How to Teach Executives About Analytics

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."

(Image: Ditty_about_summer/Shutterstock)

(Image: Ditty_about_summer/Shutterstock)

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.

Speak Plainly

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."

Had a Success or Mismatch?

If you've been educating executives and have a helpful or funny story, please share it with us in the comments section.

Lisa Morgan, Freelance Writer

Lisa Morgan is a freelance writer who covers big data and BI for InformationWeek. She has contributed articles, reports, and other types of content to various publications and sites ranging from SD Times to the Economist Intelligent Unit. Frequent areas of coverage include big data, mobility, enterprise software, the cloud, software development, and emerging cultural issues affecting the C-suite.

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Re: Agreed
  • 10/16/2017 8:25:43 AM

@SethBreedlove, I wish I could believe that but what they thought they were looking was very specific. They mixed up terminology between an in house process and a function in the software that were totally unrelated.  It may have been some of the ancillary data that they were looking at that kept them on the right track but what they were looking at wasn't even distantly related to what they thought they were looking at.

Re: Thanks, I'll try it
  • 10/15/2017 11:14:51 PM

@ k4yqm : Expanding on your thoughts and regarding where the article says "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."   I see this mistake often when I've had quaintances start their own consultanting businesses. 

There websites are so full of technical jargon and they are trying so hard to show how much knowlege they have, that they forget that the person seeking help may not know all that jargon.   The landing page at least should be understandable to someone with out much of a tech background. 

Re: Agreed
  • 10/15/2017 11:03:46 PM

@ SaneIT.  It may have been that they knew that certain number  correlations meant certain things but didn't know why it did.   Or the numbers had an indirect correlations rather than a direct correlation.   For example when I would read credit reports it was easy for me to see where kids started college, divorces happened and so on but the numbers themselves did not mean that. 


Re: Agreed
  • 10/12/2017 8:36:10 AM

I see where you're coming from.  Someone in an entry level analytics position isn't likely to be presenting to the board but if they want to advance they are going to have to learn to present to someone.  Even if they are presenting to department managers to help them manage small things being able to communicate what the data represents is important.  I think I've mentioned a number of times that I do a fair amount of steering people away from spreadsheets or decade old reports that do not reflect the story that they think it does.  In one example someone had been using the same report for 12 years to forecast product usage.  When I asked how they were translating the report into product usage numbers they looked confused.  Somehow, they had totally unrelated data and were able to make decent decisions with it.  I don't know if it was a fluke or that they were so good at what they were doing that the data didn't really matter but convincing them to look at what they were using and how they were using it really opened up some eyes. 

Re: Thanks, I'll try it
  • 10/11/2017 8:58:05 AM

All said in the article being true, it's not always easy though to accomplish those items. Knowing the audience may be especially difficult if an "outsider" is making the presentation. Not only may they speak different languages, i.e, technical vs. task oriented for example, the personality types may be of a large contrast among the participants. Finding just the right mix of techniques for the presentation will be a challenging task that can't be ignored in planning.

Re: Agreed
  • 10/10/2017 9:33:28 AM

I agree that size and rganization structure atter. My point is that a junior data employee is less likely to get through to an executive than a perr would.

Re: Agreed
  • 10/10/2017 8:33:36 AM

@Tomsg, that really depends on the size of the company.  I'm not sure the size of the company would change any of the advice given especially telling a story that is relatable and not just numbers on paper or a big display.  With larger companies some of the details can be missed in the bigger picture so choosing which ones you want to focus on is important.  You can't give everything the same weight and expect everyone to be able to follow along.  Often, I see the most successful presentations being small bites from a specific area that the audience can ask to dig into deeper at a later date.  


Thanks, I'll try it
  • 10/4/2017 2:29:52 PM

Ms. Morgan, Hello!

Thanks for the tips. I will try to apply your ideas in a Skype format presentation in exactly two weeks, and I shall let you know how it went. I recognize these tips as standards (from other training) that seem to be focused on time-effiency and individual-responsibility.

There is one concern I have and that's "knowing your audience." I know my audience, but that's from years' of experience. As a presenter what one assumes about the audience is pure bias. We teach ourselves, and that requires interest before investment. So I think knowing your audience is an albatross.

For team building, and getting research forward, feedback, criticism, communication, etc., we have lacked these regular presentations for too long. We're in research and essentially everyone has gone her separate ways. We use different software, we are at different levels, we have different responsibilies, programmatic, or free-for-all. It will be interesting to see if I can reach half the people I know.


Bureau of Economic Analysis

Washington, DC

  • 10/4/2017 9:35:56 AM

I agree with all of the suggestions you have- the only question is who uses these approaches? I think it is ideally the role of a CIO or CAO. The other executives may listen to a peer. I think the odds of a data scientist getting the ears of the executives in the company is low.