No data is still data: what missing information can tell you
I'm currently watching the Netflix documentary series The Idaho Murders: College Nightmare. Alongside the tragedy and disturbing detail of the case, I've found myself thinking about the role data played in the investigation. Not just the data investigators could find and connect but the moments where missing data became just as important.
When silence speaks
One example involved the suspect's phone. Investigators could trace it connecting to mobile phone towers before and after the murders. But during the exact window when the murders happened, the phone went dark. It stopped connecting to the network meaning it was switched off, put in aeroplane mode, or moved out of range. On its own, a phone dropping off a tower doesn't prove much. But when that silence happens at a critical moment, sitting between two other traceable parts of a journey, the gap becomes part of the story.
The same thing showed up with the suspect's car. Police found nothing inside the vehicle that you'd expect to find after a crime like this. Instead, prosecutors said the car had been cleaned so thoroughly that the interior had basically been stripped down. There was nothing left to point to. And yet, how complete that emptiness was became the interesting part.
It's a strange twist: sometimes finding nothing is exactly what makes you look closer. That doesn't mean every missing value, empty space, or silent person proves something happened. Missing something isn't automatically proof of one specific explanation. It's easy to jump from "There's nothing here" straight to "So I know why", and that jump needs to be resisted. What it does mean is that missing data or a gap in the data shouldn't get written off as a dead end. Sometimes it's just a sign to ask a better question: why is there nothing here when we'd expect something?
The gaps we tend to ignore
At work, we usually focus on what we can see, the numbers in a report the answers in a survey the incidents logged in a system, the customers who complained and the people who spoke up in a meeting. We look at what's been captured because it feels solid and real. Meanwhile, blank cells, missing answers, and unexplained silence get treated as annoying gaps to work around. But those gaps can tell us something important about the system that created the data in the first place. Silence says a lot.
Say reported safety incidents drop to zero after a company makes its reporting process more complicated. Maybe the workplace really did get safer. Or maybe people simply stopped reporting because the new process takes too long or feels like too much hassle. That zero is still data, it just might not mean what you first assumed.
Or an employee survey comes back with fairly positive results, but one team has a very low response rate. You can celebrate the good scores, or you can get curious about the people you didn't hear from and why. Their silence won't tell you exactly what they think. But it might point to something about trust, workload, access or a belief that answering won't change anything.
The same applies when complaints disappear, when a dashboard looks almost too clean or when nobody raises a concern in a meeting about a decision that affects their work. Silence might mean agreement. It might also mean people are tired, worried, checked out, or convinced that speaking up won't change the outcome. If you only look at the voices you can hear, you risk mistaking part of the story for the whole story.
Reading data means reading what's missing, too
This is why understanding data isn't just about reading the numbers in front of you. It also means understanding how those numbers came to exist: who took part, who didn't, what was measured, what was left out, what you expected to see and whether the way the data was collected shaped what showed up.
"No data" becomes meaningful when it breaks a pattern, goes against a reasonable expectation or tells you something about the conditions that created it. The phone going dark in the documentary mattered because of when it happened, sitting between other traceable activity. The spotless car mattered because the emptiness was so complete it raised questions about how it got that way. Context is what turns nothing into something and that idea sits at the heart of good data visualisation and smart data strategy.
What to look for next time
Next time you're looking at a dataset, report, or dashboard, don't just look at what's there. Look for the gaps, the sudden zeros, the groups missing from the sample, the numbers that look almost too clean, and the places where the trail suddenly stops.
The question isn't just 'What does the data tell us?' It's also ‘What should be here, and why isn't it?’ Because no data is still data. We just need to pay close enough attention to notice it.
Source note: Details about the phone were reported in the case affidavit and later news coverage, while prosecutors described the vehicle as thoroughly cleaned. See Boise State Public Radio, Associated Press and Netflix's series overview.
FAQ
Why does missing data matter if there's nothing there to look at?
Missing data isn't automatically meaningless. It can show us something about how the data was collected, who chose not to respond, or what conditions created the gap. In data visualisation, the missing points are often just as telling as the ones on the chart, especially when a gap breaks an otherwise steady pattern.
Does a drop to zero always mean something got better?
Not always. A sudden zero like reported incidents falling to none, could mean a genuine improvement. Or it could mean people have stopped using a process that's become too hard or too risky. The number is still real data, but it needs context before you decide what it's telling you.
What does a low survey response rate actually tell you?
It won't tell you what the people who didn't answer are thinking. But it can point to problems with trust, workload, access, or a belief that answering won't change anything. Treating a low response rate as unimportant is a common mistake in workplace data reviews.
Is it ever wrong to draw conclusions from missing data?
Yes. Missing data doesn't automatically prove one specific cause. The mistake is jumping straight from "There's nothing here" to "So I know why." A better approach is to treat the gap as a reason to ask a sharper question, not proof of a particular answer.
How does this connect to understanding data well?
Understanding data properly isn't just reading the numbers in front of you. It means understanding how those numbers came to exist who was included, who wasn't, what was measured, and what might have shaped the way it was collected. That's often what separates a strong data strategy from one that just reacts to whatever numbers show up.
Who can help teams build this kind of thinking?
Most people treat data as whatever shows up on the dashboard. Learning to read the gaps and ask what’s missing is a skill that separates basic data literacy from strategic thinking. It’s worth investing in your team to build their data confidence and encourage this type of thinking. Whether it’s through structured data training, a data strategy keynote or team specific workshops, it’s worth the investment. The payoff? When your teams stop accepting numbers at face value and start asking what might be hiding in the gaps.
