Where’s My Insight? Escaping the Waldo Effect in Data

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Written By Jeff Winter

Ever feel like your job quietly turned into a grown‑up game of Where’s Waldo?

Only instead of scanning a crowded cartoon beach for a guy in a red‑and‑white sweater, you’re scanning through dashboards, exports, email threads, MES logs, ERP reports and “quick” spreadsheets… all to answer what sounds like a simple question.

“Why did our OEE drop last week?”
“What’s the most profitable product this quarter?”
“Where’s the bottleneck actually coming from?”

You’re not resisting data. You’re doing your best to work with it. But it often feels like the more your organization measures, the harder it gets to actually see what matters. It’s not that we’re not trying. It’s that we’ve built an ecosystem that captures everything, but explains nothing.

Yet when someone asks one of those questions, you pause… then begin your own version of Where’s My Insight?

It’s not that we’re not capturing the info. It’s that it’s everywhere, scattered, siloed, and screaming for structure. Insight doesn’t come from more data.

That’s the punchline most teams are living right now: the page is full, but Waldo is still hiding.

The hidden cost of playing “Where’s Waldo?” with data

The “Where’s Waldo?” feeling isn’t just annoying, it’s expensive.

A 2022 Forrester-backed study summarized by the CDP Institute found that knowledge workers in large organizations use an average of 367 different apps and systems, and spend about 30% of their time just looking for data.

Gartner’s 2023 Digital Worker Survey tells a similar story: 47% of digital workers say they struggle to find the information or data they need to do their jobs, and the average desk worker is juggling 11 different applications every day.

That’s a lot of time spent zooming, filtering, exporting, and DM’ing people for “the real” version of a metric before you even start thinking about root cause or action. And while everyone is busy searching, the data pile keeps growing. According to the 2025 article Dark Data in Business Intelligence in the past couple of years estimate that around 80–90% of enterprise data is effectively “dark”, collected and stored but rarely used, and that nearly 90% of enterprise data is unstructured, sitting in documents, emails, videos, and other formats that are hard to analyze systematically.

So the hidden tax looks like this:

  • Time lost hunting for numbers across systems

  • Cognitive load from reconciling conflicting “truths”

  • Money spent storing data that never turns into decisions

  • Frustration when leadership says “data-driven” but day‑to‑day feels like guess‑and‑check

At the same time, expectations are skyrocketing. In a Wavestone survey from 2023 and an updated 2024 follow‑up, roughly three-quarters of organizations said that data‑driven decision‑making is a top goal of their data programs, yet in the 2024 results, 67% of respondents admitted they still don’t fully trust the data they rely on for those decisions, up from 55% just a year earlier.

In other words: everyone wants to find Waldo. Very few people are sure they’re looking at the right page.

How the “Waldo effect” shows up in your day

If you work with data regularly, you’ll recognize a few flavors of this.

Sometimes you know exactly what you’re looking for. You need last week’s OEE trend for Line 3, broken down by shift. You’re pretty sure you’ve seen that chart before. You log into one tool, then another. You export a CSV “just in case.” You ping a colleague to ask which dashboard is the “official” one. Twenty minutes later, you’re still zooming in and out, like staring at a Waldo page where everyone decided to wear stripes that day.

Other times, you know the zone, but not the exact answer. You can feel that something is off—throughput dropped somewhere in the middle of the process, or quality complaints ramped up mid‑month—but you don’t know where to point your eyes yet. You start filtering by date, then by line, then by product. Graphs wiggle, bars shift, but nothing screams, “Here I am!” You’re not uncovering insight so much as waiting for a pattern to accidentally pop out at you.

Then there are the times where you don’t even know what Waldo looks like yet. You’re exploring a messy situation: orders are late even though utilization looks fine, profitability is down even though volume is up, or customer churn feels higher even though your top‑line metrics look stable. In those cases, dumping more data onto the screen rarely helps. What you need is a way to connect events, context, and data into a story that makes sense.

In all three scenarios, the common thread is the same: the data exists, but it’s not organized in a way that makes the important thing obvious. It’s just one more character lost in the crowd.

Why “more dashboards” usually makes it worse

When leaders realize they aren’t getting the insight they expected from their investments, the reflex is almost always, “We need another dashboard,” or, “Let’s add that metric to the report.”

That’s how you end up with 47 KPIs, five slightly different OEE calculations, and three profitability views that never quite agree with each other.

The Wavestone survey underscores this: data and AI investments keep rising, but only about 37% of leaders say they’ve successfully improved data quality, and cultural/organizational issues still overshadow technology as the main obstacle to becoming truly data‑driven.

The result is a kind of dashboard inflation. Everyone’s working hard, more charts keep appearing, but the signal-to-noise ratio doesn’t actually improve. You’ve just created more Waldo pages.

Changing the game: from “Where’s my insight?” to “Here’s what matters.”

So what does getting out of Waldo‑mode look like in practice?

First, it means starting with the question, not the data. “We need better visibility” is not a question. “We need to know why OEE moved last week and what to do about it this week” is. “We need more analytics” is not a question. “We need to know which products are truly profitable once scrap, rework, and rush shipping are factored in” is. Clear questions narrow the page and tell everyone what “Waldo” actually looks like.

Second, it means reducing the number of “official truths.” If there are three ways to calculate the same metric, no amount of visualization will fix the confusion. Agreeing on how you define things like OEE, downtime, yield, on‑time delivery, or margin and then embedding those definitions directly into your systems, removes an enormous amount of friction. Instead of arguing over whose Waldo is correct, you’re finally talking about what to do with the one you all see.

Third, it means bringing context to the data, not just more data to the screen. A dip in performance means something different if there was a changeover, a supplier issue, a maintenance event, or an unusual product mix on that day. Capturing and linking that context—operator notes, maintenance logs, schedule changes, environmental factors—to your core metrics is what turns a random pattern into an explanation. The annotated version of the chart is usually where the “Aha!” lives.

From there, it’s about designing your analytics for insight moments, not just pretty outputs. A useful view doesn’t just say, “Here are 20 lines of trend data.” It tells a small story:

  • Here’s what changed.

  • Here’s where and when it changed.

  • Here are the likely reasons, based on context.

  • Here’s what we recommend checking or doing next.

That’s the equivalent of drawing a subtle circle around Waldo rather than leaving everyone to stare at a cluttered scene and hope for the best.

Finally, there’s the feedback loop. Insight isn’t the end of the process; it’s the beginning of action. The teams who start to escape the Waldo trap are the ones who track which questions keep coming up, which insights actually lead to decisions, and which dashboards never get used at all. Over time, that allows them to simplify, retire noisy views, and invest in the few “pages” that consistently help people make better choices.

So the next time someone asks, “Why did our OEE drop last week?” or “What’s our most profitable product this quarter?”, the goal isn’t to trigger another frantic round of Where’s My Insight?

It’s to be able to flip to the right “page,” see the pattern clearly, and say:

“Right here. Found it.
And here’s what we’re going to do next.”

References:

Jeff Winter
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