As Simple as Possible: Einstein's Wisdom for Data Storytelling
Note: I wrote this post using a custom AI writing assistant I created to gather, organize, summarize, and draft my personal ideas, stories, insights, and perspectives.
A simple depiction of a deceptively complex idea
I feature a handcrafted piece of art in my office, made during a family visit to a paint-it-yourself studio. A simple, circular, black and white design of Einstein’s iconic silhouette and his famous quote:
“Everything should be made as simple as possible, but no simpler.”
This piece isn’t just decorative though. It constantly reminds me of a personal guiding principle for life, technology, and data analytics.
The quote seems straightforward at first, but carries a lot of nuance. Simplicity increases clarity, speeds up decision-making, and makes insights more accessible for others. But if you go too far, oversimplification erases critical context, weakens understanding, causes confusion, and erodes trust. This is particularly relevant in data analytics because the need to simplify the data story often clashes with the complexity of the work.
The Simplicity Dilemma
The challenge of "as simple as possible" is knowing where to stop. I learned this through experience—sometimes the hard way. Early on, I’d strip a data report down to bare minimum topline metrics, only to receive a barrage of clarifying questions: "What time period is this? What assumptions did you use? What about this other data point?" I had removed or forgotten critical "pillars" from the data story, weakening the foundation.
On the flip side, I’ve also overwhelmed stakeholders with unnecessary details. Long-winded explanations about how I sourced the data and / or how I conducted the analysis. The result? Stakeholders disengaged, missing key insights or actions because they got lost or hung up on the minutiae. Striking the balance between too much and too little is a blend of creativity and precision.
Connecting to Data Minimalism
Einstein’s principle aligns beautifully with the philosophy of data minimalism—retaining only what is essential to tell a clear, actionable story. Simplicity isn’t just about subtraction; it’s about identifying what’s irreducibly complex—the parts of your analysis or report that cannot be removed without losing understanding or utility. Questions like "Does this piece of information or data add value?" helps guide decisions on what to keep and what to cut.
For example, when preparing a data story, ask yourself:
Does this metric or insight stand on its own? Does it still make sense if I remove other parts of the story?
Have I included enough context to make key insights and recommendations meaningful and actionable?
Could a stakeholder trust this recommendation without needing additional clarification?
It’s like testing the stability of a three-legged stool. Remove one leg, and the whole structure collapses. Add unnecessary pieces, and the stool becomes unwieldy, inefficient, and possibly unusable.
Avoiding Common Pitfalls
Analysts commonly make a big mistake when they "bury the lead." Like a journalist, an analyst’s first priority should be delivering the "what" and the "what next." But it’s tempting to get caught up in explaining how you conducted the analysis—especially if you’re proud of your hard work and methods. Most stakeholders don’t want to wade through every step of your process however. They want to know what matters, why it matters, and what to do next.
Adapting your story to your audience is also critical. Some stakeholders trust your analysis at face value and need only topline insights. Others, especially those newer to working with you or skeptical of your data, might require deeper context to build trust. Structuring your work with layers (beginning with key insights and action items, followed by supplemental deep dives) ensures everyone gets what they need to build comfort and confidence in your analysis and recommendation.
Striking the Optimal Simplicity Balance
If you want to apply Einstein’s principle to your own analysis, here’s a quick framework to assess:
Start Big, Then Subtract: Begin with the full story, then reduce details incrementally. Stop when further subtraction risks losing meaning or clarity.
Ask "Does This Add Value?": For each metric, chart, or detail, ask if it enhances understanding or drives action. If not, cut it.
Test the Stability: Treat your analysis like a three-legged stool. Remove one "leg" at a time to see if the story still stands. If it collapses, the detail is essential.
Layer Your Story: Structure reports and presentations so that key insights come first, with optional deeper layers for those who need them.
Know Your Audience: Tailor the depth of your analysis to the level of trust and familiarity your stakeholders have with you and your work.
The value of simplicity is as relevant today as when Einstein highlighted it in his famous quote. In a world full of data, simplicity isn’t just a preference; it’s a necessity. Balancing between simplification and irreducible complexity, you'll communicate your data story more effectively and drive better decisions.
And if you ever find yourself wondering if you’ve gone too far or not far enough, remember Einstein’s words and, perhaps, these as well:
“Simplicity enhances understanding...until it doesn’t.”
