How can you make your data story engaging for non-technical readers?

Knowing your audience and the end-user group is the key of good data visualization and dashboarding. A dashboard should stick to a question which will be answered by the chart elements you choose. Not knowing what your users interest and need for their daily business will lead to dashboards no one is using.

Making data fun for non-techies. Here's your go-to guide. 1?? Know Your Audience ??: What would they want to see? Tailor your visuals to their interests, making it relatable. 2?? Choose the Right Format ??: Graphics speak louder than numbers. Pick charts or infographics for it. 3?? Clear & Consistent Design ??: Ever seen a messy room? Don't let your visuals be one! Stick to a clean layout and use the same style for similar data. 4?? Add Context & Narrative ??: Numbers alone are like puzzle pieces without a picture. Tell a story around your data – why it matters and what it means for your audience. Paint the big picture! 5?? Invite Interaction & Feedback ??: Turn spectators into participants. Add clickable elements, quizzes, or a poll.

To engage non-technical readers it is important to understand the purpose of their interest. Identify their knowledge level and the end goal to be achieved by interacting with the particular audience. For instance, presenting tech to a marketing team could involve adding numbers and end results that are more actionable from a marketing perspective. Presenting achieved results to freshers could involve potential career growth strategies for them. Identify the goal of the audience and self, keep the jargon simple and be concise in providing only relevant information.

A análise de dados sem dúvida é uma arte que envolve técnica e conhecimento específico das estratégias de cada ramo aplicado. Ela é capaz de mostra a dire??o atual de uma companhia e ao mesmos tempo direcionar o caminho correto para atingir os seus objetivos.

Comece explicando o contexto por trás dos dados.Limite o uso de termos técnicos e, quando necessário,explique-os de forma simples e acessível.Gráficos e tabelas s?o ótimas ferramentas para comunicar dados de forma visual.Certifique-se de usar visualiza??es que sejam fáceis de entender, como gráficos de barras, gráficos de pizza e mapas. Apresente um problema ou quest?o inicial, mostre como os dados foram coletados e analisados.Use exemplos concretos e casos reais para ilustrar seus pontos.Isso ajuda os leitores a se relacionarem com os dados e entenderem como eles se aplicam ao mundo real.Seja claro e direto ao ponto, destacando apenas os dados mais relevantes e interessantes para o público-alvoe encoraje a participa??o.

Utilizing familiar metaphors or visual cues that resonate with your audience can greatly enhance the effectiveness of data visualizations and dashboards. Instead of defaulting to typical charts like line, bar, or pie charts, considering visual representations that your audience can easily recognize and relate to can significantly improve comprehension and engagement. By incorporating metaphors or design elements that mirror real-world objects or environments, such as floor maps, consoles, sports fields, or other familiar imagery, you can create a more intuitive and immersive experience for users. This approach can help users quickly grasp the information presented and make connections between the data and their existing knowledge.

I think this is a key step that easily gets overlooked - and we tend to default to the typical charts such as line, bar & pie charts. My tip here would be to ask yourself: what would my audience instantly recognise if they looked at the screen. Is it a floor map? Is it a console? Is it a sports field? In short, think outside the technical capabilities of your tool first to come up with your ideas. Afterwards, bring your technology capabilities closer to your great idea.

Knowing your business users and their questions, choosing the right chart elements will make it easier. Here, I have learned from practice the more simple a chart is the happier your users are. Simple charts that can be read by most users are for example bar charts, time charts, line charts. Keep it simple is the key! Using advanced charts are for the more experienced users in data visualization. Because they need explanation to read it.

Using the right visuals at the right place for your story is important. Like using a funnel chart to show a step-by-step process, not for displaying distribution. Choosing the right format makes your story clear and easy to understand.

Select appropriate data visualizations that effectively communicate your insights. Consider using charts, graphs, maps, or other visual elements that best represent your data and make it easy to understand.

Having a clear concept for your data visualization will bring your data to life: data storytelling. Ben Shneiderman has created a very good approach to follow - the Visual Information Mantra: overview first, then zoom and filter and then details on demand. It describes very good how data should be presented so that it is most effective for users.

