Common Weaknesses in Online Visualization & Storytelling

Looking around the web we notice a growing number of examples of online visualization and storytelling.

Some of these are brilliant and incisive and easily accessible and digestible by their intended audience.

Too many however (including I am sure many of my own) exemplify one or more of a common set of weaknesses that make them harder to understand and that diminish their usefulness and value.

Here is a list of the shortcomings that appear most regularly. Once you can recognize them, all of these are correctable, often with only a modest effort that will pay big dividends. We've talked about many of these in previous posts and will no doubt return to them again to describe the particular details.

Can you think of any others? Which ones do you think are most important to correct?

Please share your thinking in the comments. Thanks.

Online Visualization and Storytelling Weaknesses and Shortcomings

1. Too short a time period shown
   
2. Too few metrics shown (sometimes only one or two out of thousands) and often only a single independent view of the key story telling metrics
3. No story presented - figuring out the story is left as an exercise for the audience/reader/viewer. This often goes hand in hand with visualizations that require a substantial time investment by the audience in order to discover messages that are not obvious at first glance. Or worse to spend time and not be able to figure out why that particular graphic was chosen from amongst all the choices available to the analyst/storyteller
   
4. Data set used to create the graphics is not readily available for further analysis by interested audience members
   
5. The larger data set used by the analyst/visualizer/storyteller is not available and not even defined or listed. Consequently the viewer has no idea of how much effort the storyteller put into the analysis before deciding to display a particular choice of graphical elements.
6. A standard template is re-used without any new or fresh thinking and without any sign of building on what's already been learned from previous analyses
   
7. Presenting only a single point in time for many metrics that change over time without providing the relevant time line view
   
8. Comparing just the most recent and the previous value of a particular metric without taking earlier values into account. This goes hand in hand with over use of graphs and tables showing month over month change.
9. When showing month over month change, failing to normalize the values to yearly percentages
10. Explaining time series behavior in dense text that is hard to parse and understand even for expert data analysts when a simple time series graphic would have done the job in seconds
    
11. Limited opportunities for further collaboration between the audience and those who created the visualizations and story line.
    
12. Too few data points in the time series
13. Use of large unsorted lists where some simple sorts and application of some variant of the 80/20 rule would have conveyed much more meaning in a much shorter time
14. Too may metrics all mushed together into a single indecipherable graphic. Such charts typically are ones that have no story line associated with them. What does the chart mean? You go figure it out!
    
15. Burying the lead (the potentially most interesting story element) so only audience members who invest significant time will ever have a chance to stumble across it. Everyone focuses on some headline number while the action is just a little bit below the surface and eager to see the light of day
    
16. Absence of comparisons of the result to useful baseline values
17. Working exclusively with the raw metrics as they arrive from their providers and missing out on opportunities to combine metrics to create calculated values that enhance the storytelling potential
    
18. Heavy emphasis on working with aggregated metrics (e.g headline numbers) and not showing whether the same patterns hold up under a variety of disaggregation approaches
19. Using widely varying raw metric values when a carefully selected simple moving average would have revealed greater insight
20. Overly tiny graphics that fail to take advantage of the full screen real estate available and make key elements more difficult to read and understand
    

What weaknesses would you love to see corrected?