When you’ve got a good visualization, people get it right away and you get a conversation going. You get feedback. It accelerates productivity. It’s far better than talking on the phone or sending email. You instantly convey the same idea to many minds.
In the previous two chapters, I covered types of analysis, ranging from descriptive to causal, and the design of metrics including the especially important subset, KPIs. In this chapter, we move further along the analytics value chain: packaging up the findings, insights, and recommendations to present to the decision makers and other stakeholders, to increase the quality of discussion and decision making at every level.
This chapter is a relatively high-level overview of the process and objective of communicating analytics output in a data-driven organization—the why and the what of communicating data rather than the how. I’ll cover the prerequisites; that is, what you need to think about before you even start preparing a presentation or visualization. To be more concrete, I will also cover a chart chooser and data-visualization checklist, letting those two, and the source references more or less speak for themselves. That will leave space to cover presentation-level remarks, such as overall structure and focusing the message.
“Every dataset, every database, every spreadsheet has a story to tell,” says Stuart Frankel, CEO of Narrative Science. An analyst’s ...