In this era of “Big Data,” we have access to more records and connected data than ever before. New programming languages and applications are being developed that promise to unlock many of the world’s challenges, such as showing businesses how to market directly to the most profitable consumer groups or helping healthcare groups eradicate complex genetic diseases.

However, while this increase in records and connected data can solve many challenges, it can also create difficulties when moving from the research stage to the implementation stage. For actuaries, performing an analysis on the ever-evolving risk factors associated with future loss costs can be dizzying. However, presenting the findings of this analysis – and explaining why certain results are more important than others – is also a challenge. In fact, obtaining buy-in for implementation of the recommended path may actually be more critical than the analysis itself. If consumers don’t understand why a path is in their best interest, the analysis becomes meaningless.

You may have heard of “paralysis by analysis,” which occurs when someone feels paralyzed with an overabundance of information, rather than empowered to make decisions. As an actuary, you’ve likely experienced this during a complex analysis – but audiences can also experience it when presented with actuarial findings. The audience’s job is to look for a takeaway. Too little presented data results in a lack of confidence in the analysis; too much, and it becomes more and more difficult to identify the takeaway. Your job as presenter is to make their job easier.

While attending a CAS webinar recently (“Beyond the Numbers: Using Data to Tell Compelling Stories”), I was struck by a quote shared from the book The Laws of Simplicity by John Maeda:

“Simplicity is about subtracting the obvious, and adding the meaningful.”

This phrase has the potential to erase that paralysis and give an audience clarity. Let’s start with the first part – subtracting. While it may sound simple, execution is a bit trickier and requires careful thought and planning. The problem usually stems from a desire to “show our work” and all the value we’ve added to a project by the number of different variables we’ve analyzed. That’s not to say the work we did was not meaningful, or that we shouldn’t have analyzed it – sometimes it’s absolutely necessary to perform analysis to rule a variable out as a material factor. However, all of the necessary work we’ve done to arrive at the result isn’t necessarily meaningful to the audience making a decision; in fact, the extra detail could make their decision more confusing. To make it easier, ask yourself these questions while drafting or editing a presentation:

• Is this absolutely necessary and relevant to the main problem we are trying to solve?
• Can this be explained in a simpler way? A popular subreddit, “Explain Like I’m Five,” takes complex topics and explains them in simpler terms that a child could understand. Now, I’m not advocating to simplify results to the point where it insults your audience or fails to be impactful. Simplicity and complexity are two sides to the same seesaw; we need both, but we need a balance.
• What information does my audience need to make their decisions easier?

Mark Twain famously said, “…use plain, simple language, short words and brief sentences.” When we keep our language plain and simple, we increase accessibility for our audience and reduce the risk of obscuring our call to action.

This leads us to the second half of John Maeda’s quote – add the meaningful. After a message is pared down to only the most important, necessary parts, ask yourself if there is anything to add that hasn’t been considered.

A good strategy for this is thinking like your audience and anticipating questions they might have. Don’t be afraid to challenge yourself; you can even ask a peer to review and challenge you as well. I often bounce ideas off my wife and friends, and then ask them to explain it back to me. If what I hear back isn’t the message I intended, I add the next meaningful detail and try again. Our predictive models take this approach when designing algorithms and selecting variables, so why shouldn’t we do the same when conveying the results? While this is not an easily accomplished feat and takes diligent effort, it becomes easier over time. I still find myself editing, rewriting and creating new graphics, but there are fewer edits each time.

With that, I’ll leave you to go out and try to implement this process into your next presentation or report; writing anything more on the subject would be unnecessary.