AnalyticsHero, LLC

I try to publish three posts each week, which takes me multiple hours to produce. Many followers have told me they only see one post per week from me on average. This is frustrating because two-thirds of the LI content I produce each week isn’t reaching people who follow me and want to see it. I can’t change how LinkedIn’s platform works, so I’ve devised a solution for people who don’t want to miss my new content. I've launched a new AnalyticsHero newsletter that recaps recent LI posts, blog posts, and Forbes articles. I sent out the first newsletter last week, recapping my April content. I haven’t decided whether the newsletter will be a two-week or monthly recap yet. Occasionally, I may include some themed newsletters with handpicked collections of content around specific topics. If you like my posts on data storytelling, analytics, data visualization, and data culture, please sign up today so you don’t miss out on my latest content: https://lnkd.in/gRNMYJQ7 If you have any other ideas for the newsletter or content suggestions, please share them in the comments.

In #datastorytelling, knowing when to use complex #datavisualizations in your data stories can be challenging. These forms of charts could include scatter plots, Sankey diagrams, heat maps, box plots, etc. In most cases, I’ve found simple charts are often all that’s needed to convey most key points to audiences. Depending on the information you share, you can often use the familiar bar, line, and pie charts. However, there are some situations where simple charts aren’t going to communicate your observations and insights adequately. To help determine when and when not to use complex charts, I’ve developed this two-by-two Chart Complexity Matrix. ?? Data Complexity (x-axis) Some data or information is straightforward, with simple relationships and a limited number of variables and data points. Other data sets contain more complex relationships with multiple variables and many data points. ????♂? Audience’s Need for Depth (y-axis) Sometimes, your audience only needs a basic understanding to make a decision. When you have a clear, singular message to convey, providing extraneous details will be unwanted noise. In other situations, the audience may need a more detailed and nuanced understanding of the information. In these situations, the details are important to making the right decision. Quadrant 1?? – Low complexity + Low need for depth You should use simple visualizations. Quadrant 2?? – Low complexity + High need for depth You will use simple visualizations with more annotations or breakdowns with supporting charts. Quadrant 3?? – High complexity + Low need for depth You will simplify your complex data into more digestible visuals that emphasize your key takeaways or messages. Quadrant 4?? – High complexity + High need for depth You may need more complicated data visualizations in these situations because it will be challenging to convey intricate relationships within the data to audiences that also require a deeper understanding. You should employ techniques and strategies to not overwhelm your audience (e.g., staging how you introduce parts of the chart), but complex charts will be unavoidable. I hope you find this matrix helpful as you consider when and when not to use complex charts in your data stories. The key is to avoid overcomplicating your message, and use appropriate charts to convey your key points. Do you agree with the Chart Complexity Matrix for data storytelling? How do you balance getting your message across when dealing with complex data?