How can you make complex data more understandable with visualization?

In my experience, the magic ?? lies in choosing the right chart. A well-placed bar graph can spark a lively debate about budget allocation, its vibrant colors highlighting the areas of highest spending. Line graphs ??, on the other hand, become elegant storytellers, their smooth curves revealing a product's growth trajectory or the rise and fall of a key metric over time. Data visualization isn't just about aesthetics; it's about guiding your audience through the story the data holds. Visuals are powerful, but wielding that power wisely is key. Choosing the wrong chart can mislead your audience. Opt for clarity over complexity, and let your visuals illuminate the data's true narrative.

From my experience as a data scientist, when choosing which type of visualization?to use, one?should also consider?how difficult it is for the viewer to understand. Network diagrams and Sankey diagrams are examples of complex visualizations that might work for a specialist audience but could be too complex for a general audience to understand. Understanding your audience’s level of expertise is crucial in choosing the most effective way to present your data.

I believe that careful use of whitespace is also part of simplification because it can make a visualization much easier to read and more powerful. Using whitespace correctly keeps the viewer's eye from getting too busy. This is especially important in screens or infographics with a lot of visual information. When you use whitespace well, you can easily lead the reader through the flow of information and draw attention to important data points without using too many colors or notes.

To me, getting clear data presentation also means being aware of the biases that different types of charts can bring in. For instance, pie charts can make it challenging to understand data when comparing more than one group, while bar charts might be a clearer choice. Being aware of these small details makes sure that the data layout not only looks good, but also tells the truth about the data without skewing?or biasing the viewer's understanding.

I’ve often found that providing controls for customization within interactive visualizations can greatly enhance user engagement and satisfaction. Allowinging users the freedom to alter parameters, filter views, or even switch between different types of visual representations can help them study the data in the most useful ways for them. This method not only makes exploring the data more fun, but it also makes it more personal?and useful.

From my own experience, I can say that using visual metaphors or frameworks?that people are already familiar with can help make complex data easier to understand. Using a journey map to show data about the customer experience or a tree diagram to show connections between levels of a hierarchy can help people quickly understand the story without having to look into the data.

Data visualisation is like converting a book into a movie. A good movie can be a 30 sec movie and still give you the maximum understanding of the entire book! Similarly, with a huge set of numbers we derive a meaningful visual that helps in making smart decisions in timely manner.

Imagine going to a tailor to get a 2 piece pen-coat stitched. The tailor ignores all your requirements and makes the stitching as he likes. That is what happens when you get too attached to your dashboards. High chances are the dashboard will be used by a business/product stakeholder. 1. Get their requirement - Top metrics that they want to see. Let's call it L1 2. List down 3 metrics that directly impact L1. Let's call them L2 3. List down 3 metrics that impact each L2. Let's call them L3 1 (L1) + 3 (L2) + 3*3 (L) = 13 metrics Top to bottom, dashboard has the ability to drill down data, understand trends & root cause. Next steps (Add)- 1. Filters 2. Line charts 3. Refresh schedules Next - Beautify into 'pink wala' dashboard ;)