Collecting Data Is One Thing—Acting on It Is Another

September 9, 2019

The amount of data we produce every day is growing exponentially. This explosion of raw data means synthesis, analysis, and interpretation are more important than ever before. Without the right processes and tools in place to understand and act on our data, it has little value. It is essential that we understand what data is available, how it can answer pressing questions, and how it can enable action.

As UX professionals, we collect a wide array of data through a variety of sources and techniques—from market trends to one-on-one interviews to product-usage data to usability testing to sales feedback. We must collate, classify, and comprehend disparate sources of data to create a more holistic understanding of whatever question we need to answer. While the volume of data might seem overwhelming at first, design thinking and a tinkering mindset are invaluable in helping to break down the problem, define a plan of action, and iterate and refine solutions as necessary to turn the raw data into actionable insights and concrete products.

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Almost a year ago, I convinced my company to implement Pendo, a robust product-analytics tool, because we wanted to start tracking usage across our suite of products. As the sole UX researcher, I led our setup and implementation of Pendo. First, I explored the tool and played around with its various layers of functionality, trying to make sense of it all. I soon became overwhelmed. There were so many ways to slice and dice the data, and I lacked a clear idea of what actionable data and recommendations my internal stakeholders needed. Our products are quite complex, nonlinear, and vary greatly in their maturity, so there were no obvious or universal applications of the data.

But I was determined to push forward anyway, thinking possible solutions would begin to crystallize over time. Some general analyses and use cases came to mind, so I put together a comprehensive report about what I assumed would be most useful to my stakeholders. This was my first mistake. I wasted a lot of time putting together a huge report that no one had really asked for or would use or care about. In this article, I’ll describe what I learned from this experience.

Learning #1: Include your users and stakeholders early and often.

I recognized that my initial approach and report had failed, so I decided I had to iterate. Just as I would for a typical UX-research project, I needed to work closely with users and stakeholders. So I worked with my summer UX-research intern to identify key stakeholders who would find value in the Pendo data. We met with Product, Product Marketing, Marketing, Business Intelligence, Design, and Engineering to learn how Pendo data could help answer their core business questions. We learned what questions they’re focusing on and what problems they’re trying to solve. We gave them a demo of Pendo and generated a sample report that illustrated what data and analyses were available in Pendo. Giving them an overview of the type and depth of data sparked some productive brainstorming and discussions.

Learning #2: Work more strategically, identifying key stakeholders and their concrete needs rather than making assumptions and jumping into analysis.

During these stakeholder meetings, we came up with one or two concrete use cases that we could explore with each group right away. By breaking down and exploring individual use cases, we avoided devoting large chunks of time to creating a bulky report that lacked context—a report that stakeholders might skim briefly, then discard. Pursuing individual use cases enabled easier execution, evaluation, and refinement.

By starting small, we could focus our efforts on the most important use cases, then build on our success over time. By demonstrating clear impact and positive outcomes early on, we increased stakeholder buy-in and confidence.

Learning #3: Break large daunting tasks into more manageable chunks, then iterate, test, and refine incrementally.

The next time you feel overwhelmed by large amounts of data, take a deep breath and acknowledge that you already have the skills you need. Take a step back and think about what you’re trying to achieve overall. Ask yourself these questions:

  • What is your ideal outcome?
  • Who are your stakeholders?
  • What are they trying to accomplish?
  • What data is available?
  • What analytical methods are available?
  • How can you use this data to help answer your stakeholders’ burning questions?
  • Who else can help you analyze the data?
  • How can you make your output optimally digestible and impactful?

Applying Design Thinking When Working with Data

You can apply the design-thinking process shown in Figure 1 when tackling data problems.

Figure 1—Design-thinking process
Design-thinking process

Image source: “What Is Design Thinking? A Comprehensive Beginner’s Guide,” by Emily Stevens, on Career Foundry.

To act on your product-usage data, follow these steps:

  1. Empathize—Identify the stakeholders for your data analysis.
  2. Define—Identify your stakeholders’ main goals, questions, and the problems they need to solve.
  3. Ideate—Determine concrete use cases for the data for each set of stakeholders.
  4. Prototype—Compile, synthesize, and analyze the relevant data for each use case.
  5. Test—Present your data findings and insights to your stakeholders, asking whether they’re useful and actionable.
  6. Iterate and refine—Based on stakeholder feedback, iterate and refine your analysis and data presentation as necessary.
  7. Expand—Add more use cases and repeat this process.


Data collection alone is not enough. You need to make sense of your data, then act on it. Instead of becoming overwhelmed by analysis paralysis and falling into inaction or waiting for the perfect solution to come to you, jump in and put your design-thinking skills to work. Identify the key stakeholders who could and should act on the available data, then learn about and understand their core questions. Instead of working in isolation and making assumptions, collaborate with your stakeholders to review the available data and brainstorm possible use cases. Working together and starting with concrete use cases can save you countless time and effort and make your work infinitely more impactful. 

UX Researcher at Factual

New York, New York, USA

Meghan WenzelMeghan is starting the UX Research team at Factual, a startup focusing on location data. She’s establishing research standards, processes, and metrics; building partnerships across teams, and leading research efforts across all products. Previously, she was a UX Researcher at ADP, where she conducted a wide range of exploratory, concept-testing, and usability research across products and platforms. She was also involved in ADP’s Come See for Yourself contextual-inquiry program, whose goal was to educate colleagues on the value of UX research and get them out into the field to talk to real users.  Read More

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