Visualization is a tool that allows us to communicate more information faster, inherently reducing our speed to insight. Given enough time and the proper tools, we can almost always derive pertinent insights from data or information. The key value that visualization provides is the alacrity that we move through the analysis process.
This speed is valuable because of the simple fact that all insights are perishable and have a limited shelf life’. The value of an insight derived from data diminishes the further away from the event you get. What is the value of an insightful revelation when the time for making the decision has already passed?
Being able to produce information in a consumable format at the exact minute it is needed for a decision sets automated visual analysis apart from other more mundane reporting activities. Data visualization can help us gain insight faster and allow us to make better decisions more quickly…
In order to consume these insights and make decisions, your process or operations has to support the speed. It is very possible that your speed to insight will outpace the actionability of those insights. If you are able to understand an event, but you are unable to take corrective action, the value of the insight is null, regardless of its potential impact. Many businesses chase after ‘real-time’ dashboards and invest a significant amount of resources into preparing information that can not be used operationally because the process it describes is not flexible enough to change in pace with the insight.
Speed is admirable, but sometimes it’s not necessarily required.
During the process of analysis, one of our main requirements we need to consider is the time. Not just deadlines to complete a task, but also the time in which an insight will need to be consumed. If you are conveying a complex idea that needs visualization as a one-time insight, we can spend more effort on the desired result. More often, in the business world, our analysis will be part of an on-going effort or monitoring process. In these cases, we have to pay particularly close attention to how expensive new data is in contrast to how valuable the insights gained will be.