If you torture the data long enough, it will confess [to anything].
In the next block of three chapters, I focus on the core activity of data analysts: analysis, focusing on the goals of analysis within the organization and how to do impactful analysis.
I’ll examine activities such as analyzing data, designing metrics, gaining insights, and presenting or selling those insights, ideas, and recommendations to the decision-makers. Chapter 6 covers the design of metrics and key performance indicators (KPIs), and Chapter 7 focuses on data visualization and storytelling. This first chapter of the trio, however, focuses on analysis itself.
Importantly, it does not cover how to perform analysis or statistical inference because there are many better texts available that do that (see Further Reading). Instead, it considers the goal of the analyst: what does it mean for an analyst to analyze? What are they trying to achieve? What tools do they have in their toolkit? I’ll bring back the idea of levels of analytics from Chapter 1 and introduce some other perspectives on types of analysis.
The goal here is to highlight the range of statistical and visualization tools that are available to analysts to glean insights from data. A secondary goal is to urge analysts to use the appropriate tools and, where necessary, to learn more sophisticated tools that can provide a deeper level of understanding of the problem at hand.
A fine woodworker making a wooden ...