I once worked with a colleague who would tell me “I like numbers, not people”. This may sound a bit harsh, but really it wasn’t. It was actually an expression of deep discomfort with the task of presenting data analysis.

For many analysts, we are most comfortable in the phase where we get to prepare and examine data as we seek to uncover important insights. So it can be very intimidating when we are asked to present our analysis.

But with a little support and practice, you might actually see the presentation as a great opportunity to ensure the audience understands the key takeaways from your analysis and enhance your overall communication.

So the next time you’re preparing to deliver a data presentation, consider these tips as a way to help support that great data analysis:

Start with context.

When you are in the analyst seat, you often spend hours, days, weeks, or more with the data. You examine the nuances. You eliminate variables as you seek to draw data-driven conclusions.

So after all that hard work, it happens in your presentation…you get excited and jump right into the visuals and insights without giving the audience a chance to catch up to you!

Beginning with context means starting off your data presentation by clearly stating the business case or problem you were examining.

Based on my own experiences, I cannot emphasize how important this is especially when presenting to senior executives or leadership. At a leadership level, it is very likely individuals may be championing many projects.

Opening with a simple statement reminding them of the business case or problem can help orient them to the specifics of the project and help them get quickly aligned with what you are about to present.

Additional context you might want to include here is reminding the audience of the dataset you were working with (timeframe, data sources, etc.) and even highlighting some of the limitations with that data or problems you uncovered in data quality.

By starting with context you can help the audience focus their attention on the key insights of your analysis.

Match your spoken words with the words on the visuals.

Do you know what type of learner you are? Or more importantly, did you know that there are different learning styles?

For me, having a picture or visual can really help me process and retain information. For others, hearing the information read aloud can be key to understanding.

Knowing there are different types of learning can help us avoid falling into the trap of thinking the visual does all the work when presenting data.

Don’t get me wrong, well-developed visuals and layouts are critically important.

In fact, by following the principles covered in the Advanced Dashboard Design course like using descriptive titles, you are actually positioning yourself to support both visual and auditory learners during your data presentation.

By having your spoken words aligned with the descriptive titles on your visuals, you can help meet your audience at their own individual learning style.

While you’ll want to steer clear of directly reading the titles word-for-word to the audience, if your spoken words can mimic what the audience sees, it can help them follow along with your presentation and help them process and retain your message.

Replace data jargon with plain language.

Avoiding jargon is a pretty standard piece of advice when preparing presentations, but when it comes to presenting data in particular it’s useful to be extra careful about data-related jargon.

Often when we are presenting our analysis it is to a diverse audience with a wide range of experience or interest in data, so it’s important to talk in terms that level the field for everyone.

Here are just a few examples of terms we might be comfortable using in data analysis but can feel like jargon to an audience, and some ways you might replace them with plain language:

Data Jargon: The median is 100. Plain language: 100 is the center point. Half the group is above and the other half is below.

Data Jargon: We ran the data through the algorithm to classify. Plain Language: We grouped the data into themes based on new criteria.

Data Jargon: The data is highly correlated. Plain Language: These two factors seem to have a strong relationship.

By taking the time to understand and accommodate the knowledge gaps between you and your audience, you make a lasting impression of your own professionalism and allow your audience to focus on what matters: your analysis.

Wrapping Up

Honing your data presentation skills can become a powerful addition to your skills as an analyst.

Remember that not everyone is perfect from the start; but by following these tips and getting in a little practice before your presentation, you can really shine. Record yourself practicing or ask a friend for help, and look for these three things.

You’ll feel your comfort and confidence grow in no time.

Be passionate. Seek mastery. Learn with humility.

-Stacy