Are you stopping one step short of driving change through user insights? | by Kai Wong | Jan, 2024


Why creating user insights, not presenting information, is critical to persuade teams.

A hole in a wall that shows an idyllic green field and ‘greener pastures’ on the other side
Photo by Finn Semmer:

Working in startups taught me the power of user insights and why I never seemed to be able to get approval for meaningful changes previously.

You may have encountered this before: you do a user test, gather data, and present several key user findings around what you’ve discovered.

While your team is on board with minor changes (like changing button text), they never seem to approve any design recommendations that could make a real difference.

What I’ve discovered, after digging into Data Storytelling, was that what I found wasn’t the issue: it was how I presented it to the team. As many of you, I was presenting information when I needed to communicate insights.

The difference between Informing, where one person tells someone something in a one-direction arrow, and Communicating, where there’s a two-way arrow betweeen both audience and listener, and they both have the same insights and ideas

To understand the difference, let’s first talk about the Data pyramid and why you’re likely stopping one step short of your team’s needs.

DIKW pyramid, and why presenting information isn’t enough

To explain why you’re stopping one step short of providing actionable insights, we need to take a step back and talk about the DIKW pyramid, otherwise known as the Data pyramid.

This is a model of the steps people go through with knowledge management and learning, and this provides one of the critical insights into why presenting information isn’t enough.

To help illustrate this, let’s go through a user research presentation around user frustration to showcase this.

The first level is called “Data,” but Raw Data is a better description. You would likely see this immediately after collecting user testing responses. This would be your spreadsheet of responses with essentially 0 analysis done to it.

For example, it might consist of:

  • Participant 1 said X and liked Y
  • Participant 2 said Z and liked X


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