I Say “Data.” They Feel “Ugh.”
Everyone agrees data matters. So why does the word make so many smart people want to leave the room?
“Data” is too often a four-letter word that makes otherwise confident people feel stupid.
After launching We Dig Data, I consistently got the same question: “What do you mean by data?” It baffled me. To me, everything is data: numbers on a financial statement, an offhand comment in a customer interview, the fact that I’ve now heard someone mention the same book twice in one week, or that my kid is sleeping fewer hours than usual.
Anything that can be counted, observed, or treated as a signal? That’s data.
For me, data has always been a quiet superpower. It cuts through noise, helps me make decisions with confidence, and brings sanity to situations where politics might drown out facts. So, imagine my surprise when the main response to my carefully crafted elevator pitch was not “Cool! That sounds incredibly helpful. Let’s talk.”, but instead a request to clarify the fundamental noun (“data”)!
My first theory was that it was a vocabulary problem: maybe “information” or “insights” would land better. Wrong. I got the same question. My second theory was that data just felt boring or irrelevant to them. Also wrong. These people genuinely believed that good data could improve their work and their lives.
So as I continued to ask colleagues, clients, and friends about what the word “data” meant to them, I found an iceberg. The word “data” wasn’t triggering confusion; it was triggering shame.
“I know I should use it, but I don’t know where to start — and honestly, I don’t like it.”
“I am suffocating in data already. What more do you want me to do?”
“I’m not a numbers person.”
“I’m not technical.”
These aren’t lazy or checked-out people. These are driven, capable, goal-oriented people who regularly stretch outside their comfort zones. The problem wasn’t motivation, but the word itself.
What we learned is that the workplace data literacy gap isn’t just a skills problem. It is also a language problem that became an identity problem. The word “data” has quietly accumulated decades of baggage that makes capable, intelligent people feel inadequate before they’ve even looked at a single spreadsheet.
The word “data” comes with baggage.
When many people ask “what do you mean by data?,” they’re actually asking something deeper. Welcome to manager-turned-therapist territory. Here are four beliefs standing between many people and their own data, and what to do about it.
The word “data” can make someone feel exhausted before the conversation even begins. For many managers and business owners, the word conjures an already-overflowing inbox of dashboards, metrics, reports, and spreadsheets, none of which seem to connect to the actual problem weighing on them. When this group asks “What do you mean by data?,” they’re not asking for a definition. They’re really asking: “Which piece of this overwhelming pile are you about to make my problem?” Lower their resistance by focusing on the business issue they care about.
Instead of: “Let’s analyze your data”
Try: “To grow profitability so you can expand the business, let’s look at who your best customers actually are, which days of the week generate the most revenue, and where cash might be quietly leaking out.”
Some people hear “data” and worry they are about to be exposed as bad at math. This one surprised me. Many high-performing managers and successful entrepreneurs still carry an old belief that they “are not numbers people.” When they hear “data,” they brace for regression models or technical explanations that make them feel out of their depth. They want to know: “Are you speaking the language of business, or the language of statisticians and engineers?” The antidote is to focus on what to measure rather than how to measure it. People rarely fear evidence; they do fear looking stupid.
Try: “Before making staffing changes, let’s measure where your team’s time is going, identify the bottlenecks, and total how much time and money their activities save the company.”
Others hear “data” and assume you’re talking about technology. In many organizations, “data” historically belonged to IT. So they picture databases, cloud infrastructure, and cybersecurity. This group is often trying to clarify a practical question: “Are you talking about a dashboarding software or business insights I need to manage my team?” Anchor your conversation with the business insights and benefit, not with a discussion of the data. People do not wake up wanting cleaner data. They wake up wanting to grow lead pipelines, shorten project timelines, and retain more customers.
Finally, some people think “data” equals “Big Data”, not their everyday operations. Media coverage of data focuses on AI, machine learning, and privacy. These people look at their 5-person team and don’t see how it applies to them. What they miss is their data hiding in plain sight. The sticky notes on their desk. Which customers quickly go from quote to sale. The projects that always run late. This group asks for clarification because they don’t see how the concept applies to their messy, real-world operations.
Try: “Let’s keep track of which customers ghost you after sending them a quote, which customers responded quickly, and what the quotes’ services and terms. This will help us identify patterns that will help us make adjustments.”
Data as a supporting character.
Notice what all four reactions have in common: the remedy is to focus on an outcome the person already cares about, not the data itself. You probably can’t talk someone out of their reaction to the word “data.” That baggage is theirs to unpack. But you can stop making data the main character. When you focus on the outcome, data takes its proper place: not as the point of the work, but as a tool that helps the work get better.
Nobody lies awake worrying about their “data collection strategy.” But plenty of people lie awake wondering why a good client went quiet, whether their team is actually making progress, or where last quarter’s margin disappeared. Those are the conversations data was meant to support.
Someone’s reaction to the topic of “data” has been shaped by years of their own professional experience, assumptions, frustrations, and even identity. And that is certainly more baggage than I intend to raise and unpack during an elevator pitch.
So, we are practicing something different. We try to talk about what data does, rather than what it is. We talk about turning an existing, abundant asset (data) into a manager’s survival guide. We talk about finding proof instead of relying on guesses. We talk about spotting patterns before problems become expensive. By shifting the conversation from numbers to evidence and decisions, you lower anxiety and connect more directly to what people care about.
Easier said than done, of course. But if this resonates and you’d like to try it, here’s a cheat sheet we’ve started and are adding to as we go:
Don’t say: “Data collection.” Say: “Keeping track of ...”
Don’t say: “Data analysis.” Say: “Looking for patterns...”
Don’t say: “Data-driven decisions.” Say: “Making a choice based on proof, not a guess.”
Don’t say: “Utilizing data-driven performance management and focusing on core KPIs and OKRs.”
Say: “Building a scoreboard so everyone knows exactly how to win the game.”
If nothing else, this prevents eyes glazing over when you mention the word “data” to someone who doesn’t work in data. :-)
What We’re Reading Right Now
In honor of this week’s theme, here are some articles where people are writing about data as an enabler to affect change in the tech industry, to lead better, to invest better, and to work better.
Karo (Product with Attitude) and Dinah talk on Code Like A Girl about Substack Bestsellers lists and affecting change in Women Rising: Build the Flywheel. The Badge Follows.
Alina Khay writes about whether the ‘rules of thumb’ for seasonality and stock market returns actually hold up The Calendar’s Seasonality Quiet Edge.
Gustavo Razzetti writes about leadership and habits of winning teams in Progress Is Fuel: Why Perfectionist Leaders Never Win.
We Dig Data talks about how to fix the process before jumping into a new tool in Process Before Platforms.