Have you ever reached for a Kleenex when you needed a tissue? Or asked for a Band-Aid when what you really needed was any adhesive bandage? We're all guilty of using these brand names as generic terms. One of my favorite examples is Velcro – a fascinating case where a brand name derived from "velvet" and "crochet" became the universal term for hook-and-loop fasteners. The company even produced a humorous music video featuring "lawyers" pleading with consumers to stop using their trademark generically.
This phenomenon of genericization – where brand names become the common terms for products – offers a compelling parallel to the world of data visualization. Just as we default to saying "Velcro" instead of "hook-and-loop fastener," we often fall into using standard visualization techniques without considering whether they're truly the best fit for our data story.
Our brains are wired for efficiency, Neuroscience shows that we default to familiar patterns to conserve cognitive energy. This explains why we say "Velcro" instead of "hook-and-loop fastener," and why we instinctively reach for bar charts without considering alternatives. Forrester's 2024 predictions confirm that organizations using AI-enhanced visualization tools achieve 25-40% faster operational decisions, with layered dashboards reducing time-to-insight from weeks to days in complex analytics scenarios.
As someone who's spent 35 years wrestling with data, let me tell you: this tendency toward genericization in data visualization can be both a blessing and a curse. Let's explore how we can build a "SLAB" of understanding that balances standardization with innovation. To that end, here are four pillars of effective data visualization with keywords that spell the word SLAB.
The first pillar of effective data visualization is Storytelling. Just as Velcro's success story began with a simple observation of burrs sticking to clothing, every dataset has a unique story waiting to be told. The key is finding the right structure to tell that story effectively.
Consider how Google's "Year in Search" initiative expertly weaves facts into engaging stories that reflect the spirit of our times. Or see how The New York Times' "The Upshot" transforms complex political and economic data into engaging visual narratives that readers can readily understand. These instances show that excellent data storytelling is not so much about presenting numbers as it is about creating a narrative flow that guides viewers through the insights.
MIT studies confirm the brain can process images seen for as little as 13 milliseconds; hundreds of times faster than it can process text. This is why Spotify's "Wrapped" campaign outperforms regular charts by 185%.
When faced with a new dataset, ask yourself this question first: "What's the story these numbers are trying to tell?" For example, rather than automatically going to a standard line graph for time-series data, ask whether a narrative visualization that emphasizes the most relevant events and turning points would be more useful to your audience. Create a storyboard before deciding upon your visualization type. Learn from Spotify's "Wrapped" campaign that translates individualized listening data into a compelling year-end story that users eagerly anticipate and share. Here’s a potential implementation framework:
1. Diagnostic Phase: Audit existing visualization practices
2. Pattern Recognition: Identify overused generic formats
3. Experimentation: Allocate 20% of dashboard real estate for innovative approaches
The second pillar focuses on Layering. Think about how Velcro works: two distinct, disparate layers coming together to create something functional. Similarly, effective data visualization often requires multiple layers of information working in harmony.
Modern data visualization tools have revolutionized how we can layer information. Consider the sophisticated layering techniques used in urban planning, where multiple datasets are overlaid on a single map to reveal complex relationships between population density and traffic patterns. The Medallion Architecture in data lakes demonstrates how structured layering can transform raw data into actionable insights.
Power BI users have particularly benefited from strategic data layering, generating reports that increasingly reveal multiple aspects of information. This makes it easier for users to start with a big picture before zooming in on those nitty-gritty details they are interested in most.
While 65% of enterprises now use interactive dashboards as their primary analytics interface (McKinsey 2023), Gartner finds only 31% implement UX best practices like the Three-Click Rule, which ensures users access key data within three interactions. This gap creates a “Velcro effect,” where users stick to surface-level insights rather than uncovering deeper value.
Make your visualizations progressive disclosure-oriented. That is, start with a high-level summary that grabs attention, and then offer users a chance to drill down to more detailed levels of granularity. A sales dashboard might begin with the top-line figures for revenue and then allow drill-down to breakdowns by regions, categories, and so on, to levels of individual transaction detail. Experiment with pyramid charts to display hierarchical relationships or stacked area charts to reveal how contributing elements add up to a whole.
The third pillar emphasizes Adaptability. Just as Velcro has been used in applications far beyond its original intended use, our visualization techniques must be flexible enough to accommodate different contexts and audiences.
New visualization tools today incorporate AI-based flexibility, which automatically adjusts to market trends and user feedback. This technology allows dynamic content adaptation, making visualizations relevant and accessible on different devices and contexts.
Time-to-Insight (target: 40% reduction)
User Customization Rate (goal: >60% adoption by power users)
Cross-Device Consistency (target: 95% rendering accuracy)
The key to flexibility is understanding that distinct audiences require varying levels of detail and interactivity. Classroom settings have been particularly successful with adaptive systems that are able to adjust according to the needs of the students, delivering customized experiences that result in better comprehension and engagement.
