I am currently reading a book called Effective Data Storytelling: How to Drive Change With Data, Narrative, and Visuals written by Brent Dykes. I found it quite enlightening, and want to write summary on each chapters for my own note taking, and at the same time, sharing with all of you.

The book is not about how to do data analysis, statistics, or machine learning. It is a book, in my opinion, about communication. How one can communicate insights effectively to your audience.

Why we need effective communication techniques? If you are more “academic”, you may think data is data. It is fact. If you present the fact or observations based on data to your audience, they should be accepting it. It is difficult to argue with numbers. The author points out that this is usually not the case since people and organization aren’t always open to new findings deliberately or unintentionally. I think it has something to do with human psychology, for example, we tend not to admit we are wrong, and we tend to resist risk, change, and uncertainty. These are the reasons why we need communication techniques to be persuasive (we are dealing with human, not machine).

The solution is by telling data story in which the author is going to elaborate more on the coming chapters. The main idea in this chapter is that communication is different with inform. When you inform others, what you need to do is just to ensure they have received your message. When you communicate with others, you need to ensure they understand your message, have 2-way communication , and may touch their mind and heart on something.

In Chapter 1, the author draws something called the analytics path to value which summarizes the steps going from data to value creation.

ItemDescription
DataRaw materials
InformationSummary or reports created
InsightsAn unexpected shift into the way we understand things that potentially inspires us to do things differently.
DecisionMaking a choice/ commitment to take actions
ActionChanging/ do things differently
ValueBenefits, such as profits

I think the book is not only for data professionals, but basically everyone. A data professional, who knows awesome machine learning algorithms and builds great models, may not be able to drive change and create value if he cannot communicate well. On the contrary, a layman who just knows basic data summary techniques (mean, max, median, trend) and can communicate his findings well, may drive bigger changes and create more values.

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