What are the best ways to design data visualizations for insurance decision making?

For effective insurance data visualizations, it's vital to understand the audience, simplify complex data, choose suitable visualization types, provide context, and use interactive elements. Highlight key insights, ensure accessibility and data integrity, iterate on designs, and offer training support.

Understand your audience: Tailor the visualizations to the specific needs and preferences of decision-makers in the insurance industry. Select appropriate visualization types: Choose the right visualization techniques such as bar charts, line graphs, or heatmaps to effectively represent complex insurance data. Adhere to design principles: Simplify complexity, highlight key insights, provide context, ensure accessibility, and maintain compliance with data security regulations. Test and refine: Continuously improve visualizations based on user feedback, conducting usability testing to identify and address any usability issues. Consider additional factors: Provide training and support for users, ensure data security and compliance.

In my experience part of knowing your audience is understanding what information they know/ have and what are they are interested For eg with premiums going up audience might be be aware of the factors driving it and may be more interested in what can be done to lower it. This could save you time by directing your efforts to what’s not known or providing an alternative view that may challenge existing views ie provide insights

The goal should be to create a visualization that the user intuitively understands. This can be difficult when dealing with complex data storytelling. It feels great when a visualization I made sparks an aha moment for my audience. For this reason it's crucial to know what the audience already understands so the data visualization can build on their understanding and elevate the conversation.

When developing data visualizations for the Consumer Credit Union, my role as a Reporting Analyst involved a delicate balance between providing valuable insights and engaging our diverse audiences. For our forward-thinking senior executives at the helm, I crafted an eye-catching dashboard featuring key performance indicators. This visual feast offered a comprehensive view of the credit union's financial health, the rhythmic patterns of our loan portfolio, and the dynamic trends in membership growth. This thoughtful design, akin to orchestrating a strategic symphony, not only painted a vivid picture of our landscape but also highlighted crucial details like unexpected spikes in delinquencies or promising areas of expansion.

The type of visualization does matter to some extent in dashboard creation. The charts / visualizations should ideally be self explanatory to the end user. For example, a time series data can be represented in a line / bar graph. But, let's say if you have huge number of insurance product offerings and want to track a 3-dimensional data like, year on year what are the types of insurance products are the customers purchasing / renewing. A line or bar chart would be too cumbersome to consume the data in one go. But if some thing like sankey chart is built, it could help visualise the data much better. A line / a stacked bar chart would be much preferable if the number of product offerings were not too huge.

Identificar a mensagem-chave. Escolher visualiza??es claras e informativas, como gráficos de barras para compara??es e gráficos de pizza para propor??es. Garantir legibilidade e simplicidade. Usar cores significativas e intuitivas. Incorporar interatividade para explora??o adicional.

Selecting the right visualization is critical for effective data communication. Each type serves a specific purpose, enhancing understanding and insight. The wrong choice can obscure insights and lead to misinterpretation. Thus, choosing wisely is essential for unlocking your data's full potential.

1. Select visualization types based on the nature of the data and the question being asked - Comparisons: bar charts, column charts, scatter plots - Trends: line charts - Distributions: histograms, box plots - Relationships: scatter plots, network graphs - Geospatial: maps, heatmaps - Overviews: dashboards combining multiple visualizations 2. Use advanced visualizations for complex insurance use cases - Risk modeling: heatmaps, network graphs for risk scenarios and chain events - Predictive analytics: visualize outputs of machine learning models.

Choosing the right chart type depends on factors such as the nature of the data, the message you want to convey, and the audience’s preferences. Experimenting with different chart types can help you find the most effective way to visualize your data and communicate your insights clearly.

Colours that one might find good looking might not be good for another. Keeping the colurs to a minimal has helped me. Also, if all the visualisations in all of your dashboard repository follows a similar color pallette, it will help the users connect faster with the new dashboards. These points goes with out saying - never publish a Graph with out the legend or axis lables - The sale of the axis should be consistent. - Leverage shapes and colours not to just beautify the dashboard, but to convey important information as well. - If adding a rainbow of colurs is not adding value, please forego doing so. - Reading a bit about the color theory and the use of contrast, complimenting colurs will go a long way

The right tool will need to be agile enough to quickly identify anomalies in addition to both emerging and trending patterns. Stunning data visualizations can then be leveraged to craft more personalized insurance policies that better meet customer needs.

Incorporating design principles is essential for effective data visualization. Elements like simplicity, clarity, and consistency ensure that information is easily understood and visually appealing. By adhering to these principles, you create visualizations that engage users and convey insights efficiently. Ignoring them can result in confusion and loss of impact. Thus, integrating design principles is key to crafting compelling and informative data visuals.

