How do you present data science results to decision-makers?

1 Know your audience

Before you prepare your presentation, you need to understand who you are talking to and what they care about. Different decision-makers may have different goals, expectations, backgrounds, and preferences. You should tailor your message and format to suit your audience's needs and interests. For example, you may need to use more or less technical language, focus on different aspects of the problem or solution, or provide more or less detail depending on who you are addressing.

2 Define the problem and the objective

Your presentation should start with a clear and concise statement of the problem you are trying to solve and the objective you are trying to achieve. This will help you establish the context and relevance of your data science project and set the stage for your results. You should also explain how you approached the problem, what data sources and methods you used, and what assumptions and limitations you faced. This will help you demonstrate your credibility and transparency.

3 Highlight the key findings and recommendations

Your presentation should focus on the most important and actionable findings and recommendations from your data science project. You should avoid overwhelming your audience with too much information or technical details that are not relevant to the decision-making process. Instead, you should use clear and simple language, visual aids, and storytelling techniques to convey your main points and persuade your audience. You should also anticipate and address any potential questions or objections that your audience may have.

4 Show the impact and value

Your presentation should not only show what you found, but also why it matters. You should quantify and communicate the impact and value of your data science project for your audience and their stakeholders. You should show how your findings and recommendations can help them achieve their goals, improve their performance, or solve their problems. You should also provide evidence and examples to support your claims and show the benefits and risks of different scenarios or alternatives.

5 Invite feedback and discussion

Your presentation should not be a one-way communication, but a dialogue with your audience. You should invite feedback and discussion from your decision-makers and listen to their opinions, concerns, and suggestions. You should acknowledge their expertise and perspective and show respect and appreciation for their input. You should also be open to learning from them and adapting your analysis or recommendations based on new information or insights.

6 Follow up and follow through

Your presentation should not be the end of your data science project, but the beginning of a collaboration with your decision-makers. You should follow up and follow through with your audience after your presentation and keep them updated on the progress and outcomes of your project. You should also seek feedback on your presentation skills and style and learn from your experience. You should aim to build trust and rapport with your decision-makers and establish yourself as a valuable partner and advisor.

7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?