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Stop Building Dashboards Nobody Asked For

4 hours ago
4 min read

Part 4 of a Series: From Messy Problems to Clear Definitions

When Good Work Goes Nowhere

You can do good analysis… and still create no impact.

By now, we have spent time getting the problem right.

In the earlier articles, we moved from messy situations to clear problem definitions. We broke down KPIs to understand where issues sit, and we used Pirate Metrics to identify where in the journey things break.

By this point, the problem is no longer vague. It is specific, structured, and grounded in context.

And yet, something still goes wrong.

You can have a clearly defined problem, and even know exactly where the breakdown is, but the work that follows still fails to create impact. The issue is no longer about understanding the problem. It is about whether what you build is connected to what the business actually cares about.

A Familiar Situation

Let’s return to the same example.

Sales are down. We narrowed it down. We identified that new customers are not converting through the online channel. That is already a strong level of clarity.

So what happens next?

In many cases, the team moves quickly into building a dashboard. It brings together traffic, conversion rates, drop-offs, and trends over time. It is well designed, accurate, and often quite comprehensive.

But nothing changes.

The dashboard exists. The insight is visible. People look at it. They understand it. And then they move on.

Because the question was never, “What dashboard should we build?”

The real question was, “What outcome are we trying to change?”

A clear problem does not automatically lead to meaningful action. Something is missing in between.

Introducing OKR → Data Product Thinking

This is where most analytics work quietly breaks down. And this is where OKR → Data Product thinking becomes useful.

Instead of starting with data, it forces you to start with outcomes.

OKRs, or Objectives and Key Results, define what success looks like. The Data Product perspective then translates that into something tangible, whether it is a dashboard, a dataset, or a workflow. When these are connected, the question shifts.

It is no longer just about what we are analysing. It becomes about what we are trying to achieve, and how what we build contributes to that outcome.

What It Forces You to Clarify

This shift sounds simple, but it changes the way work is shaped.

You begin by defining the objective in clear terms. In our case, it may be improving new customer conversion for online sales. You then define what success looks like through key results, perhaps increasing the conversion rate over a specific period.

Only after that do you decide what to build.

The output might still be a dashboard. But it might also be something else entirely, such as an experiment, an alerting mechanism, or a targeted intervention.

What matters is that the output is no longer the goal. It becomes a means to move a specific outcome.

How It Works in Practice

Returning to our example, the difference becomes clear.

Instead of starting with a dashboard, we start with the objective: improve new customer conversion for online sales. We define the key result, such as increasing conversion from 2 percent to 3 percent over the next quarter.

With that in place, the decision of what to build becomes more deliberate.

A dashboard may still be useful, but only if it supports monitoring and decision-making toward that goal. In some cases, it may not be sufficient. The better solution might involve testing changes to the user journey or refining how customers are guided through the purchase process.

The work is no longer centred on producing analysis. It becomes centred on driving change.

What you build should exist to move a specific outcome. Not just to display data.

Why This Changes the Way You Work

Once you start from objectives, your work becomes more focused and intentional.

You are no longer building for visibility. You are building for impact. This reduces unnecessary outputs, avoids duplicated effort, and makes it clearer why something is being created in the first place.

More importantly, it changes how analytics fits into the organisation.

Instead of being a function that produces reports, it becomes part of how decisions are made and actions are taken. Dashboards don’t create impact. Decisions do.

The difference is not in the tools. It is in how the work is approached.

Same data. Different starting point. Very different outcome.

A Common Mistake to Avoid

A common mistake is to treat OKRs as something separate from analytics work. Objectives are defined at a management level, while data teams focus on producing dashboards and reports.

This disconnect is exactly what leads to unused outputs.

The purpose of OKR → Data Product thinking is to bridge that gap, ensuring that what is built is directly tied to what the business is trying to achieve.

Connecting Back to the Bigger Picture

In the earlier articles, we focused on getting the problem right. We introduced structure, broke down KPIs, and identified where performance breaks within a journey.

OKR → Data Product builds on that foundation by ensuring that once the problem is clear, the work that follows is aligned to a meaningful outcome.

It connects understanding to action.

Where We Go Next

Even when problems are clear and work is aligned to outcomes, another issue often emerges.

The solution is built, but no one really uses it.

In the next article, we will look at the Data Team Lean Canvas, and how it helps ensure that what you build is not only aligned to outcomes, but also relevant to the people who need it.