you heard that the human attention span is shorter than that of a goldfish?
According to Microsoft’s 2015 study, the average human attention span decreased from 12 seconds in 2000 to 8 seconds in 2013. The same report stated (using a very straightforward visual) that this fact officially allowed us to supersede goldfish, which achieved a whopping result of 9 seconds [1].
Perhaps luckily for us, this claim lacked solid peer-reviewed research and has since been criticized. The “goldfish” comparison was used more for shock value than for scientific accuracy. The idea that goldfish have a 9-second attention span also originates from hype, not rigorous scientific research. As a matter of fact, goldfish can remember tasks for months and learn spatial routes [2], [3].
Nevertheless, the authors of that research were not that far away from more realistic values. Multiple surveys and reviews indicate that the time we stay focused on a single screen has decreased from approximately 2.5 minutes in 2004 to just about 47 seconds now. The reasons include stress, anxiety, sleep issues, and constant notifications, as well as multitasking or constantly checking your mobile phone for new messages [4].
People are not only able to concentrate for just a few minutes, but they also tend to forget what they have heard, sometimes even instantly. We often forget birthdays and names; we leave a meeting without recalling what was said; we share something and then forget; and so on [5].
Lastly, we easily get discouraged. Give me an obstacle, even a tiny one, and I will lose interest and focus. Take the example of Internet pages and e-commerce. A page loading 1 second longer results in a 20% drop in conversions [6]. And, from my own experience, obstacles like the necessity to pick a delivery method that is not optimal can bring it to a complete standstill.
What’s in this post?
Here comes my point: Today, it’s really challenging to capture people’s attention and understanding. The longer we need to do that, the more complicated the information we want to convey, the bigger the risk that we will fail.
In a few of my past articles, I wrote about the concept of data literacy [7] and speaking to people who are not data-literate [8].
Here, I want to highlight a different kind of paradox: speaking to data-literate people who, due to the issues I’ve outlined earlier, often behave as if they were data-illiterate. What does that mean in practice? How do we communicate with such audiences in a way that helps them truly understand, stay engaged, interact meaningfully with the content, and ultimately make informed decisions?
I can immediately say this isn’t easy. I often find myself presenting to people I know are highly competent, well-versed in data, smart, and experienced. I invest time in crafting what I believe is a clear, structured narrative, supported by solid data. And yet, I fail to get through.
Why does this happen? What am I doing wrong—or not doing yet—that could make a difference? What am I planning to change? Let me try to unpack that here.
What do we use to understand data literacy (and should we still use it)?
A few years back, data literacy was understood in a relatively narrow, technical way. The “old” data literacy concept focused mainly on the ability to read, interpret, and manipulate data. It emphasized numeracy, comprehension, and proficiency with basic tools, such as using spreadsheets, charts, or statistical methods. A “data-literate” person, in that context, might have been a business analyst who could pull reports and summarize trends, a student who could interpret a graph in a textbook, a manager tracking sales in Excel, or a policymaker reading census data. Storytelling, interaction, or audience engagement were rarely part of the conversation. It was mostly about technical understanding—not communication, persuasion, or insight.
Over time, however, the concept of data literacy has been reshaped. This happened in large part due to the popularization of data-driven storytelling by authors such as Cole Nussbaumer Knaflic, Brent Dykes, Nancy Duarte, and, to some extent, myself. Today, data literacy is no longer just about reading charts or crunching numbers; it also includes the ability to frame insights effectively, engage diverse audiences, and influence decisions through clear, context-aware narratives.
In this modern view, context is not just important—it’s foundational. It decides if a given story is a success or not. Today, more data doesn’t mean more clarity. That old idea is gone. Now, the focus is on purposeful simplification. It’s about meeting audience expectations and using smart narrative design. The goal isn’t just to show numbers. It’s to guide decision-makers—so they understand and act on what truly matters.
Ultimately, a crucial aspect of modern data literacy is striking a balance between objectivity and persuasion. Being data-driven doesn’t mean overwhelming people with raw facts; it means telling stories that are both truthful and actionable—stories that connect data to decisions in a way people can understand and trust.
Modern data literacy isn’t about knowing formulas — it’s about understanding what questions to ask.
It’s less about math and more about judgment, context, and skepticism. Especially now, when AI can make wrong conclusions look polished and convincing, true data literacy means thinking beyond the dashboard.
Reality of “data literacy”
Scenario: a conversation that falls apart
I walk into the room of my company’s CEO with confidence. I’ve spent hours preparing a clean, data-driven story for her. I took care of context, visualizations, and a clear takeaway. I believe I’ve structured it well: the “why,” the numbers, the recommendation.
I start presenting.