In the 3rd crucial step of data visualization design enhances clarity, aesthetics, and impact. Leveraging color, shape, size, and position to encode data facilitates effective communication, but moderation is key to prevent distraction or confusion. Careful application of labels, titles, legends, and annotations provide context without overwhelming the viewer with excessive text or jargon. Scales, axes, and grids play a pivotal role in depicting the range and distribution of data, but it's imperative to avoid misleading or inconsistent representations. By incorporating these design principles judiciously, you ensure that your data visualizations not only convey information effectively but also captivate and engage your audience.

If you choose to use BI tools like power BI then it is a far better approach to create a cascading meta data interactions such as drill downs and tooltips and one thing to make sure is to create a dashboard within a visible area to avoid scrolling, when every indicator is within one screen there will be faster decision making and lower risk of ignoring any indicator while decision making.

While preparing and while showcasing, there should be a story that a dashboard wants to convey. If there is no story, there will be discussions without any decision making. I strictly follow the rule where each story should leave the decision making onto the audience pre-finishing the dashboard story right before it. If the story is not smooth, change it, if the decision is hard coded within the dashboard, change it, if the story is taking too much effort to make, start from scratch, because you may be furnishing something which does not exist.

The first pass is never the best. I almost always need to see several versions of a visualization before I'm convinced it might be good enough. Then I show it to my peers for feedback. Every iteration I try to make it simpler and easier to understand. When my peers understand the visualization without any explanation from me, I know it's solid.

Besides test and refining, it is always a good option to include the end user in the discovery journey. Schedule meetings with the end user, check how they build their analysis, and watch them analyze a business case. After this, prototype and schedule meetings to explain to the user the design concept and how to use the dashboard. This will increase your success rate. Além de testar e refinar, é sempre uma boa op??o incluir o usuário final na jornada de descoberta. Agende reuni?es com o usuário final, verifique como ele constrói sua análise e observe-o analisar um business case. Depois disso, fa?a o protótipo e agende reuni?es para explicar ao usuário o conceito de design e como utilizar o dashboard. Isso aumentará sua taxa de sucesso.

In the conclusive step of designing data visualizations for insurance decision-making, thorough testing and refinement are very important. It brings the accuracy and credibility upon us. Evaluation should center on the effectiveness of communication, ease of understanding and use, and the overall appeal to the intended audience. Employ diverse testing methods, including gathering feedbacks, conducting usability tests, and leveraging specialized data visualization tools. This iterative process allow for continuous improvements, ensuring that the visualizations align with objectives and surpass expectations.

What good does a super beautiful dashboard with completely wrong information do? I think this is the most crucial step in the dashboard creation. Testing the data and usability goes a long way to get the dashboard adopted. Rigorous testing needs to done and there should be UX and Data Quality Audit for each of the dashboard. From the visualization perspective, annotations and captions on the dashboard will help. In most of my experience i saw that the more adoption on the dashboard, the better it can be refined from the various inputs. The biggest red flag for the end user is dashboards showing wrong information. Once the trust is lost, it is very tough to gain it back. So test, test and test until your are 1000% sure.

Less is more so… check if you can simplify what has been created…Remove all clutter and simplify again. Leaving only what’s absolutely crucial to the story is key.

- Ensure that the data presented in the visualizations is relevant to the specific needs and interests of insurance decision-makers, avoiding information overload and focusing on key metrics and insights. - Incorporate historical data into the visualizations to provide context and perspective on past performance and trends, helping decision-makers make informed decisions based on historical patterns. - Enable users to conduct comparative analysis through the visualizations, allowing them to compare different insurance products, customer segments, or geographic regions to identify opportunities and areas for improvement.

Choosing the right KPI All the above points are helpful in creating visuals.But showcasing the right KPI is also essential. For instance in taking the decision to choose the best policy, avg time to settle a claim may be useful for one audience and for another client satisfaction score may be more helpful.

Here are the do's and don'ts for designing data visualizations: Do’s: ? Know Your Audience ? Choose the Right Visualization Type ? Simplify and Clarify ? Use Color Wisely ? Label Axes and Data ? Provide Context ? Ensure Accuracy ? Test and Iterate Don’ts: ? Overcomplicate ? Mislead with Scale ? Use Misleading Visuals ? Overuse Color ? Ignore Accessibility ? Omit Context ? Ignore Feedback ? Forget to Update

Entrevista a tu cliente/audiencia, simplifica y guía al usuario a través de notas y explicaciones que contextualicen. Dise?a desde niveles agregados y bajas desagregaciones, de forma que se entienda como se descomponen los datos.