This is the easiest part because you know what numbers to highlight, what colors to use for highlighting the metrics, there are resources available about how to use colors for graphs and reports. Keep them consistent over your whole presentation so that their muscle memory knows about what color means what and they are not trying to figure out the graph but the information presented in it.

In the "Attention" phase of the ART model, consistency is key to not only capturing but also maintaining the audience's interest. By employing a uniform visual theme and narrative style, it builds trust and ensures the cognitive load is limited, making complex data more accessible. This approach fosters a comfortable and engaging environment where trust grows and learning is facilitated with minimal effort.

In the third step, apply clear and consistent design principles to ensure your data visualizations are easily comprehensible and comparable. Utilize suitable colors, fonts, labels, legends, titles, and annotations to emphasize crucial information and guide your audience through the data story. Avoid clutter, noise, and distortion, such as superfluous grid lines, backgrounds, borders, or 3D effects, which may distract or confuse. Maintain uniform design conventions and standards across the data story, including consistent color schemes, scales, and units for related visualizations. Adhering to these principles enhances the effectiveness of your data visualization and aids audience understanding.

Simplicity and consistency are key when it comes to designing visualisations. Use simple colors, fonts and words consistently, to help minimise the shift in focus. Choose formats and colour schemes that are muted for background and bold in places where the audiences' attention needs to be drawn. Help the audience focus by de-cluttering the visual and providing concentrated information with required labelling.

One of the guidelines is “Start here” page, similar to “REAdME” file. In a dashboard, analysis or collection of work packages, we add one page that contains the story. Which data we used, its timeframe, and if filters were applied. What are the questions we answered in the dashboard. If there’s a complex calculation or abbreviations, we explain it in a nutshell and how to interpret it. Who’s the owner, contributors, and potential audience? The objective is, whoever open this page, should understand the minimum knowledge to interpret the info.

Crafting an engaging data story for non-technical readers involves more than just presenting raw facts. It's akin to a doctor prescribing medicine without providing instructions on its usage; without guidance, data can be overwhelming and meaningless. To truly captivate an audience, one must weave together the facts into a cohesive and relatable narrative. This approach ensures that the information isn't just delivered but also comprehended, making data visualization a powerful tool for communication across various fields and audiences.

Consider the visualisation to be a story and use the story format of "beginning, middle and end". Provide background required to understand the information that will be brought up, while keeping it simple and straight forward enough to draw the attention of the audience. Create a visual narrative the provides all the necessary information. Finally, summarise the salient points. This will help the audience follow along and retain information even if they have no previous technical knowledge or context.

In the fourth step, integrate context and narrative into your data story to enhance its relevance and meaning for your audience. Provide background information about the data, including its source, collection method, processing, and significance. Craft a coherent narrative structure, comprising introduction, body, and conclusion, with a logical flow from problem to solution and action. Employ descriptive and engaging language, such as metaphors, analogies, or anecdotes, to foster audience connection and evoke emotions and curiosity. By incorporating these elements, your data visualization becomes more compelling and accessible to your audience.

I always provide a methodology page to state the purpose, data source and logic for major measurements, so that users can use as a reference. For each page or tab, adding some notes about the time period of the data shown is also a good practice to give user a clear understanding of how recent the dashboard is refreshed.

In the fifth step, encourage audience interaction and feedback to enhance the impact of your data visualization. Employ interactive features like filters, sliders, and tooltips, enabling audiences to explore data independently and uncover insights. Integrate feedback mechanisms such as comments, ratings, or surveys to gather audience opinions and suggestions. This fosters dialogue, allowing you to understand perspectives and refine your data story accordingly. Through these interactive elements, your data visualization becomes more engaging and responsive to audience needs.