Within a minute, she glances at her phone. Midway through a key insight, she interrupts:
“Wait—why is this number different from what I saw last week?”
I shift gears to explain, but in doing so, I derail the flow of my narrative.
She asks another question, seemingly unrelated to the topic. I answer, but now I’m jumping between slides, losing track of the logic I had so carefully built.
The focus is gone.
She’s confused.
I’m frustrated.
She doesn’t care.
We both leave the meeting unclear on what was decided—if anything.
The trap of modern data literacy
Is it a fake scenario? By all means not. I experienced a very similar situation myself, no longer than a few weeks ago.
And guess what? I genuinely believed I was perfectly prepared. I had solid, verified data, a coherent story, and a clear objective. Everything was structured, logical, and relevant. In my mind, it was bulletproof. But when I presented it, something went off the rails. Despite my preparation, the meeting fell short of expectations. Why?
When data literacy isn’t enough
In today’s high-velocity, distraction-heavy workplace, even highly data-literate professionals increasingly behave as if they’re data-illiterate. This isn’t due to incompetence, but rather the environment in which we all operate. People are bombarded with dashboards, KPIs, alerts, and emails across multiple platforms. It’s constant noise. The result is cognitive overload—our brains can’t process or retain everything, including relevant information.
Furthermore, relentless context switching—from one meeting to the next, from strategy to operations, and from product to finance—shatters any ability to focus or follow a logical data narrative from start to finish.
Even when data is presented clearly and logically, things can still go wrong. Why? Because of one of the most underestimated factors in data communication: context. Misalignment around context is one of the primary reasons good stories fail to land [9].
As presenters, we assume a shared understanding—that our audience knows the definitions, remembers past decisions, or views the business landscape in the same way we do. However, in reality, our audience may approach the problem from a completely different angle: short-term KPIs versus long-term goals, operational pain points versus strategic shifts, or simply a different baseline for comparison. So, when they raise questions or challenge assumptions, it’s not because the data is wrong—it’s because we’re not speaking their language within their context.
This misalignment often breaks the flow of the story and undermines trust. Worse yet, in high-stakes settings, data can be interpreted not as insight but as confrontation. It triggers defensiveness, not dialogue.
The problem is magnified by the tools we now rely on. With the rise of AI-powered platforms like ChatGPT, insights are more accessible than ever. These tools can auto-generate summaries, flag anomalies, and even suggest decisions. But they also make it easy to mistake automation for understanding.
A clean dashboard or a natural-language summary gives people the illusion of clarity. But insight ≠ truth. It is always filtered, modeled, and framed—often by machines, sometimes by people. When we fail to question the assumptions behind these insights or skip the necessary context, we fall into what I call fake data literacy: we feel informed, but we don’t engage critically with the data.
At the same time, business decision-making is becoming increasingly rapid. Speed is rewarded; depth is sidelined. Self-service tools promise empowerment but often mask complexity, encouraging surface-level interaction. Snap judgments replace thoughtful reflection. People are exposed to more data than ever before—but with less time, less context, and more risk of misinterpretation.
The New Data Literacy
In today’s landscape, traditional data skills—such as reading charts, calculating metrics, and building dashboards—are no longer enough. Modern data literacy means being able to frame insights, navigate ambiguity, and translate numbers into decisions. It’s about understanding the narrative, the emotional and political context, and the timing. It’s about knowing how to challenge AI-generated insights, rather than just accepting them.
The new data literacy means:
- Learning context: understanding who the audience is and what matters to them,
- Developing the ability to challenge insights, especially those generated by algorithms,
- Practicing narrative thinking: to guide people, not just inform them,
- Thinking beyond the dashboard: focusing on judgment, relevance, and timing.
How to build stories with data for (il) literates of today?
All of this might sound solid in theory—and it is. But you might rightly ask:
If you say you were so well prepared in the scenario above, what makes you think these strategies will work?
And here’s the honest answer: there’s no guarantee. That’s the beauty—and the frustration—of working with people and data in today’s environment. Everything I’ve written about—the speed, the unpredictability, the fractured attention—creates conditions where things can go off track at any moment. The truth is, the risk of misunderstanding or derailment is always high. And the more people in the room receiving your story, the greater the odds that something will misfire. Those risks don’t just add up—they multiply with every new person in the audience.
Risky or not, I’ve developed a list of practical steps to help maximize chances of success. I’ve divided them into two parts. The first focuses on what can be done before the meeting — preparation tactics that serve as your best line of defense. After all, prevention is always better than a cure. But when things don’t go as planned, the second part offers in-the-moment strategies — a kind of emergency kit to be used during the meeting or immediately afterward to get things back on track.