Design multiple versions of your visualizations for different audiences. An executive summary might require a simplified, high-level view, while analysts might need interactive features for deeper exploration. Create templates that can be easily modified based on user feedback and changing needs. Consider implementing responsive design principles to ensure your visualizations work effectively across all devices and screen sizes.
The final pillar encourages Breaking beyond basic visualization techniques. While standard charts have their place (just as Velcro has its proper applications), innovation often requires thinking outside the conventional toolbox.
Some breakthrough approaches have challenged traditional wisdom with remarkable success. Consider the case of inverted y-axis visualizations that can create powerful metaphorical representations, or the transformation of mundane academic timetables into engaging circular formats. These examples show how breaking conventions can lead to visualizations that are more impactful and memorable.
The challenge: finding the right balance between innovation and intelligibility. Although you should by all means avoid "chart junk," using secondary visual elements creatively can enhance engagement without sacrificing understanding. Interactive and adaptive visualizations have been particularly effective in this capacity, allowing users to explore data in their own manner while maintaining the integrity of the data itself.
For each project, challenge yourself to create at least one alternative visualization that breaks from convention. If you typically use pie charts for market share analysis, experiment with treemaps or sunburst diagrams. The goal isn't to be different for difference's sake, but to find more effective ways to communicate your data's story. Consider how visual narratives can guide viewers through complex datasets, making them more accessible and memorable.
Practical Exercise:
1.Sketch three alternative representations
2.Identify one insight each new format reveals
3.Test with one non-technical colleague for comprehension
Just as Velcro's creators probably never imagined their invention would become a household name, we can't always predict which visualization techniques will become standard practice. The key is to build a solid foundation – a SLAB, if you will – that supports both standardization and innovation.
But foundations evolve and sometimes you must adjust your strategy. Perhaps your situation might warrant a “SLAB 2.0” framework; that is, one that might be considered more strategic in nature:
Strategic: Align visualizations with organizational goals, not just data stories
Layered: Implement true progressive disclosure, not just drill-downs
Adaptive: Build for multiple consumption patterns, not just device types
Bold: Allocate 15% of visualization efforts to experimentation, not just best practices
This evolution mirrors how Velcro expanded from shoe fasteners to aerospace applications: by staying true to its core function while adapting to new contexts.
Remember that while genericization of consumer products is a trademark lawyer's worst enemy, in data visualization, it's a question of finding the correct balance. We need standard practices to deliver clarity and consistency, but we must be sufficiently adaptable to innovate when necessary.
The best way to break the “Velcro effect” (for lack of a better term) is through deliberate practice. Here’s one way you might operationalize SLAB 2.0 in your workflow:
Week 1: Replace one generic chart with a narrative visualization
Week 2: Add drill-down to an existing dashboard
Week 3: Create a mobile-optimized version of a key report
Week 4: Pilot one innovative chart type
While you work your way through these challenges, track your progress with these metrics:
✦ Stakeholder feedback scores (target: 20% improvement)
✦ Time spent explaining visualizations (target: 30% reduction)
✦ Novel insights generated (target: 1 per week)
Don't let your visualizations become the Velcro of the data world; stuck in one mode of thinking. Instead, use these four pillars (whether they’re from “SLAB” or “SLAB 2.0”) to create visualizations that are both accessible and innovative, familiar yet fresh, standardized but not stagnant.
The next time you reach for that "Velcro" fastener or default to that standard bar chart, pause and ask yourself: Is this really the best solution for my needs? Sometimes it will be; and that's perfectly fine. But other times, thinking beyond the generic might lead to discoveries that transform how we see and understand our data.
After all, every visualization revolution started with someone who dared to think differently about how to tell their data's story. What story will you tell with yours?
Dr. Joe Perez is a powerhouse in the IT and higher education worlds, with 40-plus years’ experience and a wealth of credentials to his name, having been featured on multiple Times Square billboards. As a former Business Intelligence Specialist at NC State University and currently a Senior Systems Specialist/Team Leader at the NC Department of Health & Human Services (and Chief Technology Officer at CogniMind), Perez has consistently stayed at the forefront of innovation and process improvement. With more than 18,000 LinkedIn followers and a worldwide reputation as an award-winning keynote speaker, data viz/analytics expert, talk show co-host, and Amazon best-selling author, Perez is a highly sought-after resource in his field. He speaks at dozens of conferences each year, reaching audiences in over 20 countries and has been inducted into several prestigious Thought Leader communities. When he’s not working, Dr. Joe shares his musical talents and gives back to his community through his involvement in his church’s Spanish and military ministries.
