The Art of Presentation: How can Data Scientists level up?
Public speaking is the bane of data scientists.
Public speaking is one of the banes of data scientists. You go up, armed with technical details about your project - as multiple stakeholders eyes bore into you.
After the presentation you realize it didn’t have impact. People seemed confused. You told them the details. What went wrong?
You’re not alone in this. It’s a rite of passage on the journey that all data scientists go through. The problem can lie in:
Being used to presenting in a scientific and technical way
Not framing the problem for the business
Lacking confidence to present your findings.
By far, these are the most common I’ve seen when I’ve mentored data scientists. You need to reflect and iterate on how you speak, frame the problem, present solutions, and follow up.
In this article, I’ll help break these down, and level your presentation skills up.
How are you speaking?
How you are speaking is decisive. I’ve found this out many times at presentations, shooting my LinkedIn Learning course, and even networking events. Audience understanding hinges on the experience you make.
I love how Chris Chin frames it. It’s multiple factors than confidence.
How you speak affects the message you get across. It affects the key points that the listeners understand, and how they interpret it. He really hits the right points here. You need confidence, solid arguments, and emotion to really make an impact.
You must build a framework of your points using your confidence, emotion, and argument.
Confidence is Mental. Confidence isn’t just being knowledgeable. It’s also confidence to answer questions, clarify points, and define arguments quickly. But it’s also knowing your lack knowledge and being able to redirect quickly. Some questions need an offline conversation. Some requires research. Confidence is faith that you can respond to unexpected events quickly.
Argument is frameworks. Rehearsing your main points? Can get complex, especially for tech people. There’s always more to say. There’s always points to leave out. Start with a framework. Define the argument’s points, illustrate the connection, tie to the larger picture. Doing this forms a framework in your head - and the audience. You get the best questions and response this way. Make it understandable for you, make it clear for them.
Emotion gives depth. Talking in front of audience is nerve wracking. The difficult part isn’t just the content. It is making the presentation an extension of you. Emotion is the tool to do that. When you inject your passion into your voice, presentation, and tie it with belief? You get a compelling presentation. You’re not communicating an argument. You’re providing an experience, that ties it you and your brand.
Messaging isn’t just the content you communicate. It’s the content you communicate within an experience - one that reflects your brand, team, and organization. When you use these three points, you create memorable ones.
How do you frame the problem?
Framing the problem, helps solve it. Or at least gets you on the way to solve it. Problem solving revolves around definitions, context, and the big picture. Communicate this to your audience. ‘
Framing it narrows the discussion to what is important. It creates a focused and compelling narrative, that sparks audience curiosity, focus, and thought.
Start by identifying the business problem and use case - then tackle the larger picture. Answer that first. Creating a framework is important if you really want points to stick. They give a context and build relationships around the problem, tie them to the use case, and the larger business need. Create a framework to make sure your points resonate. It will provide context for deeper questions and better meetings.
Give three steps leading to the problem to illustrate its origins. What led up to the problem? Illustrating this helps weave a story. Three steps before show the sequence of events or needs that lead up to the problem. It makes it more relatable, grounding tech problem in real-world events and decisions. It builds a mental picture and framework.
Showcase the three actions you've already taken to establish credibility. Credibility is showing that you made an effort. Buy in is being able to show a solution focused delivery cadence. Both are essential in convincing stakeholders of your commitment and capability to resolve the problem effectively.
Start from the larger picture. Drill down. If you’re working with a larger group, you need to hit the larger value statements first. Then drill down as needed.
I’ve found this out many times at presentations, shooting my LinkedIn Learning course, and even networking events. Audience understanding hinges on the experience you make.
Framing the problem and context sets the foundation for talking about the solutions you’re bringing to the table.
How do you present the solution?
Give options, don’t set policy. Remember, you’re the subject matter expert on the model, outcomes, and the data. Provide clear, actionable alternatives, and explain the outcomes.
Focus on strategically relevant data points. Place relevant technical or business datapoints in an appendix to refer to. Important points need to be aggregated into a framework.
Create Solution Paths. Outlining several different strategies to tackle the issue at hand. Each of your solutions must be distinct and addresses the problem from a unique angle. Each of them must take into account different preferences, resources, and timelines. Research your audience to give the best impact paths. Even if they don’t adopt any, they’ll know you’re a resource and partner that understands them.
Explain Pros and Cons: Detail the advantages and disadvantages for options. The pros might include things like cost-effectiveness, ease of implementation, or potential for quick results. On the cons side, factors like resource requirements, possible risks, or longer timelines. You must be ready to explain the impacts and tradeoffs. Stakeholders weigh each option against their priorities and constraints. Give them a lift.
Show How It's Done: Break down the steps involved in implementing each solution, providing a roadmap from start to finish. You need to include initial preparations, key teams, resources, and maybe even risk management needed. Highlighting the practical aspects helps stakeholders understand the level of effort and commitment required. Discussing potential obstacles let them prepare their teams for the challenges ahead.
Framing it narrows the discussion to what is important. It creates a focused and compelling narrative, that sparks audience curiosity, focus, and thought.
Give more than one solution to illustrate your value. Don’t get discouraged if none are adopted - that’s valuable feedback. It helps you tailor your solutions to their constraints.
But even if you got adoption of your options, you still need to follow up.
How do you follow up?
Presenting doesn’t end after the last word leaves your lips. Projects doesn’t end when you ship. Customer relationships (internal or external) don’t end when buy in happens. You have to follow up.
Focus on alignment - there’s often questions or points not raised in the presentation.
Frontload. Hit the ground running with tasks after the presentation. Frontloading shows how quickly you can learn from feedback, apply it, and take action when a decision is made. Get burning requirements questions right away, begin planning next steps. The first week after is a lower risk asking period.
Schedule 1:1 Meetings: Arrange individual meetings with key stakeholders to delve into the specifics of how each solution might impact their area. One-on-one discussions allow for a more personal exchange of ideas and concerns. It helps to manage expectations, find hidden use cases, and deepen understanding. As a data scientist, you’ll often find 1:1s have questions, concerns, or even complements that wouldn’t have been aired in the original presentation.
Hammer Out Details. After you’ve engaged with key stakeholders, start collabs. The best advice I got early in my career was to get everyone in the same room. You’ve set the expectations by this point - its far easier to understand roles, timelines, and alignment. After all, you did talk to them before the alignment meeting. Meetings often go smoother when you do this.
Build Coalitions of Support. Identify and engage influential figures within the organization who can champion the best paths in the presentation. Garnering support from respected leaders and advocates can significantly enhance the project's visibility and acceptance. It creates a broader base of support and increases curiosity around the project. It helps you meet them where they are at.
I’ve found data scientists can struggle with the follow up. Especially after buy in. A great advice my manager gave me was “when successful, tighten your helmet cords”. Even in times of success, you should remain prepared and vigilant.
Follow up. Make sure you create a quick roadmap immediately after the presentation. As data scientists, our workload can pop up quickly, so this can help you prioritize better.
Final Thoughts
We all know creating beautiful visuals and great content are essential to great presentations. They enhance your message. But framing, presenting solutions, and follow up are decisive. It breathes life into your topic. It makes it an experience, and it becomes an extension of you. You’re selling brand AND the presentation.
Presentation isn’t easy - its a process before and after. Data scientists who learn to the how, what, and why of presenting get more buy in. Building our solutions is hard, explaining them and rallying support can be harder.
As you advance farther in your data scientist career, this gets to be more important. Your building and tech skills are the fundamentals. Presentation and how you do it? Bridges the gap between an okay data scientist and a great one.