Modern data literacy: prescriptive measures
Take care of the anchors: Always make sure the audience knows what they’re looking at. Set clear anchors early: what is the scenario, which KPI is under review, and what percentage of revenue or annual target is at risk? Without this context, people can’t judge the importance of what you’re saying. Anchors provide context for numbers and help your audience stay oriented throughout the story.
Ensure consistency across your story: It’s not enough for your data to be technically correct—it also needs to align consistently with what’s been shown before and with the narrative you’re building. If you reference a number in one part of your story and show a different one on the screen—saying, “Oh, that wasn’t updated yet, but imagine it’s right”—you immediately lose your audience’s trust and attention. These small inconsistencies can be significant distractions, especially for individuals already struggling to stay focused. Make sure all numbers, visuals, and commentaries are synchronized and up to date, so your story feels coherent, credible, and deliberate.
State goals, key messages, and conclusions: In a world full of noise, ambiguity is your enemy. Make it unmistakably clear why you’re speaking, what the audience should take away, and what action is expected. Don’t bury your goal in slides or hope they “get it” by the end. Say it up front: “We’re here to decide whether to invest in X.” Reiterate key messages as you go, and land clearly on your conclusion. For attention-fatigued audiences, clarity isn’t a bonus—it’s a lifeline. When your purpose is sharp, your story has direction, and your audience knows how to engage.
Be clear about the point: Say exactly why you’re there and what you want to achieve. For example: “We’re here to decide on X.” State your main message early and clearly, and come back to it throughout. Don’t assume people will pick it up from context—make it obvious. End with a clear, actionable conclusion. If people don’t understand the goal, they won’t follow the story, and they definitely won’t act on it.
Cut off the suspense: Don’t build up to your point—lead with it. Attention is limited, and audiences today don’t have the patience for slow reveals. State the key message or insight immediately, then provide the supporting data. If you wait too long, you risk losing people before you get there. Make your story easy to enter, fast to follow, and quick to grasp.
Ensure a proper flow: Build a clear and coherent narrative. Cut the backstory down to only what the audience truly needs to understand the point. Lead with the core message, and structure your content so it flows logically from insight to action. Eliminate distractions and side tracks—they dilute your message.
Validate, crosscheck, practice: Before you present, stress-test your story. Validate your data, double-check key numbers, and make sure everything aligns—from your summary to your charts. Crosscheck for consistency: is your language clear, are your visuals accurate, and do they all support the same message? Then, practice. A dry run helps uncover weak spots, confusing transitions, or moments where your audience might get lost. The more you rehearse, the more confident and focused you’ll be when it counts.
And lastly, be a storytelling Yoda: Clarity, structure, and calm guidance—these are your tools. Speak wisely, frame your thoughts carefully, and help others see what they need to see. Don’t overwhelm with data dumps or convoluted logic. Instead, guide your audience through the story with intention and empathy. Focus not on showing how much you know, but on helping them understand what matters.
Modern data literacy: if things don’t go to plan…
Okay. Now that you have done your homework, you step into the meeting room, and guess what? You get out in 20 minutes with the same result as before.
Here is what you can do during the meeting, and after it, so that you either further reduce the risk, or minimize losses if the bad scenario eventually materializes.
During the Meeting
- Remember that you still are a Storytelling Yoda. Above all, don’t panic. Remain focused on your goal, keep your composure, and don’t let the pressure shake your confidence. Calm must you stay, my apprentice…
- Re-anchor frequently: Start with your anchors—but don’t stop there. Throughout the meeting, remind the audience of the scenario, the KPI at stake, and the business impact (e.g., “This puts 12% of our Q3 revenue at risk”). Repeating anchors help maintain orientation and reinforce relevance.
- Restate the goal when necessary: If the conversation starts to stray, refocus it on the original goal. A simple phrase, such as “Just to refocus us—we’re here to decide on X,” can reset attention and clarify next steps.
- Watch for signals of confusion: look for cues such as silence, unrelated questions, or jumping ahead. These are signs people are lost or disengaged. Pause, rewind to the key point, and clarify. Don’t power through confusion—address it openly and calmly.
- Use signposting language. This helps focus minds, especially when attention is slipping:
- “Here’s the key point…”
- “This is where we make the call…”
- “Now, let’s connect that to the KPI.
- Summarize Often. Every 5–7 minutes, give a short recap. This supports retention and decision-making:
- Why it matters
- What decision or feedback is needed
- Ensure note-taking. Ensure that someone is taking notes, capturing key conclusions and takeaways, and presenting them for final alignment. Eventually, you can use an AI script generator (e.g., available in the Zoom app if the meeting is held online), but these are not always accurate yet, so I would not rely solely on them.
