4/24/2021 0 Comments Click Data Visualization
When you set up your axis scale, keep it close to the highest data point.But while doing so is easy, a great dashboard still requires a certain amount of strategic planning and design thinking.Knowing what story you want to tell (analyzing the data) tells you which data visualization type to use.Lets assume you have the right data and the right data visualization software.
Hopefully, this post will help you create better data visualizations and dashboards that are easier to understand. The fundamental categories that differentiate these questions are based on. Data-driven storytelling is a powerful force as it takes stats and metrics and puts them into context through a narrative that everyone inside or outside of the organization can understand. And ultimately, youre likely to enjoy the results youre aiming for. Take the time to research your audience, and youll be able to make a more informed decision on which data visualization chart types will make the most tangible connection with the people youll be presenting your findings to. We know what it takes to make a good dashboard and this means crafting a visually compelling and coherent story. At a glance, you can see any total such as sales, percentage of evolution, number of visitors, etc. This is probably the easiest data visualization type to build with the only consideration being the period you want to track. Do you want to show an entire history or simply the latest quarter It is crucial to label the period clearly so your audience understands what story you are telling. Adding a trend indicator compares your number to the previous period (or to a fixed goal, depending on what you are tracking). Using too many can also make your dashboard a little superficial. For example, if you are tracking total sales for the current quarter, compare that data to the same quarter last year (or last period depending on your story). Again, remember to label the trend indicator clearly so your audience knows exactly what they are looking at. They display relationships in how data changes over a period of time. In our example above, we are showing Sales by Payment Method for all of 2014. Right away, you can see that credit card payments were the highest and that everything took a dip in September. You may also find your audience constantly referencing the legend to remind them which one they are looking at. If you have too many variables, its time to consider a second (or even third) chart to tell this story.
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