Seven Tenets of Quantitative Data Presentation
Presenting quantitative information is a specialized form of communication. Like all forms of communication, quantitative data presentation is most effective when we follow a few best practices, such as the following seven tenets.
- Know your data. Until you understand the stories that live in your data, you can’t begin to tell them.
- Know your audience. Unless you understand what matters to your audience, you won’t know what is of interest and use to them.
- Determine your message. Every dataset contains multiple stories. You can’t tell them all at once. Before you present quantitative information, you must determine the specific message or messages that you want to communicate. Start by writing a sentence or two or three to express the message before moving on to determine the ideal means of expression.
- Reduce the data to what’s needed to communicate the message. Pare the data down to the essence of what your audience must see to understand the message. What’s essential usually involves more than a simple set of primary values (e.g., monthly sales figures), for without context in the form of comparisons, numbers mean little. For example, monthly sales figures compared to target values or to values for the same months last year are more meaningful than sales figures alone.
- Determine the best means of expression. Some quantitative messages are best communicated with words, some with tables of numbers, some with graphs, and some with a combination. Some messages are best displayed in a bar graph, some in a line graph, some in a scatter plot, and so on. Knowing which form of expression works best for the message that you’re trying to present requires a little training into how our eyes and brains process visual information. The principles are easy to learn, but they aren’t intuitive. I wrote the book Show Me the Numbers, in part, to teach these principles.
- Design the display to communicate simply, clearly, and accurately. Include nothing that isn’t data unless it’s needed to support the data. Unnecessary color variation and visual effects, or even grid lines in a graph when they aren’t needed, will detract from the message. Non-data elements that are needed should only be visible enough to do their job and never so visible that they call attention to themselves. Non-data elements should sit politely in the background so the information stands out clearly in the foreground. If some information is more important to the message than other information, do something visual to feature it. For example, a brighter color or thicker stroke would make a particular line in a line graph stand out more than the others.
- Suggest a way to respond. Whenever possible, make it easy for your audience to respond with appropriate action by suggesting specific steps. Most quantitative messages aren’t presented merely to inform, but also to motivate a useful response.
Take care,
5 Comments on “Seven Tenets of Quantitative Data Presentation”
Items 6 and 7 (present clearly and suggest a way to respond) made me think of previous clients who have had accountancy backgrounds. I’ve always found that some people are more averse to graphs and happier with tables. In particular, they get into the table by seeing how it adds up and checking that it reconciles. Once they’ve got through this (if they do) then they might start thinking of consequences and responses. If I include graphs, they are usually the most vociferous in demanding that values are imposed on the columns, etc., and that gridlines are used so they can add things up and net them off.
This small but influential group also appear to have had their intuitions lobotomised at some point, and efforts to make information clearer appear to either bemuse them or convince them that you’re selling snake oil because they don’t see it. I suspect they see numbers more as concrete nouns rather than abstract representations and this reflects how they analyse data.
Neil,
Just one small correction: when numbers are expressed textually, this is also an abstract expression. In fact, language is a more abstract representation of quantitative values than pictures. I suspect that this particular group of folks that you’ve described prefer tables of numbers, not because they are less abstract, but because they are more precise. They are incorrectly assuming, however, that precision indicates accuracy and truth, which is not the case.
Stephen,
Very true that they require the precision and certainly numbers are an abstraction. I was thinking of abstraction more as a higher order concept.
For my old clients with accountancy backgrounds (and I’m only singling them out because of familiarity), the numbers, along with the mechanistic rules for adding them up and netting them off, are an end in themselves — the objective is to get the accounts signed off. But using these numbers to understand how the world works, and how their actions could influence it, is a higher order abstraction and less mechanistic/concrete. Like a computer, they are more wedded to the process without consideration of what the number means, only that rules can be applied and verified.
I fancy that a prerequisite for graphicacy is numeracy. And a prerequisite for numeracy (rather than computation) is critical thinking. And critical thinking is often missing, even amongst those who regularly work with numbers and data.
I’d be interested in additional detail related to tenet number 7. Do you have examples or perhaps other blog posts that explore that message?
Tom,
Regarding tenet #7 — “Suggest a way to respond” — I suggest that you read the books by Chip Heath and Dan Heath titled “Made to Stick” and “Switch.” They cover this tenet well.