From Data to Insights

When you start researching, you’ll quickly drown in data. It’s easy to get lost. But if you trust the process—organizing what you’ve gathered, adding context, connecting the dots—you’ll find patterns and actionable insights after passing the messy middle That’s where the magic happens.

To navigate it well, you need to understand what you’re working with. We use terms like data, information, knowledge, and insights interchangeably—but they’re not the same. Knowing the difference helps you aim for what really matters.

The Building Blocks of Understanding

Data: Raw facts, numbers, or opinions. They’re isolated and context-free.

Example: "CR is 2.5%." / "Someone likes fries."

Information: Data in context. Now it tells us something.

Example: "Our CR was 2.5% on September 3rd." / "Our ICP tends to like fries."

Knowledge: Information that’s been interpreted and connected.

Example: "Sales of ice cream increase when temperatures rise above 20°C."

Insight: A shift in perspective. It reveals a new opportunity, a cause-effect link, or a surprising truth that helps guide decisions.

Example: "Users churn because our onboarding doesn’t answer their first question."

In practice, these lines blur. So let’s bundle them together under one name: intellectual capital (IC). You won’t neatly separate these in daily practice, but it shows that the insights emerge when you connect the dots, add context and put them into contrast.

Not All Insights Are Equal

Some signals are weak. A single story from your CEO might be interesting, but not enough to act on. Still, don’t ignore it. If similar stories keep popping up across user interviews or support chats, you’re seeing a pattern. That’s called convergent validity—multiple, independent sources pointing to the same truth.

Organize by Type. Contrast to Learn.

One way to make sense of your intellectual capital is to group similar data together, then compare across groups.

For example:

• Competitor features, prices, and USPs → Bucket A

• Your own features, prices, and USPs → Bucket B

By comparing similar types of information—features to features, prices to prices—you spot patterns:

• “Our product has fewer features.”

• “Customers love X, but we don’t talk about it on our site.”

• “Our competitors highlight benefits we don’t even mention.”

Now go further: What do your customers say your unique strength is? What makes you stand out that nobody else can claim? When you compare that to your own messaging and your competitors’, you might find a hidden strength you’ve been underselling.

Insights aren’t just found in what’s there—they’re also in the gaps.

From Insight to Action: Ask Better Questions

Good insights don’t come from isolated numbers. They come from curiosity.

Let’s say you see that your conversion rate is 2.5%. That’s just a data point. But it sparks a question:

"Is that good or bad?"

Then more:

"How does it differ by channel?"

"What’s the CR for mobile vs. desktop?"

"What happened before and after we changed the landing page?"

The contrast allows us to judge the information we see.

This is how you build a network of insight—not just gather loose facts, but connected ideas that deepen your understanding and drive action.

Building Relationships

By putting the insights into relation we can conclude really interesting things.

Let me give you a simple example:

• When customers express why they love the product we have interesting information. But that alone is not useful.

• When we compare this to the USPs we communicate on our homepage, we get an interesting contrast. USPs that come from customers are more convincing. They are evidence-based.

• Now we can use the USPs from our competitor to match this against our own. If the benefits the user has from using our product are not much different, we might uncover an interesting problem. Our differentiator should be strong enough that customers choose our solution over the other.

• We can take this even further: Let’s now imagine we have a customer segmentation in place and the strategic decision to double down on a small but fast-growing customer segment.

• We can start to listen explicitly to them to find what benefits we provide and build up dedicated communication for that group.

What can we learn from that? Let’s derive some insights.

E.g.

• Our differentiator is not strong enough we should do better

• Our USPs don’t relate to what our users say

• We have no clue what our customers like about our product

• We don’t understand our competitors well enough

We end up with some key insights. The conclusion is an opinion or fact. But this gets pretty actionable, no matter if we conclude to perform more research, inform our strategy, or just change the product.

Go Broad. Then Go Deep.

Don’t pick between breadth and depth. Use both.

Start by gathering widely: user quotes, metrics, anecdotes, research, support tickets. Then zoom in where something feels off, interesting, or unclear.

As you add context and contrast, connections start to form. You move from “What’s happening?” to “Why is this happening?” to “What should we do about it?”

That’s how raw data leads to clarity. That’s how insights lead to smart decisions.