Seneca said, “We are more often frightened than hurt, and we suffer more from imagination than from reality.” Imagination is a powerful weapon. Designing compelling data visualizations to sell stories might get a human imagination down to work.
To make it happen, the context is a key player. Without the context, it is hard to understand presented numbers or outcomes. The human brain always seeks for comparisons to create a meaningful picture of the world. In this article, I would like to talk about how we can add context to presenting the behaviour of the phenomenon over time.
From my experience, I often see a single line of eg. revenue, sales, costs or number of claims presented on a line chart. However, without the proper highlighted background, it’s hard to say if what we see is positive or negative. Is this change is for better or worse. Using additional information, the message is strengthened and helps tell a thoughtfully crafted story.
This approach is especially important when the report supports the decision-making process. Quick business insights can be easier revealed when decision-maker can benchmark presented data to thresholds.
Let’s check how different stories can be told. On this chart, we can see a single line represents revenue of company X. Analytical eyes will see the downward trend over time. However, maybe this observation is not so clear for people who have other skills then analytical.
The first story can be about a decline in revenue over two last years. The declines in revenue can be depicted with an added trend line. In real scenario would be good to highlight specific points in time which caused this change.
The second one can focus on now and then. Comparing the two times period, current and last year helps see the magnitude of change. However, it’s good to remember that on such visualization trend over the longer period is lost.
The last one doesn’t emphasize changes over a longer time at all. It just presents performance vs. budget and directs the audience attention to “here and now”.
In conclusion, there are three different contexts for the same dataset, which changing the data perspective. Frankly speaking, combining these three perspectives gives an insightful story of revenue condition.