teams move beyond assumptions and guesswork to build products that solve real problems. In a recent research study on data observability, we uncovered major gaps in existing tools, identified opportunities for innovation, and learned just how essential intuitive design is—especially for non-technical users.
This article walks through why this kind of research is valuable, how it helps multiple teams make better decisions, and how we measure the success of a research study. Plus, I’ll share some of the most eye-opening feedback from real users.
The Value of UX Research: Why Bother?
User research isn’t just about gathering feedback; it’s about understanding the underlying needs and frustrations that users might not even articulate themselves. In our study, we focused on data observability workflows, looking at both product gaps (features that were missing or broken) and user gaps (which personas struggled to use the tools effectively).
Here’s what we learned:
Data observability tools often fail non-technical users.
Many users wanted no-code solutions because their teams weren’t all technical.
Business users and data stewards needed better usability and simpler workflows.
As Aparna Vaidya, Vice President at Franklin Templeton, put it:
“Our business analysts don’t need SQL—they need answers.”
Data lineage is crucial—but often unusable.
Users wanted a Google Maps-like interface for navigating data lineage, instead of clunky diagrams.
100% of users interviewed referenced “stoplight” colors (red, yellow, green) as a mental model for data status.
Nathan Caplan, Director of Data Governance at Kenvue, emphasized the need for better tracking:
“For every critical data element in the company, I want to see how that data flows—from the moment it enters our ecosystem to when it leaves.”
Data engineers need better tools for profiling and monitoring.
Many teams rely on manual workflows to clean and validate data before monitoring.
90% of the time, users relied on Virtual Tables and Deltas, but these didn’t integrate well into lineage tools.
Philipp Leufke, Tech Lead of Data Platform at Vay, explained how automation could help speed up migrations:
“Profiling should help us shorten the effort of migrating to a new system, not add to it.”
These insights weren’t things we could have guessed. They only emerged through structured, focused research.
How Research Helps Teams Prioritize and Make Better Decisions
One of the biggest challenges in product development is deciding what to build next. Every team has competing priorities—engineering wants scalability, product wants differentiation, sales wants features that close deals. So how does research help?
It clarifies what actually matters to users.
We analyzed hundreds of interview transcripts and feature requests to identify patterns.
This helped product managers cut through the noise and focus on the most impactful pain points.
It aligns teams on the "why" behind decisions.
Instead of debating which features “feel” important, teams could point to real user data.
One example: our research showed that business users wanted a unified data catalog, not just an observability tool. This completely shifted the roadmap.
A user at Super, a fintech company, put it best:
“We don’t want a dozen different dashboards—we want one place where everything connects.”
It helps justify investments to leadership.
Research findings make it easier to secure buy-in for new features.
Instead of pitching “we think this is important,” teams could say, “80% of users requested this exact functionality.”
In short, research ensures we’re building what users actually need, not just what seems like a good idea internally.
How Do You Measure the Success of UX Research?
Running a research study is one thing—proving its impact is another. So how do we know if research was successful?
1. Did it lead to real product changes?
The biggest indicator of success is whether the insights influenced the roadmap.
In this case, our research directly led to:
A redesigned data lineage UI with a stoplight color system.
A new focus on no-code workflows for business users.
Improvements to issue resolution and integration of AI.
2. Did it help teams make better decisions?
We looked at how often teams referenced research findings in decision-making.
Product managers, designers, and engineers used the insights in planning meetings, design reviews, and prioritization discussions.
Mike Dunn, Senior Data Engineer at Freedom Mortgage, highlighted how valuable the findings were:
“Before this research, we were flying blind. Now we know exactly where the pain points are.”
3. Did it impact key business metrics?
We tracked how changes inspired by research affected user engagement and retention.
For example, after simplifying the UI for non-technical users, adoption rates among business users increased by 23%.
4. Did stakeholders find it useful?
The final research report was shared with leadership, product teams, and engineering.
A C-suite executive from BCBS summed it up perfectly:
“This research is a goldmine. We need more of this.”
Final Thoughts: Making UX Research a Core Part of Product Strategy
This study reinforced a simple but powerful lesson: when you talk to users, you build better products. The insights we gathered helped align teams, prioritize the right features, and ultimately create a more user-friendly data observability experience.
If you’re working on a product and haven’t invested in user research, ask yourself:
Are we making decisions based on assumptions or real user feedback?
How often are we talking to our users?
Do we have a process for incorporating research into our roadmap?
UX research isn’t a nice-to-have—it’s a necessity for building products that people actually love to use.
