Data analysis is a valuable skill that can help you understand patterns, trends, and insights from various sources of information. But how can you communicate your findings effectively to support learning for yourself and others? In this article, you will learn some tips and strategies to present and share your data analysis in a clear, concise, and engaging way.
Before you start communicating your data analysis, it is important to consider the background and context of the audience, their goals or problems that your data analysis can help solve, their main questions or concerns, and how they prefer to receive and process information. Different audiences may have different levels of familiarity, interest, and expectations with your data and analysis; for example, a technical team may want to see the details of your methods and assumptions while a non-technical audience may want to see the main conclusions and implications. Therefore, tailoring your communication to your audience is essential.
Instead of focusing on presenting your findings, data analysts need to focus on presenting to their audience. Your audience needs to be the central part of your presentation preparation. Thinking through who they are and what they need to know from your findings will help you frame your presentation.
Communicating results of data analysis depends on: 1. Type of data (numeric or qualitative or mixed) 2. Nature of audience (students, trainees, technical, managerial, c-suite, investors, other stakeholders) 3. Reason of communication (teaching, informing, brainstorming) These 3 factors will essentially decide the nature of data presentation, the level of details and the time duration of presentation. For a lot of technical presentations, I feel graphs, charts in slides and dashboards are great ways of presenting results. Slides can also be used to discuss methods and can help provide pointers for further discussion and recommended actions. Videos, if too long cause people to lose focus in such cases, they are better for creative fields.
When deciding how to communicate your data analysis, you should consider the advantages and disadvantages of different formats, such as reports, dashboards, slides, or videos. Each format has its own benefits and drawbacks, so you should select the one that best meets your needs and resources. For instance, a report may provide more comprehensive and detailed information but can take more time and be less interactive than a dashboard. On the other hand, a slide may be more visual and concise but also more limited in space and depth than a video. When choosing a format, think about factors like how much information you need to convey, how complex or simple your data analysis is, how interactive or static you want your communication to be, and how much time and effort you have to prepare and deliver it.
Choosing the correct visuals can make or break communication with your audience. Make sure the format can easily be understood. Most of the time, as data professionals, we have to make our analysis more user-friendly and understandable for people who aren't at the same level of expertise as us.
When communicating data analysis, it's essential to use language that your audience can comprehend and relate to. Steer clear of jargon, acronyms, or technical terms that may confuse or turn off your audience. Instead, opt for plain and common words that effectively convey your meaning and message. Explain any concepts or terms that may be unfamiliar or ambiguous to your audience. For example, instead of saying "We performed a linear regression to analyze the relationship between X and Y", you could say "We used a statistical method to see how X affects Y". Here are some tips for using clear and simple language: employ active voice and short sentences; use examples, analogies, or stories to illustrate your points; include transitions and signposts to guide your audience through your communication; ask questions, provide feedback, or check for understanding to make sure your audience is following you.
A college professor I had encouraged his students to think of overly complex ideas in terms of how it could be reduced to a bumper sticker. That idea has stuck with me, and I think it also relates to presentations. Clear and simple language promotes inclusivity, and prevents audience members from disengaging when faced with too much jargon or unknown abbreviations.
When communicating data analysis, it is essential to visualize your data in a way that highlights your findings and insights. Visualizing your data can help you and your audience identify patterns, trends, and outliers that may not be obvious from numbers or text. Additionally, it can aid in making comparisons, contrasts, or correlations among different variables or groups. However, not all visualizations are equal, so choose the ones that best fit your data and purpose. For example, a bar chart may be more suitable for displaying categorical or discrete data, while a line chart may be more suitable for showing continuous or time-series data. When visualizing your data, remember to use appropriate and consistent scales, labels, and colors; titles, captions, and annotations to explain your visualizations; charts, graphs, or maps that match your data type and analysis; and simple and clean designs that avoid clutter and distraction.
Finally, one of the most effective ways to communicate data analysis is to tell a story that connects your data, analysis, and audience. This can help make sense of the data and analysis, as well as engage and persuade your audience. To tell a story with your data analysis, you should follow a clear and logical structure that has a beginning, a middle, and an end. For instance, start by introducing your topic, goal, and audience, providing background and context. Then present your data, analysis, and findings - explaining how they relate to your goal and audience. Finally, summarize your main points and provide recommendations, implications, or next steps.
Application I have found it useful to present data with examples that applicable to the real life situation or field of study. For climate change studies have used temperature change graphs in real settings at conferences. Limitations Highlighting Limitations on data being presented helps too to ensure that its not misinterpreted to apply in other settings.