Try to align with your client what they want to obtain as a final result. Interaction between elements, data segmentation, interactive visuals are very welcome

One thing I've found helpful to invite interaction and feedback is scheduling meetings with the stakeholders throughout the design process: 1) Pre-Design: Start to engage the audience with a wireframe conveying your initial roadmap based on the business cases/audience story 2) Mid-Design: Meet with the audience/stakeholders a few times as you bring the Data Story to life as you solicit their input and trust on visualizations, functionality and storytelling 3) Post-Design: Once the data story has been implemented, schedule a follow up meeting at 30 days, 90 days and 6 months to understand usage metrics and the value the data story has added to the audience.

Share your data stories with others and solicit feedback to gain insights into areas for improvement. Consider joining data storytelling communities or workshops to receive constructive feedback from peers.

For digital formats, incorporate interactive elements that allow readers to explore the data further. Encourage feedback to understand how your audience interacts with the data story and what aspects they find most engaging.

One way of testing is sending to audience without explanation. Then, you measure the effectiveness by how many questions you got, how they interpreted the results, how much time tool them to get the needed info. Some points are hard to measure, but if you noticing not correct interpretation of numbers, you need to think about adding info about definitions and formulas you used. If they take long time to find a specific KPI, you might think about organizing the KPIs in a flow aligned with business funnel. Then after refining it, the questions should decrease, and take less time to find the right numbers, and have clear interpretations.

This is crucial and should never be stopped no matter how long you have been working telling stories with data. Be humble and understand that delivering a good product means making mistakes. So be open to always receiving feedback.

Before sharing your data story, it's crucial to test and refine it thoroughly. View your story from your audience's perspective, ensuring accuracy, clarity, consistency, and relevance. Solicit feedback from a subset of your audience or a representative, integrating their suggestions and critiques. Through this iterative process, align your data story with audience needs and expectations, effectively conveying your intended message and achieving desired outcomes.

Sometimes, when you visualise some data, you might be surprised yourself, or you might think "I am sure they'll know what it means". Long story short, they won't know what it means. Add your insights straight away in your reports or next to your visualizations. Don't spread information around and force your colleagues to search for an explanation.

Share your data story with a small, representative target audience group before wide release. Use their feedback to refine and improve the presentation, ensuring it effectively communicates and engages.

In order to convey a data story to a non-technical audience, it is crucial to simplify the complex narrative or problem, steering clear of technical jargon and acronyms. Structuring and articulating your findings in a clear manner is equally essential. Incorporating real-life examples introduces a human touch to the data story, illustrating its impact on individuals or communities. Encourage engagement by posing open-ended questions, transforming the audience into active participants rather than passive listeners. Lastly, gathering feedback at the end of each session is invaluable, gaining insights from the audience's perspective.

- Begin your data story with a relatable and engaging narrative that frames the context and significance of the data analysis. Use real-world examples, anecdotes, or storytelling techniques to capture the reader's interest from the outset. - Incorporate visual elements such as charts, graphs, infographics, and interactive visuals to convey key insights in a visually appealing and digestible format. Choose visualization types that are intuitive and easy to interpret for non-technical readers. -Avoid technical jargon and complex terminology, and instead, use plain language to explain concepts and findings. Break down complex ideas into simpler terms and provide clear explanations .

- You can offer practical recommendations or next steps based on the data presented. This will assist non-technical readers in their decision making processes and they will be able to apply the insights gained from that visualization. - You can incorporate visual analogies which are relatable to non-technical audiences as that can help simplify complex concepts for them, making the information more engaging. For example, using color-coded indicators (green, yellow, red) to show performance levels, representing market share as slices of a pie chart, etc.

In addition to testing and refining your data story, consider incorporating accessibility features to ensure inclusivity. This involves making your visualizations navigable for individuals with disabilities, such as providing alternative text descriptions for images, ensuring compatibility with screen readers, and offering options for adjusting font size and contrast. By prioritizing accessibility, you enhance the usability and reach of your data story, making it accessible to a wider audience and fostering a more inclusive data-driven dialogue.

It is a wonderful guide to begin building dashboards for non-technical users. It is important users understand the story and what insights they can obtain from the dashboard.