Data visualization is a powerful skill that can help you communicate complex information, insights, and stories with your audience. However, it also involves many challenges and conflicts that can affect the quality, credibility, and effectiveness of your visualizations. In this article, we will explore some of the most common conflicts in data visualization and how to resolve them.
One of the main conflicts in data visualization is finding the right balance between aesthetic and functional aspects. Aesthetic aspects refer to the visual appeal, creativity, and style of your visualization, while functional aspects refer to the clarity, accuracy, and relevance of your data and message. Both are important, but sometimes they can clash or compete for attention. For example, you might want to use a fancy chart type, a colorful palette, or a catchy title, but they might not suit your data, audience, or purpose. To resolve this conflict, you need to prioritize your goals, know your audience, and follow some best practices for design and data visualization.
Let's think first what's the purpose of your data visualization. You can get creative and splash colors if you are trying to attract attention with an amazing graph. But you may end up with something fancy that is difficult to understand and the audience will need to read the "instructions" on what each color and form means. If your main goal is for your audience to understand the trends at a glance, you should ensure your graph is simple and clean, and even if it looks boring to you, your audience doesn't necessarily feel the same way. We tend to add background color and make dashboards different to outstand, but that's because we have already seen thousands, because we work with data visualizations every day.
Another common conflict in data visualization is deciding how much data to show and how much story to tell. Data refers to the raw numbers, facts, and evidence that you want to present, while story refers to the narrative, context, and meaning that you want to convey. Both are essential, but sometimes they can overwhelm or contradict each other. For example, you might have a lot of data to support your point, but it might be too dense or confusing for your audience. Or you might have a compelling story to share, but it might be too vague or biased without enough data. To resolve this conflict, you need to focus on your main message, simplify and filter your data, and use storytelling techniques to engage and persuade your audience.
A third common conflict in data visualization is choosing between exploration and explanation modes. Exploration mode refers to the process of analyzing, discovering, and interacting with your data, while explanation mode refers to the process of communicating, summarizing, and presenting your data. Both are valuable, but sometimes they can require different approaches and tools. For example, you might use a dashboard, a map, or a scatterplot to explore your data, but you might use a report, a slide, or a bar chart to explain your data. To resolve this conflict, you need to understand your objective, your audience, and your medium, and use the appropriate visualization techniques and formats for each mode.
Your audience here will determine whether an exploratory or an explanatory dashboard works best. Normally, when addressing high-level executives, a brief explanatory visualization is the best option. They won't have time to play with the filters and extract conclusions. On the contrary, when designing visualizations for data analysts, or people who want to interact with the data and play with the variables, an exploratory dashboard is the go-to option.
A fourth common conflict in data visualization is dealing with novelty and familiarity. Novelty refers to the originality, innovation, and surprise factor of your visualization, while familiarity refers to the conventionality, recognition, and comfort level of your visualization. Both are desirable, but sometimes they can create confusion or resistance. For example, you might want to use a novel visualization technique, such as a network graph, a treemap, or a heatmap, to show your data in a new way, but your audience might not be familiar with it or how to read it. Or you might want to use a familiar visualization technique, such as a pie chart, a line chart, or a table, to show your data in a clear way, but your audience might find it boring or predictable. To resolve this conflict, you need to consider your audience's background, expectations, and preferences, and use novelty and familiarity strategically and appropriately.
If you chose a novel visualization, how are you planning to teach your audience how to understand it? Would you add a "read me" text? Would you be presenting the graph at a meeting and answering all their questions? There is a risk of them not wanting to look like the only ones not understanding, and therefore not asking questions and as a result, your graph won't be useful. You can add novelty colors or forms to a familiar graph to refresh it.
A fifth common conflict in data visualization is maintaining consistency and variety. Consistency refers to the coherence, harmony, and alignment of your visualization elements, such as colors, fonts, symbols, and layouts. Variety refers to the diversity, contrast, and differentiation of your visualization elements, such as chart types, data sources, and perspectives. Both are important, but sometimes they can create monotony or chaos. For example, you might want to use consistency to create a unified and professional look and feel for your visualization, but it might also make it dull and repetitive. Or you might want to use variety to create a dynamic and interesting look and feel for your visualization, but it might also make it messy and distracting. To resolve this conflict, you need to follow some design principles, such as contrast, repetition, alignment, and proximity, and use consistency and variety wisely and moderately.
A sixth common conflict in data visualization is working with others or working alone. Collaboration refers to the involvement, input, and feedback of other people, such as colleagues, clients, or users, in your visualization process. Autonomy refers to the independence, control, and creativity of yourself, as the data visualizer, in your visualization process. Both are beneficial, but sometimes they can create tension or compromise. For example, you might want to collaborate with others to get different perspectives, insights, and ideas for your visualization, but you might also have to deal with conflicting opinions, expectations, or agendas. Or you might want to work autonomously to express your vision, style, and expertise for your visualization, but you might also miss some opportunities, errors, or improvements. To resolve this conflict, you need to communicate clearly, respect different roles and perspectives, and balance collaboration and autonomy.
Co-creation thrives on the ABC of the audience: Autonomy, allowing individuals to express themselves freely and they can act on their own. Belonging, creating a sense of community from the beginning, so that your audience feel they belong to the project. Competence, ensuring everyone’s skills are utilized and they are able to truely grasp the meaning of your data visualization and can use all the features (drill down, buttons, filters, …) This approach not only enhances the quality of outcomes but also promotes engagement and satisfaction among readers and creators of the data visualization. Co-creation needs autonomy and can act together as a powerful tool for achieving shared goals.