Big data. Analytics. Data science. Businesses are clamoring to use data to get a competitive edge, but all the data in the world won’t help if your stakeholders can’t understand, or if their eyes glaze over as you present your incredibly insightful analysis. This post outlines my top ten tips for presenting data.
It’s worth noting that these tips are tool agnostic-whether you use Data Studio, Domo, Tableau or another data viz tool, the principles are the same. However, don’t assume your vendors are in lock-step with data visualization best practices! Vendor defaults frequently violate key principles of data visualization, so it’s up to the analyst to put these principles in practice.
Are you looking for ways to present data and statistics in a clear, engaging way? Creating compelling data presentations requires strategic visualization, storytelling, and design choices.
As a data analyst and presentation specialist, I’ve helped many clients transform complex datasets into captivating presentations that inform and inspire audiences.
In this comprehensive guide, I’ll share my proven process for developing high-impact data presentations from start to finish. Follow these steps and tips to create visually appealing, meaningful presentations packed with data insights.
Step 1: Collect and Organize Your Data
The foundation of any data presentation is the information itself Follow these best practices when gathering and organizing your data
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Identify your objective – Be clear on what insights you want to communicate about the data. This guides what to include.
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Compile relevant datasets – Only gather data that directly supports your goal. Omit anything superfluous.
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Clean and process the data – Fix any errors, inconsistencies, missing values. Transform into appropriate formats.
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Summarize key points – Distill datasets down to noteworthy trends and takeaways.
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Structure logically – Arrange data to tell a coherent story that builds to your main point.
Taking the time to thoroughly collect and organize your data ensures you have focused, polished content to work with.
Step 2: Choose your Visualization Approach
Next, determine the best visualization method for each dataset based on these factors:
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Data types – Use appropriate charts for time series, geospatial, statistical, hierarchical, network data.
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Story flow – Arrange visuals to take viewers on a logical journey. Group related data.
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Audience appeal – Consider visual preferences of target viewers – simplicity vs. novelty.
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Consistency – Use the same chart types consistently for similar data.
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Clarity – Select charts that most clearly represent the data without excessive decoration.
With mindful visual choices, your data will be understandable, meaningful, and memorable for audiences.
Step 3: Design Visually Appealing Slides
When designing your presentation slides, keep these design principles in mind:
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Clean layout – Allow plenty of negative space around charts and text. Avoid clutter.
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Legible typography – Use simple, readable fonts at a size that’s visible from the back of the room.
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Strategic annotations – Include labels, notes, callouts to highlight key data points.
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Contrast and alignment – Use colors, positioning, and whitespace to distinguish elements.
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Consistent branding – Maintain a cohesive style with fonts, colors, etc.
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Focus and flow – Arrange visual hierarchy and transitions to guide the viewer’s eye.
With strategic design choices, you can transform raw data into stunning and meaningful slides.
Step 4: Craft your Data Story
Tying your presentation together with a compelling storyline takes thoughtful messaging:
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Establish objective – Start by framing the purpose, context, and importance of the data.
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Set up insights – Provide necessary background for audiences to understand the coming data points.
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Reveal data sequentially – Methodically walk through data-based findings in a logical order.
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Summarize takeaways – Distill key conclusions at natural stopping points.
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Connect to impact – Relate data insights back to audience goals, challenges, and interests.
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Close with a memorable line – End with a concise, meaningful summary statement.
Skillful storytelling transforms dry data into an engaging journey filled with meaningful aha moments for audiences.
Step 5: Practice your Delivery
To deliver the presentation effectively, rehearse these performance skills:
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Vocal variety and projection – Speak clearly and vary tone for emphasis.
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Eye contact – Frequently look up from slides to connect with the audience.
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Confident body language – Stand tall, avoid nervous movements.
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Conversational flow – Sound natural, not overly rehearsed. Include well-placed pauses.
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Smooth transitions – Use natural segues between concepts, never abrupt jumps.
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Audience engagement – Invite reactions, questions, insights from viewers.
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Time management – Keep close track of time when rehearsing. Refine to fit timeframe.
With practice, you can deliver your data narrative with energy and poise to maximize impact.
Step 6: Open Strong and Close Strong
Make powerful first and last impressions with your audience by heeding this advice:
Grab attention early – Hook listeners right away with an intriguing opener: surprising stat, story, or question on the core topic.
Summarize key message – In your close, reinforce the most important takeaway you want viewers to remember.
End on a memorable final image – The last visual should represent your key point creatively and impactfully.
Tie back to the opener – Return to your introduction theme or image to bookend the presentation.
