Data Mystification Techniques

I am a fan of studies in Data Visualisation. It is a creative and dynamic field with a lot of room for experiment. I am considering report and dashboard design, and within this frame Data Visualisation, as a form of practical art. Well designed and built reports are critical for solution adoption and usability. However, in this post I will concentrate on exactly the opposite topic – intentionally mystifying reports, obscuring the data and making it hard, for the report consumers to reach informed conclusions. I will also show how we can make the data visualisations as misleading as possible. This post is not as abstract as one may think, as it draws its examples from a very real project, for which I had to build a report under heavy pressure.

Initially, I was asked to build a report based on a small data set (~450 rows) stored in an Excel workbook. The data was perfectly suitable for building a Pivot Table on top of it, so I did so and then I decided to use the pivot table as a source for my report. The users have one measure – Spending Amount and a few dimensions – Category and Focus for that spending. The request came in the form: “We want to see the Amount by Category and Focus in both graphical and numeric form“. So, I sat down and produced this prototype (with their colour theme):

As you can see from the screenshot, the report is fairly simple – the bar graphs on the top clearly show how the Amount is distributed per Category and Focus. Also, because of an explicit request, I build the bar graph on the second row to show category and focus amounts combined, and in order to clarify the whole picture, I added the table at the bottom of the report. The report works for colour-blind people too, as the details for the Focus per Category Expenditure bar graph are shown directly below in the data table.

I sent the report prototype for review, and the response was:

1. Remove the table.
2. Change the bar graphs to make them more “flat”. Meaning: there is too much difference between the bars.

The reasoning – it is “too obvious” that the data is unevenly distributed. As the report is supposed to be presented to higher level management, discrepancies in the amount allocation would “look bad” on the people who requested me to create the report at the first place.

Adding more fake data got rejected, so I was advised to prepare a new prototype report with the new requirements. It follows:

Now, Stephen Few would spew if presented with such a report. I did everything I could to obscure the report – added 3D effects, presented the amount in 100% stacked graphs when absolutely not necessary and, of course, I had to add a Pie Chart. A 3D Pie Chart. The whole report is totally useless. It is impossible to deduct the actual numbers of the various category/focus amounts. Furthermore, the Pie Chart combines the focus and category and uses virtually indistinguishable colours for the different slices. The 3D effect distorts the proportions of these slices and if printed in Black and White, or if viewed by a colour-blind person, the report looks like this:

Since my goal of total mystification of the report was achieved, I sent the second prototype back.
The response was: “It looks much better, and we really like the top right bar graph and the Pie Chart. Would it be possible to leave just those two and change the Pie Chart to show data broken down just by Category?”

So, the users did not like my combined series. A point for them. Then I decided to remove the 3D cone graph, to remove all 3D effects, to make it more readable and to create the following 3rd prototype:

Here, we can see that the pie has the actual percentages displayed, and each category amount can be calculated from there. The stacked bar graph is again almost useless.

The users liked it, but still thought that it was too revealing, and were particularly concerned with the fact that there are a couple of “invisible” categories (the ones with small percentages) on the Pie Chart. They had a new suggestion – change the pie chart back to a bar graph and play with the format, so that even the smallest amount is visible. I offered an option, made another prototype and it finally got approved. The exact words were: “The graphs are exactly what we want“. There it is:

The Y Axis in the first graph is interesting. Not only that there is a “scale break”, but in fact the scale is totally useless, as it is manually adjusted, because of the big gaps between the amounts. I needed two scale breaks, which for a series with 6 values is a bit too much. You can compare the normal linear scale of the first Prototype I created and this one. However, it hit the goal – my users were satisfied.

I consider this effort to be contrary to be an exercise in “Data Mystification”. It is very easy to draw the wrong conclusions from the report, and it achieves the opposite to the goals of Business Intelligence, as instead of empowering users, it could actually confuse them and lead them to making wrong decisions.

Developing reports for colour-blind people

According to Wikipedia 8% of all males and 0.4% of all females in Australia are colour-blind to some degree. The percentages are slightly different in the USA – 7% for males and 0.4% for females. It is estimated that these would be similar to other countries in the world, which means that a very high percentage of people may have difficulties distinguishing colours. Therefore, a significantly large part of our potential report users may not be able to interpret some of our graphs and charts correctly (There would be around 400 colour-blind males and 20 colour-blind females in an organisation which employs 10000 people).

In example, the following chart is quite nice and simple but can be useless to colour-blind people:

versus:

Similarly, this table is somewhat confusing if we remove the colours:

versus:

We have to be very careful with design of KPIs and dashboards in general:

versus:

As we can clearly see from the above examples, not being able to clearly distinguish colours makes our poorly designed charts and tables confusing.

We should always keep in mind the following considerations when designing and implementing our most-common report items to ensure that they can be used by everyone in our client organisation:

• KPI indicators must be different in shape rather than just colour
• Line charts should utilise markers with different shapes
• Bar graphs should include a marker on the top of each bar
• Avoid colour-coded pie-charts – they can be extremely difficult to read for a person with even the slightest colour-blindness condition
• Avoid colour-coding tables – either backgrounds or text colours are usually unacceptable

Other more general suggestions:

• Shapes are much more important than colours
• Greyscale and shades of the same colour are acceptable, as a colour-blind person can distinguish between those

Of course, even after all our efforts to create reports readable by everyone, we may miss some detail. The best way to ensure that we have done a good job is to test. There are two good and easy ways to do that:

1. Printing – print the report in black and white and see if all information is well presented
2. Changing display to greyscale – Windows lets us choose how many colours we want to display on our screen. Choosing greyscale and then playing with our report is possibly the best way to ensure that colour-blind people can use our reports.

It is fairly simple and easy to always apply these design principles when creating reports. I have found that most organisations are quite happy to include minor tweaks to their dashboards and reports when they understand how important they could be for some of their employees. Furthermore, it helps to promote accessibility to technology regardless of minor disabilities and gender.