Data analytics is a powerful skill that can help you discover insights, solve problems, and communicate results. But to do that effectively, you need to adjust your communication style depending on your audience, purpose, and context. In this article, you'll learn some tips and strategies to tailor your data analytics communication style to different scenarios and stakeholders.
The first step to adjust your communication style is to understand who you're talking to and what they need from you. Different audiences have different levels of data literacy, interest, and expectations. For instance, a technical team may require the details of your data sources, methods, and code, while a business leader may prefer the main findings, implications, and recommendations. To know your audience, consider questions such as their background, role, and goal; their familiarity with the data and the problem; how much time and attention they have; what questions or concerns they have; and how they prefer to receive information.
Also, in many places, accessibility requirements as well as cultural requirements mean that you will need to provide inclusive accommodation and communication in all official languages in both outgoing and incoming communications.
One important note to consider the amount of time your audience has. A CEO might only have time for a quick overview, while a project manager might allocate more time to delve into details, and another note to consider about visualization you should figure out people prefer visual representations like charts and graphs or might prefer written reports or verbal explanations.
When presenting your data analytics results, you should consider the best format to use for your audience and purpose. A report is a formal and comprehensive document that covers the background, objectives, methods, results, analysis, and conclusions of your data analytics project. Alternatively, a dashboard is an interactive tool that displays key metrics, trends, and insights from your data in real time or periodically. Presentations are a concise and engaging way to share your main findings, stories, and recommendations with a live or recorded audience. Lastly, a blog post is an informal and accessible way to showcase your data analytics skills, insights, and learnings with a wider or online audience.
Your client will benefit from multiple methods of data visualization, and it is more the standard now to offer complementary reporting styles versus just one.
The third step is to use clear and simple language to communicate your data analytics results. Avoid jargon, acronyms, and technical terms that might confuse or alienate your audience. Instead, use plain and precise words that convey your message clearly and accurately. For example, instead of saying "We performed a linear regression analysis to test the hypothesis that x affects y", you can say "We used a statistical method to see how x changes y". You can also use metaphors, analogies, and examples to explain complex or abstract concepts in a relatable way.
Not all stakeholders understand university-level data science, but it is possible to still communicate effectively in plain language. Test your reporting on a non-data science expert first.
The fourth step is to use data visualization to make your data analytics results more appealing, understandable, and memorable. Data visualization is the art and science of presenting data in graphical forms such as charts, graphs, maps, and diagrams. It can help you show patterns, trends, and outliers in your data; compare and contrast different data sets or groups; highlight key findings or messages; tell a story or narrative with your data; and engage and persuade your audience. To create effective visualizations, it's important to choose the right type of chart for your data and purpose, use colors, labels, and legends to enhance clarity and readability, avoid clutter, distortion, and misleading scales or axes, add context and annotations to explain your data, and test and refine your visualizations with feedback.
Be prepared to template but also to make modifications in the future. Sometimes, the data collection will dictate what is reported, but sometimes, the client will surprise you.
Providing context and interpretation for your data analytics results is the fifth step. Data alone is not enough to communicate your results; you need to explain what it means, why it matters, and how it relates to the audience's goals or problems. To do this, you should frame your results with a clear and relevant question or problem statement. Additionally, provide background information and assumptions that inform your data analysis, as well as key takeaways and insights from the data. Don't forget to provide evidence and reasoning to support your claims or arguments, while addressing potential limitations, uncertainties, or biases in your data or methods. Lastly, relate your results to the bigger picture or the broader implications.
It’s important to not feel too confident about implications and recommendations without a sufficient level of background knowledge, ideally education in a particular subject that the data relates to. Do your best to ask industry experts who are researchers versus relying on researchers who cover multiple industries.
The final step is to invite feedback and action from your audience. Communication is a two-way process, so you should engage your audience and encourage them to respond to your data analytics results. To do this, pose open-ended questions that invite discussion or reflection, solicit feedback on your data analysis, methods, or presentation, provide suggestions or recommendations for further action or improvement, include a call to action that motivates your audience to take the next steps, and follow up with your audience to ensure understanding, satisfaction, or impact.