Offer to take questions – After closing, invite audience members to come forward with any questions.
With well-crafted opening and closing statements, you can deliver truly lasting data presentations.
Key Takeaways on Developing Data Presentations
To sum up, here are my top tips for creating data-driven presentations:
- Collect focused, organized data tied to your goals
- Strategically visualize data for clarity and appeal
- Design polished, branded slides to spotlight your data
- Craft a compelling story to reveal insights
- Practice smooth delivery with vocal and visual energy
- Grab attention early and end on a memorable final image
Using this step-by-step process, you can develop beautiful presentations that bring data to life in a clear, meaningful way for audiences.
Recognize That Presentation Matters
The first step to presenting data is to understand that how you present data matters. It’s common for analysts to feel they’re not being heard by stakeholders, or that their analysis or recommendations never generate action. The problem is, if you’re not communicating data clearly for business users, it’s really easy for them to tune out.
Analysts may ask, “But I’m so busy with the actual work of putting together these reports. Why should I take the time to ‘make it pretty’?”
Because it’s not about “making things pretty.” It’s about making your data understandable.
My very first boss in Analytics told me, “As an analyst, you are an information architect.” It’s so true. Our job is to take a mass of information and architect it in such a way that people can easily comprehend it.
Take these two visuals. The infographic style shows Top 10 Salaries at Google. The first one is certainly “prettier.” However, the visual is pretty meaningless, and you have to actually read the information to understand any of it. (That defeats the purpose of a data viz!)
Pretty, but not helpful
On the flip side, the simpler (but far less pretty) visualization makes it very easy to see:
- Which job category pays the most
- Which pays the least
- Which has the greatest range of salaries
- Which roles have similar ranges
It’s not about pretty. When it comes to presenting data clearly, “informative” is more important than “beautiful.”
Just as we optimize our digital experiences, our analyses must be optimized to how people perceive and process information. You can think of this as a three-step process:
- Information passes through the Visual Sensory Register. This is pre-attentive processing-it’s what we process before we’re even aware we’re doing so. Certain things will stand out to us, objects may get unconsciously grouped together.
- From there, information passes to Short Term Memory. This is a limited capacity system, and information not considered “useful” will be discarded. We will only retain 3-9 “chunks” of visual information. However, a “chunk” can be defined differently based on how information is grouped. For example, we might be able to remember 3-9 letters. But, we could also remember 3-9 words, or 3-9 song lyrics! Your goal, therefore, is to present information in such a way that people can easily “chunk” information, to allow greater retention through short-term memory. (For example, a table of data ensures the numbers themselves can’t possibly all be retained, but a chart that shows our conversion rate trending down may be retained as one chunk of information-“trending down.”)
- From short-term memory, information is passed to Long-Term Memory. The goal here is to retain meaningful information-but not the precise details.
Don’t Mix Chart Types for No Reason
I repeat. Don’t mix chart types for no reason. Presenting data sets together should tell a story or reveal insights together, that isn’t possible if left apart. Unfortunately, far too many charts involving cramming multiple data series on them is purely to conserve the space of adding another chart. The problem is, as soon as you put those two series of data together, your end users are going to assume there’s a connection between them (and waste valuable brain power trying to figure out what it is).
Below are good and bad examples of mixing chart types when presenting data. On the first, we have a column and line chart together, because we’re trying to demonstrate that the two metrics trend similarly. Together they are telling a story, that they wouldn’t tell on two separate charts.
The second, however, is an example of “just trying to fit two series onto a chart.”
For the second chart, a better option for presenting the data might be to have two side-by-side bar or column charts.
7 Effective Tips for Presenting Data at Work!
How do I create a data presentation?
The first step to creating a data presentation is to collect the data you want to use in your share. You might have some guidance about what audience members are looking for in your talk. If you’ve received an assignment from a manager or someone at your company, follow the instructions they give you and include all the information they request.
How do you deliver a data presentation?
Storytelling with data is a highly valued skill in the workforce today and translating data and insights for a non-technical audience is rare to see than it is expected. Here’s my five-step routine to make and deliver your data presentation right where it is intended — 1. Understand Your Data & Make It Seen
How to make a good data presentation?
Your audience won’t be able to identify the differences and make an appropriate correlation between the two data sets. Next time, stick to one type of data presentation only. Avoid the temptation of trying various data visualisation methods in one go and make your data as accessible as possible.
What should a data presentation include?
What is a Data Presentation? A data presentation is a slide deck that aims to disclose quantitative information to an audience through the use of visual formats and narrative techniques derived from data analysis, making complex data understandable and actionable.
