How (Not) to Screw up an Analytic Presentation?

In the data world, communication is crucial.

Have you ever sat in a forum where you presented results of an analysis to stakeholders/clients, and everyone doubted you? Have you presented a genius data model that you had been working on for several months, and didn’t understand why you were getting hollow stares at the end?

Because we have all experienced this feeling at least once, I would like to argue that you should invest energy and thought in preparing a presentation — at least the enthusiasm you put into the analysis itself.

Let me show you several ways how not to screw up an analytic presentation — so you’ll be able to pass this phase in flying colors and be recognized for the great value that your work is bringing.

Giving it all (or: structure as the first step)

During the analysis phase, you have a long process of cleaning the data, arranging it, characterizing it, building a model based on it, and creating several tests. So why wouldn’t you share this long process with the client?

Well, you can… but you shouldn’t.

The client is not looking to hear about your journey (or that great chart you created in plotly) — they are there for the bottom line. So in every analysis you present, you should spend time thinking about what the appropriate structure is for the story coming out of the data.

1. Don’t start with more questions: It is important to remember that our internal/external customers are coming to us to get answers — not additional question marks. That’s why you should start your flow with clear conclusions and recommendations.
2. Have a repetitive template: In most cases, start with the business question and the upshots (two slides max), then review your methodology and continue to the analysis itself. Only then, present the results that lead you to the conclusions you presented earlier.
3. Use your appendix wisely:

• You’ve created a slide and are having trouble finding what the stakeholder should take from it? Hide it in the appendix.
• You’d like to talk about a complex technical level, or think that the client might ask you about something specific — prepare an easy to understand section in the appendix. Try to avoid data-analyst-scientist buzzwords without being ready to explain them (yes, I’m talking about all the “Pearson’s chi-squared test” or “Random forest algorithm”).

Show complicated ideas by simple visualization

The simpler the better (or: let’s talk about your content)

The quickest way to confuse the people sitting in front of you is to copy/paste outputs from your data (tables/model/code) to the presentation itself. We already discussed the importance of structure, but how do we not bore people to death? And how do we help them distill the relevant conclusions from the analysis?

1. And… Action: I make sure my output is actionable and shows what are the next steps that my customers can take, or what is the value of my analysis. Is there a new way to order the shelf? Who are the best and worst auditors in each store? Can you spot frauds?
2. Data paradox: Geeks like us loooove data, and we tend to forget that very few people feel this way about numbers too. Here are some tips to communicate your results:

• Not everyone can look at a spreadsheet or table and draw quick and clear conclusions about what the data indicates. Anyone can follow a trend line or the size of bars on a bar chart.
• You can start simple, by describing the trend — “For each unit decrease in [add variable], we expect to see a lift of X% on average”. In this way, you avoid talking about the technical aspects of the analysis and focus on the business value it brings.
• Pick one data point and demonstrate how your story applies to it. At Trax, we collect data from the shelf itself. I tend to use the actual images from the store itself to back up my conclusions from the analysis. A real-life example that brings the discussion down from a highly technical one to an easily digestible and simple to relate to.

Present your argument in a way that will make it actionable (in this case, we showed two different ways of data collection)

Design is where science and art break even (or: how to be clear?)

Once you have submitted the presentation, it is no longer in your possession — and may well get a lot of viewers. How do you ensure that the messages you conveyed orally will also pass to those who only read it? Here are some tips…

1. Be bold: Each headline should represent the slide and give insight.
2. Creative mode: In the presentation — the idea is to convey the message clearly, easily, and quickly. Did it take more than 5 seconds to understand what the slide is about? If so, you’re in the wrong direction. Whenever I have a coherent slide idea but no clue on how to display it, I search for its template in Google. You’ll be amazed by what you’ll find.
3. Don’t force the chart to speak for itself: Make sure that you have labels on your charts and a few lines to describe them. If you’re displaying data in a chart, only show one graph at a time. Be sure to explain what it shows and what it means in the broader context of the problem you’re addressing. Lastly, don’t be creative and overuse colors — green is good, red is bad, and blue helps to emphasize things.

Example of templates for a presentation you can find on the internet

To conclude, to (not) screw up your analytics presentation, there are three parts that you should focus on: structure, content, and design. We have reviewed some notable tips that have helped me in each of those areas, but obviously, we only reviewed the tip of the iceberg.

I’m sure you have quite a few extra in mind…is it a mid-font change? Or maybe a spelling mistake?

Share what disturbed you with others, and what you learned from your previous presentations, so we’ll be able to ‘make our presentations great again!’

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