If your data is failing to persuade executives, maybe it's not the data that is the problem. Here's how to change your approach to fit the audience.
One of the biggest challenges data analysts and data scientists face is educating executives about analytics. The general tendency is to nerd out on data and fail to tell a story in a meaningful way to the target audience.
Sometimes data analytics professionals get so wrapped up in the details of what they do that they forget not everyone has the same background or understanding. As a result, they may use technical terms, acronyms, or jargon and then wonder why no one "got" their presentations or what they were saying.
They didn't anything wrong, per se, it's how they're saying it and to whom.
If you find yourself in such a situation, following are a few simple things you can do to facilitate better understanding.
Discover What Matters
What matters most to your audience? Is it a competitive issue? ROI? Building your presence in a target market? Pay attention to the clues they give you and don't be afraid to ask about their priorities. Those will clue you in to how you should teach them about analytics within the context of what they do and what they want to achieve.
Understand Your Audience
Some executives are extremely data-savvy, but the majority aren't just yet. Dialogs between executives and data analysts or data scientists can be uncomfortable and even frustrating when the parties speak different languages. Consider asking what your target audience would like to learn about and why. That will help you choose the content you need to cover and the best format for presenting that content.
For example, if the C-suite wants to know how the company can use analytics for competitive advantage, then consider a presentation. If one of them wants to understand how to use a certain dashboard, that's a completely different conversation and one that's probably best tackled with some 1:1 hands-on training.
Set Realistic Expectations
Each individual has a unique view of the world. Someone who isn't a data analyst or a data scientist probably doesn't understand what that role actually does, so they make up their own story which becomes their reality. Their reality probably involves some unrealistic expectations about what data-oriented roles can do or accomplish or what analytics can accomplish generally.
One of the best ways to deal with unrealistic expectations is to acknowledge them and then explain what is realistic and why. For example, a charming and accomplished data scientist I know would be inclined to say, "You'd think we could accomplish that in a week, right? Here's why it actually takes three weeks."
Tell a Story
Stories can differ greatly, but the one thing good presentations have in common is a beginning, a middle, and an end. One of the mistakes I see brilliant people making is focusing solely on the body of a presentation, immediately going down some technical rabbit hole that's fascinating for people who understand it and confusing for others.
A good beginning gets everyone on the same page about what the presentation is about, why the topic of discussion is important, and what you're going to discuss. The middle should explain the meat of the story in a logical way that flows from beginning to end. The end should briefly recap the highlights and help bring your audience to same conclusion you're stating in your presentation.
Consider Using Options
If the executive(s) you're presenting to hold the keys to an outcome you desire, consider giving them options from which to choose. Doing that empowers them as the decision-makers they are. Usually, that approach also helps facilitate a discussion about tradeoffs. The more dialog you have, the better you'll understand each other.
Another related tip is make sure your options are within the realm of the reasonable. In a recent scenario, a data analyst wanted to add two people to her team. Her A, B, and C options were A) if we do nothing, then you can expect the same results, B) if we hire these two roles we'll be able to do X and Y, which we couldn't do before, and C) if we hire 5 people we'll be able to do even more stuff, but it will cost this much. She came prepared to discuss the roles, the interplay with the existing team and where she got her salary figures. If they asked what adding 1, 3, or 4 people looked like, she was prepared to answer that too.
Plain English is always a wise guide. Choose simple words and concepts, keeping in mind how the meaning of a single word can differ. For example, if you say, "These two variables have higher affinity," someone may not understand what you mean by variables or affinity.
Also endeavor to simplify what you say, using concise language. For example, "The analytics of the marketing department has at one time or another tended overlook the metrics of the customer service department" can be consolidated into, "Our marketing analytics sometimes overlooks customer service metrics."
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