What are some tips to make your data more accessible to a non-technical audience?

- Use clear visualizations like charts and graphs. - Minimize technical jargon for better comprehension. - Provide concise summaries and key takeaways. - Offer real-world examples to illustrate data points. - Use plain language to enhance accessibility.

Understanding your audience is crucial before preparing data. Consider their familiarity with the topic, data sources, and methods Identify their goals, and pain points. Determine the key messages or actions you want them to take away from your data. By knowing your audience, you can tailor your data to meet their needs, interests, and expectations. Knowing your audience helps you create data that is relevant and valuable to them. It allows you to address their specific goals, and pain points By understanding their level of familiarity, you can present the data in a way that is easily understandable and to them Additionally, knowing their goals and desired actions helps you focus on providing insights that align with their objectives.

Números soltos n?o dizem nada. Dados s?o uma história contada por números. Um gráfico de impress?es no LinkedIn por exemplo diz o quanto as pessoas est?o vendo suas postagens. Se você cruzar essas informa??es com os posts individuais, consegue contar a história de como um post específico gerou mais impress?es e trouxe mais pessoas para seu site ou a página da sua empresa. O jeito mais simples é tranformar os números em uma "história". Um gráfico sozinho pode n?o ser o suficiente para convencer a sua empresa de come?ar uma iniciativa nova, uma história pode!

Data can be made easily understandable in the following ways - Visualization* - Presentation In Layman Terms - Statistical breakdown - Text toning (Higlighting the most important terms which makes users to understand easily). - Easily understandable examples this might help them easily to understand each and everything * - Data visualization is crucial , not every kind of visualization is necessary for the data , the data should be analysed and the correct kind of data analysing pattern should be taken. For comparison data a kind of visualization or a statistic data another kind of visualization would help etc.

The secret is simple. Simplify. Make each concept easy to grasp. In fact, if you do this, even the technical audience will appreciate your effort! Simplicity is King when it comes to technical writing. Break down the concepts into smaller chunks. Explain any jargon you find within the text. And use diagrams wherever you can. These simple tips will put your data-base content above most of what's available to your reader.

Data storytelling techniques are great for showing readers the data without making it look like just a bunch of numbers in a spreadsheet. Spotify Wrapped is a great case of this technique. At the end of the year, we receive a compilation of our main music and podcasts streams: the 5 most listened to artists, our favorite music genre and so on. Of course, the data contained in Spotify's retrospective isn't the only data they have on us, but it's the data that has managed to become such a success case that we look forward to see this compilation year after year. It's a great job of analysis, of curation and of storytelling. We need to do that with our data stories too.

Here is what I do while writing for a non-technical audience: I close my document after finalizing the first draft. Returning to the article after a break, with a reader's mindset allows me to identify which parts of the article need technical knowledge to understand. This step helps me immensely in simplifying the write-up as required to suit my audience's understanding of the topic.

I can tell you that Brazil has a territorial extension of 8,515,759 square kilometers and leave it at that, or I can add the information that you can fit about 25 Germanys inside Brazil. Although you might get a certain idea of the size of Brazil, the real dimension comes from the comparison made with Germany. That's because, generally speaking, a single number doesn't mean anything. Something is only big or small in comparison to something else. That's why it's important to put things into context, to make comparisons with something the reader is familiar with or to provide historical data.

The most effective data tells a story. Seeing data that shows a before-and-now, a cause and effect, a reason why something is occurring in our community or our business -- you collect data to explain something, which in turn can tell a story. For example, data showing the rise of gun violence in the U.S. in the past two decades has the potential to tell a story beyond just that data. Be curious. Why is there a rise? Is it in specific places? What else can the data show us that explains this trend, and what story does it uncover? It is synthesizing our data into stories that matter to our audience that will captivate a non-technical audience. Don't be afraid to explain things from the beginning.

Diagrams and charts make it easy to see how data points relate to each other. Use them as much as you can. In fact, this is why infographics are still popular. The thing is, people don't want to interpret data. It's like asking people to bake their own bread. What people want is an expert to tell them what the data means. So, supplement your diagrams and charts with your comments. Do that, and people will be able to use the data you provide.

Visual aids can help to simplify complex data, making it easier for a non-technical audience to grasp the key messages. A well-designed chart or graph can distill intricate information into a much more digestible and comprehensible format. Visual representations can facilitate the understanding of comparisons between data points, identifying patterns, trends, or anomalies within the data, and can help to make data more accessible to a broader audience (such as to different languages, for example). Not only that, but people tend to remember visual data much more readily than a list of numbers or figures, which can also help to get the data across when it comes time to present or discuss your findings.

Something I have found helpful is utilizing the cause-effect principle. Tell your audience what makes the data significant and what they are supposed to do with it (what it implies). Also, whenever you can, use comparison. It gives your audience context, creates relationship, and reveals patterns.

Data is the raw material of information. Remember that. Your power lives in your ability to interpret the data. And, to make it easy for people to understand your interpretation. If you understand this, you will be well above most writers trying to compete with you.

Data is important to support your ideas, and context is important to support your data. An example. Emissions of carbon dioxide and other greenhouse gases fell by 4.6% in 2020, according to the International Monetary Fund's climate change indicator. This information would normally be very positive. But as this is the first year of the Covid pandemic, we can interpret the drop in emissions as an effect of the restriction of mobility, and not because human beings are emitting less carbon. In 2022, annual global greenhouse gas emissions increased by 6.4%, reaching a new record and surpassing the pre-pandemic peak as global economic activity resumed. Therefore, data and context go hand in hand.