How to turn data into product ‘sense’

Today, product decisions are rarely impulsive. Whether you're optimising workflows, deciding feature prioritisation or creating a new user journey, data plays a crucial role. 

But it isn't data that drives product, it's the transformation of raw numbers to insights that fuels intuition – providing product managers the pathway to understand trends. These pathways also guide product managers to fulfil market needs essential for growth.

As someone who transitioned from business analysis and project management to product, I’ve worked on projects either with heaps of data but no direction, or a single, well-crafted data story that changed the course of a product roadmap.

In this article, I’ll explore how product managers can harness raw data and use it to build product 'sense' – intuition mixed with actionable frameworks.

What is product sense? Why does data fail to acknowledge it?

Product sense is the intuition that helps product manager know what to build, when, and why.

It holds the ability to:

• Understand what users need before they make it obvious
• Expect what will make a product useful
• Connect business goals with user experience

Data, on the other hand, is the real time link of truth between you and the product. Based in fact,, a snippet at a point of time. It tells you what has happened, not what should happen next. Data is not futuristic.

So, when product managers become too data-driven without insight, they risk:

• Over-optimizing for the wrong metrics
• Prioritizing easily measurable but less meaningful work
• Ignoring context from macro view

Bridging this gap requires more than dashboards. It requires data storytelling.

Data alone doesn’t speak. You need to ask the right questions.

One of the biggest shifts I’ve experienced in my transition from business analysis to product management is: while analysts focus on what happened, product folks need to know why it matters. Answer to the 'why' starts by asking better questions.

For instance, when you work on datasets like user behavior, funnel drop-offs or sales metrics, don’t dive straight into charts. Step back and ask:

• What are we trying to learn?
• What decisions do we need to make?
• What’s the impact on the user?

Keep the goal clear. This framing ensures your analysis is purposeful.

For example, a 20% drop in onboarding journey might trigger panic but if you know that a new KYC compliance step was added recently, you can dig deeper to identify where friction arose in the workflow.

That’s when data becomes insight.

Qualitative vs quantitative data

This was a game changer for me: learning to combine qualitative data with quantitative. Numbers tell you what users do; support tickets tell you what users really do.

Understand the difference between vanity metrics and actionable metrics.

• Vanity is a feel-good metric, for example, total number of users landing on your product page
• Actionable metrics reflect behaviour, retention, or revenue, for example total number of users renewing product subscription.

Let’s say you notice a sharp rise in drop-offs at the payment screen. The metrics tell you where the problem is, but not what the user is thinking in that moment.

That’s where contextual data like customer support logs, website analytics and user interviews become handy. I once discovered how users were abandoning an onboarding journey because the address field didn’t accept apartment numbers in the expected format.

A quick fix led to a 12% boost in conversion. That would never have surfaced through dashboards alone.

This analysis becomes crucial in the digital era where attention span is at all-time low. And where your competition is one step away in locking your potential customer.

Product managers must periodically test their product workflows to understand user pain points that wouldn't come to notice right away. Do it when you’re out of work as fresh eyes and fresh mindset brings out better perspective.

Tell the story behind the spike and dip

Good product managers don’t just present dashboards. They tell stories with data. Stories that encompass journey, market trends.

Think of it like this: Dashboards tell. Stories sell.

During my time working on a banking product, we noticed that conversion rates for one digital channel had dropped. A flat graph showed the drop. It wasn’t until I pulled in customer support feedback, layered it with funnel data, and framed it like a user journey that leadership began to pay attention.

Dip conversion rate occurred because user would drop off at document upload page due to unclear file formats and document size concerns. The user journey story backed by data got us the feature prioritized.

Translate insights into design decisions

Data is only powerful if it leads to action. The best product managers don’t stop at reporting. Instead, they use insights to shape the product.

Let’s say, your analytics report shows most users drop off on step 3 of a five-step flow. There’s your hint to redesign that specific step.

Or let’s say a screen heatmap shows users often click on a non-clickable area. Maybe that’s a sign users expect something there like a button. So, next step would be to revisit user interface design.

I’ve worked closely with UX and development teams to translate these kinds of insights into interface tweaks and flow simplification.

Mistakes to avoid when using data in product work

• Vanity metrics vs actionable metrics: As described earlier, don’t rely feel good metrics that add little value.
• Correlation does not equal causation: Just because two things move together doesn’t mean one caused the other.
• Ignoring edge cases: Sometimes outliers reveal more than the average.
• Overcomplicating the story: Your audience is not always data-literate. Simplify. Avoid jargons.

Teach the team to (also) see through the data

Product sense isn’t built in isolation. As a product manager, I make it part of my job helping others like developers, designers, business teams understand and develop the same curiosity and comfort with data-driven insights.

It’s as important for them as it is for product manager to know the why and how data trend will benefit customers using the product. 

I’ve seen firsthand how being transparent about process, goals encourage better collaboration and openness from peers. Sometimes, their suggestions have revealed discoveries I would’ve missed as a PM, if it weren’t for sharing.

When everyone starts thinking like a product analyst, the quality of decision-making improves across the board.

Mindset shift from data-driven to insight-led

Here’s a subtle but important shift: don’t be only data-driven. Be insight-led.

Data-driven implies data is the king. But it’s not the case.

Data can be messy, delayed, incorrect or misleading. Having worked with millions of datapoints and deriving patterns out of it, I can say that data can be notorious for being skewed and appear biased to the PM eyes (confirmation bias – the hidden devil).

Hence, it becomes imperative to let loose and view from different angles.

Being insight-led means using data in context, balancing it with intuition, user empathy, and business sense. Not to forget, current market trends too.

Final thoughts

Product sense is a muscle. It’s about developing the judgment to know what matters, what to ignore, and when to act.

In my journey from business analyst to product manager, I’ve learned the difference between good and great PMs often lies in their ability to:

• Zoom in and out of the data
• Translate findings into stories
• Balance intuition with evidence

If you’re transitioning from business analysis or engineering into product, you already have data muscle. All you need is to exercise your muscle by learning to apply it with purpose, and action.

Because in the end, product sense is just applied curiosity using data as your compass.

Read more great data content on Mind the Product

• Lessons from building data products
• Demystifying data as a product
• Storytelling and driving accountability with data