How to present your analysis results will vary by the audience, the time available, and the level of detail required to tell a story about the data. Your data may have an inherent bias, be incomplete, or require more attributes in order to create a complete picture, so don't be afraid to include this information in your analysis. For example, if you have done some research on climate change, which is a very broad topic, presenting the consumers of your analysis with a narrow scope of assumptions specific to your dataset is important. How and where you include this information is not as important as ensuring it is available for peer review.
Storytelling
Storytelling with data requires some practice and you need time to sell your message to the audience. Like any good story, presenting the data results in a cadence with a beginning, middle, and end will help with the flow of the analysis being consumed. I also find using analogies to compare...