Be the Signal in the Noise

Anyone who has spent time around aviation knows that radio traffic can get busy fast.

There are call signs, headings, altitudes, runway assignments, wind checks, hold-short instructions, and that one pilot who somehow sounds like they are broadcasting from inside a washing machine. Everyone is talking. Everyone has important information. Everyone believes their message matters.

And yet, in the middle of all that noise, the pilot has one essential job: hear the instruction that applies to this flight, in this moment, under these conditions.

That is not a bad metaphor for organizational decision-making.

Most leaders today are not suffering from a lack of information. They are surrounded by it. Dashboards refresh constantly. Reports arrive weekly. AI can summarize anything that sits still long enough. Customer quotes appear in Slack. Metrics show up in meetings. Forecasts, trackers, journey maps, personas, win-loss studies, brand health reports, social listening alerts, and competitive scans all compete for attention.

And somehow, with all of that available, the most common question in the room is still: “So what should we actually do?”

This is the challenge, and the opportunity, for insights teams.

Our job is not simply to add more information to the system. The system has plenty. Some days it has so much information that you expect the dashboard to ask for a snack and a nap.

Our job is to help decision-makers find the signal in the noise.

Actually, I would take that one step further.

Our job is to be the signal in the noise.

I started thinking about this after reading Crispin Beale’s recent Insights Association submission. Here is the article, in which he asked insight leaders around the world what market research and insights organizations need to “get away from” to improve efficiency and impact. The responses pointed to a common theme: more data is not the same as more understanding, and the real work is synthesis, judgment, interpretation, and helping organizations act more wisely.

If we’re serious about elevating impact, we need to move away from performative research rituals and toward disciplined synthesis. The better use of client time isn’t passive observation, it’s sharper engagement upfront and downstream: pressure-testing hypotheses, refining the business questions, and interrogating implications. Let researchers do the work of finding signal in noise and bring stakeholders in where their thinking compounds the outcome.

Michaela Gascon, CEO/President, KJT USA

For me, three takeaways rose to the top:

Simplify without sacrificing contextual complexity.

Democratize data without disintermediating meaning.

Amplify impact without performance antics.

That last one may sound like something you need to stretch before saying out loud, but it matters.

Simplify without making things simplistic

There is a dangerous kind of simplification that happens in organizations.

It usually begins with good intentions. Someone says, “Can we make this easier to digest?” Reasonable request. No one wants to read a 74-slide deck unless there is a hostage situation or really good catering.

But then “easier to digest” slowly becomes “remove all the nuance.” The messy context gets trimmed. The contradictions get smoothed over. The uncertainty gets hidden. The inconvenient finding is moved to the appendix, where it essentially becomes a ghost.

What remains is simple, yes. But it may no longer be true enough to guide a good decision.

Good insight work does not eliminate complexity. It gives complexity a usable shape.

That means helping stakeholders understand what matters most without pretending the rest does not exist. It means separating what we know from what we infer. It means saying, “This is the pattern, this is the context, this is the tension, and this is the decision it should inform.”

The best insights are not always the shortest. They are the clearest.

There is a difference.

A simplistic insight says, “Customers want convenience.”

A useful insight says, “Customers say they want convenience, but what they really reward is confidence. They want to feel that the process will work, that someone has thought through the handoffs, and that they will not have to become a part-time project manager just to get the thing they already paid for.”

That second version is still simple enough to act on. But it preserves the complexity that makes it meaningful.

In aviation terms, it is the difference between saying, “Land the plane,” and saying, “You are cleared to land runway 27, winds 260 at 12, traffic departing ahead.” (Meaning, your landing to the west with lights winds from the west, beware of aircraft wingtip turbulence ahead of you as you approach.)

Both are brief. Only one helps you land safely.

Democratize data without abandoning meaning

I am a believer in data democratization. More people in an organization should have access to useful information. Evidence should not be locked away in a research vault guarded by a dragon in business casual.

But access is not the same as understanding.

Giving everyone access to dashboards without shared meaning is like giving everyone cockpit instruments and saying, “Good luck with the crosswind.” Technically, the data is there. Practically, we may have just created a more sophisticated way to be confused.

This is where insights teams play a critical role.

We should not be gatekeepers in the old sense. We should not hoard information or make people submit a ticket every time they have a question. But we also should not confuse self-service access with self-service interpretation.

Meaning still needs stewardship.

Organizations need people who can help define what metrics mean, where they are useful, where they are misleading, and what decisions they should and should not influence. They need people who can connect research findings across time, methods, audiences, and business questions. They need people who can say, “This dashboard shows a movement, but here is the customer context behind it.”

Without that layer of interpretation, data democratization can become data fragmentation. Everyone sees a number. Everyone builds a story. Everyone walks into the meeting with confidence. Then we spend 45 minutes debating whose number is “right,” which is a wonderful use of time if your hobby is slowly aging under fluorescent lights.

The goal is not just more access.

The goal is better shared understanding.

That requires insight teams to move from being producers of reports to stewards of meaning. We help the organization understand not only what the data says, but what the data is qualified to say. We help people avoid over-reading, under-reading, cherry-picking, and the ever-popular “this chart confirms what I already wanted to do” maneuver.

Data can be democratized.

Meaning has to be cultivated.