Showing posts with label data sharing. Show all posts
Showing posts with label data sharing. Show all posts

Friday, August 19, 2011

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?

Thursday, July 19, 2007

Unveiling the Beauty of Statistics

Jesse Robbins' excellent blog post - Unveiling the Beauty of Statistics - at O'Reilly Radar points us to another recent and Rosling video presentation - this time from the OECD World Forum in Istanbul. Unfortunately, the camera work is only a fraction as good as with the TED presentations and weather channel type presentation showing only Rosling speaking or showing a long view of the whole auditorium. In neither case is the exciting data that Rosling is talking about visible to the video viewer.

However, Rosling's message in this talk is still exactly on the money - a continued call
for more open and available data,
for better tools for visualizing it, and
for making it available to ordinary citizens around the world.

The chorus of voices echoing this message is rising. Web sites with visual tools such as Gapminder, Swivel and ManyEyes are leading the charge and changing the way we think about data.

Wednesday, April 11, 2007

Iraq Trend Data Set now posted to Swivel and Many Eyes

A while back we mentioned the great new data sharing and visualization web site called Many Eyes and we pointed out an Iraq Trend data set we had uploaded so that others could more readily examine and analyze it.

We've recently come across another great new data sharing and visualization web site called Swivel and have uploaded the same data set there for comparison purposes at: http://www.swivel.com/data_sets/show/1004780.

Both Many Eyes and Swivel fall into a category I have started calling Trend Visualization Appliances. The amazing Gapminder work also fits into this category as do all the various stock market visualization tools currently available.

The old fashioned form of trend visualization appliances (e.g. a standard report including graphical output) can still be pretty useful when managed carefully. For further comparison, here is the link to a PDF report that graphically presents the same data - vizualizing-trends-ohanlon-testimony.pdf.

Similarly, for data sharing, the old fashioned URL links to csv files offer an alternative to the WEB 2.0 mechanisms such as Many Eyes and Swivel. Here's the Iraq trend data the old fashioned way: ohanlon-key-factors.csv

So many choices, so little time. How can we decide which is best for our purposes?

I'll be revisiting this topic, but for now, my own criteria for deciding which tools I will use at a given moment are:
1) ease of use,
2) shortness of learning curve, and
3) personal productivity and time saving -- the speed at which I can navigate through complex trend data sets to discover previously hidden patterns.

What do you think?