How do you make data analysis and visualization results easy to understand for non-technical audiences?

1 Know your audience

Before you start creating your data analysis and visualization, you need to know who your audience is and what they want to learn from your data. Are they executives, managers, customers, or stakeholders? What are their goals, challenges, and pain points? What level of detail and complexity do they expect and prefer? Knowing your audience will help you tailor your data analysis and visualization to their needs and interests, and avoid overwhelming or confusing them with irrelevant or technical information.

2 Choose the right format

Depending on your audience and your data, you may need to choose different formats to present your data analysis and visualization results. For example, you may use a dashboard, a report, a presentation, or a story to convey your findings. Each format has its own advantages and disadvantages, and you should consider the following factors when choosing one: the purpose, the context, the scope, the interactivity, and the frequency of your data analysis and visualization. For instance, a dashboard is suitable for providing a quick overview of key metrics and trends, while a report is more appropriate for providing a detailed and comprehensive analysis of a specific topic.

3 Use clear and simple language

One of the biggest challenges of making data analysis and visualization easy to understand for non-technical audiences is to use clear and simple language that avoids jargon, acronyms, or technical terms. You should explain your data analysis and visualization results in a way that your audience can relate to and understand, using familiar words, phrases, and examples. You should also define any terms or concepts that may be unfamiliar or ambiguous to your audience, and provide context and background information when necessary. For example, instead of saying "the correlation coefficient between X and Y is 0.8", you could say "X and Y have a strong positive relationship, meaning that when X increases, Y also increases".

4 Use appropriate and effective visualizations

Another important aspect of making data analysis and visualization easy to understand for non-technical audiences is to use appropriate and effective visualizations that highlight the main points and patterns of your data. You should choose the right type of visualization for your data, such as charts, graphs, maps, or tables, and follow the best practices of data visualization design, such as choosing the right colors, scales, labels, legends, and titles. You should also avoid cluttering your visualizations with too many elements, such as data points, axes, or annotations, and focus on the most relevant and meaningful information. For example, instead of showing a pie chart with 20 slices, you could show a bar chart with the top 5 categories and group the rest as "others".

6 Solicit feedback and questions

Finally, one of the best ways to make data analysis and visualization easy to understand for non-technical audiences is to solicit feedback and questions from your audience. You should invite your audience to share their opinions, insights, or concerns about your data analysis and visualization results, and address them in a respectful and constructive way. You should also ask your audience questions to gauge their understanding, interest, and satisfaction with your data analysis and visualization, and use their responses to improve your future work. For example, you could ask your audience what they liked or disliked about your data analysis and visualization, what they learned or found surprising, or what they would like to know more about.