- Steer the wave: Hyper-attentive people with distractions all around them tend to drift off-topic easily—and the more senior or important they are, the more likely it is to happen. What personally annoys me (if I may share) is that when I get sidetracked, they stop me and apologize to the audience on my behalf. However, when they derail the discussion, it’s somehow completely acceptable. Small frustration—thanks for letting me vent… And apologies for straying from the main point… 😊
Anyway, what can you do in such a situation?
Stay calm and steer the conversation back without calling anyone out. Use gentle framing like, “That’s a great point, and I think we can link it back to…” or “Let me quickly tie that to the main KPI we’re discussing…” Your job is to ride the wave, not resist it—guide the energy back to the core message, reinforce your anchors, and protect the narrative flow without making it personal.
After the Meeting
Send a follow-up summary. Include:
- The goal of the meeting,
- Key data points and anchors
- Main conclusion or open questions,
- Next steps or decisions made.
Even if the meeting went sideways, a crisp follow-up can reframe the story and recover clarity.
Clarify misunderstandings promptly: If something was misinterpreted or challenged, follow up directly. Say, “Let me clarify the data we discussed—I’ve cross-checked it, and here’s the exact scenario.” Closing the loop quickly restores trust.
Document what didn’t land. Use this insight to revise your materials or story for the next time. Take note of:
- Where people got confused
- What distracted them
- What questions disrupted the flow
Book a short debrief (if needed): If the decision didn’t happen or felt unresolved, propose a brief follow-up session with a focused agenda: “I’d like 15 minutes to close the loop on our discussion. I’ve tightened the key points for quicker alignment.”
Reflect and adjust. Ask yourself:
- Did I lead with the conclusion?
- Were my anchors clear and repeated?
- Did the audience have what they needed to act?
Each meeting is a test—and a chance to sharpen your delivery for next time.
Technology is to help
… but we need to keep it a bit old school.
As I was writing all this, one thing struck me: today, we rely heavily on technology—especially LLMs and AI agents. And that’s largely a good thing. These tools boost our productivity, help us scale, and simplify our lives in countless ways. But no matter how advanced they become, they can’t replace the depth of human interaction—real contact, genuine emotion, or the tension that emerges in the moment. Great preparation, perfect visuals, and even a flawless story won’t land if we forget the “human” part of communication. We need to blend timeless skills—such as diligence, accuracy, empathy, and emotional awareness—with modern tools that help us analyze and present data effectively.
That doesn’t mean abandoning these modern tools. But it does mean not relying on them entirely. Think of it like going to a big concert. Have you been to one recently? A major band, a packed venue, the energy buzzing through the crowd?
Then you’ve probably noticed how many people experience it… through their phone screens.
Personally, I don’t understand it. I prefer to experience the concert in the moment—to soak in the music, share the energy with others, maybe even jump around (okay, perhaps not me), take in the sights, the sounds, the smells—everything. Watching it later on a phone screen doesn’t come close. Maybe 1% of the real experience, and even that comes at the cost of missing the moment because I was too busy recording it.
Now, let’s compare that to how concerts felt not so long ago…
See? Energetic music that gets the huge crowd dancing and jumping. Musicians use modern instruments and look futuristic. Like those state-of-the-art apps and tools we use. And now ask yourself—which version truly carries you away? The choice is yours.
Conclusions
Data literacy today is no longer just about interpreting graphs or building dashboards; it is also about understanding the underlying concepts and principles. It’s about navigating an environment overloaded with data, distractions, and decision pressure—where even smart, experienced professionals can behave as if they’re data-illiterate. The new data literacy is deeply human, focusing on context, clarity, empathy, and judgment. It means knowing what matters to whom, guiding attention, and turning information into action. While there’s no guaranteed formula to make every data story land, we can raise our odds by simplifying our messages, reinforcing meaning, and anticipating distractions. And when things go sideways—as they often will—we can adapt, recover, and learn. Ultimately, the ability to connect people with insight defines real data literacy today.
References
[1] Are we no better than goldfish?, Jules M Epstein
[2] Memory like a goldfish? Why this could be a good thing
[3] Busting The Social Media Ruined Our Average Attention Span Goldfish Myth, Michael Brenner
[4] Easily distracted? How to improve your attention span, Devi Shastri, Laura Barggeld
[5] My own experience 🙂
[6] How website performance affects conversion rates
[7] The might of data literacy, Michal Szudejko
[8] How to talk about data and analysis to non-data people, Michal Szudejko
[9] Power of context in data-driven storytelling, Michal Szudejko
Disclaimer
This post was written using Microsoft Word, and the spelling and grammar were checked with Grammarly. I reviewed and adjusted any modifications to ensure that my intended message was accurately reflected. All other uses of AI (image and sample data generation) were disclosed directly in the text.