Why UX Research Matters: Insights from a Deep Dive into Data Observability
If you’ve ever worked on a product and wondered, “Are we building the right thing?” then you already understand the value of user research. Good UX research helps teams move beyond assumptions and guesswork to build products that solve real problems. In a recent research study on data observability, we uncovered major gaps in existing tools, identified opportunities for innovation, and learned just how essential intuitive design is—especially for non-technical users.
This article walks through why this kind of research is valuable, how it helps multiple teams make better decisions, and how we measure the success of a research study. Plus, I’ll share some of the most eye-opening feedback from real users.
The Value of UX Research: Why Bother?
User research isn’t just about gathering feedback; it’s about understanding the underlying needs and frustrations that users might not even articulate themselves. In our study, we focused on data observability workflows, looking at both product gaps (features that were missing or broken) and user gaps (which personas struggled to use the tools effectively).
Here’s what we learned:
Data observability tools often fail non-technical users.
Many users wanted no-code solutions because their teams weren’t all technical.
Business users and data stewards needed better usability and simpler workflows.
As Aparna Vaidya, Vice President at Franklin Templeton, put it:
“Our business analysts don’t need SQL—they need answers.”
Data lineage is crucial—but often unusable.
Users wanted a Google Maps-like interface for navigating data lineage, instead of clunky diagrams.
100% of users interviewed referenced “stoplight” colors (red, yellow, green) as a mental model for data status.
Nathan Caplan, Director of Data Governance at Kenvue, emphasized the need for better tracking:
“For every critical data element in the company, I want to see how that data flows—from the moment it enters our ecosystem to when it leaves.”
Data engineers need better tools for profiling and monitoring.
Many teams rely on manual workflows to clean and validate data before monitoring.
90% of the time, users relied on Virtual Tables and Deltas, but these didn’t integrate well into lineage tools.
Philipp Leufke, Tech Lead of Data Platform at Vay, explained how automation could help speed up migrations:
“Profiling should help us shorten the effort of migrating to a new system, not add to it.”
These insights weren’t things we could have guessed. They only emerged through structured, focused research.
How Research Helps Teams Prioritize and Make Better Decisions
One of the biggest challenges in product development is deciding what to build next. Every team has competing priorities—engineering wants scalability, product wants differentiation, sales wants features that close deals. So how does research help?
It clarifies what actually matters to users.
We analyzed hundreds of interview transcripts and feature requests to identify patterns.
This helped product managers cut through the noise and focus on the most impactful pain points.
It aligns teams on the "why" behind decisions.
Instead of debating which features “feel” important, teams could point to real user data.
One example: our research showed that business users wanted a unified data catalog, not just an observability tool. This completely shifted the roadmap.
A user at Super, a fintech company, put it best:
“We don’t want a dozen different dashboards—we want one place where everything connects.”
It helps justify investments to leadership.
Research findings make it easier to secure buy-in for new features.
Instead of pitching “we think this is important,” teams could say, “80% of users requested this exact functionality.”
In short, research ensures we’re building what users actually need, not just what seems like a good idea internally.
How Do You Measure the Success of UX Research?
Running a research study is one thing—proving its impact is another. So how do we know if research was successful?
1. Did it lead to real product changes?
The biggest indicator of success is whether the insights influenced the roadmap.
In this case, our research directly led to:
A redesigned data lineage UI with a stoplight color system.
A new focus on no-code workflows for business users.
Improvements to issue resolution and integration of AI.
2. Did it help teams make better decisions?
We looked at how often teams referenced research findings in decision-making.
Product managers, designers, and engineers used the insights in planning meetings, design reviews, and prioritization discussions.
Mike Dunn, Senior Data Engineer at Freedom Mortgage, highlighted how valuable the findings were:
“Before this research, we were flying blind. Now we know exactly where the pain points are.”
3. Did it impact key business metrics?
We tracked how changes inspired by research affected user engagement and retention.
For example, after simplifying the UI for non-technical users, adoption rates among business users increased by 23%.
4. Did stakeholders find it useful?
The final research report was shared with leadership, product teams, and engineering.
A C-suite executive from BCBS summed it up perfectly:
“This research is a goldmine. We need more of this.”
Final Thoughts: Making UX Research a Core Part of Product Strategy
This study reinforced a simple but powerful lesson: when you talk to users, you build better products. The insights we gathered helped align teams, prioritize the right features, and ultimately create a more user-friendly data observability experience.
If you’re working on a product and haven’t invested in user research, ask yourself:
Are we making decisions based on assumptions or real user feedback?
How often are we talking to our users?
Do we have a process for incorporating research into our roadmap?
UX research isn’t a nice-to-have—it’s a necessity for building products that people actually love to use.
