Storytelling with Data: A Data Visualization Guide for Business Professionals

This book was recommended by a work colleague a number of years ago. It was a great and insightful read for someone who was an entry level analyst. Since I bought it I often find myself reverting back to many sections when I’m looking for help or need to check if what I’m doing is the best method for my audience. I would also recommended this to others new in analytical roles in a heartbeat.

None of this is rocket science but sometimes you just need a book written by an author that makes it so easy to follow and understand. Great guidance and powerful, clear examples. I’ve been passing this round my colleagues to help the, up there game a bit too.

This book has some good ideas in it and is straightforward enough that I think most people can get something from it. There are good real world examples, although I do think it's overpriced for the Kindle version.

If your job is to talk with people about numbers (any numbers: profit and loss, salary, election results, number of stars in the universe) then this book is for you. If you have created a chart to explain something, but not sure if people will understand what you're trying to tell them then this book is for you. If you work with Excel, Power BI or any other tool to visualize data, don't stop on readying books about ETL and Power Query, Excel formulas, data modeling and DAX. ETL, data modeling, formulas/calculations - all this steps you're doing for one reason - to make it possible and easy for you or other people to get useful insights from the data. To make data easy to understand, to extract useful insight you need to visualize the data. To visualize the data doesn't mean to drag&drop data fields onto a chart. It means to make meaningful visual representation of the data. And you need to read good books about data visualization to understand the concepts. And this is the first book to read. Easy to read book based on real life examples. This books will help you understand why colorful visualization is not the same as insightful visualization, why you need to think carefully about each line, each caption, each color on your data visualization. After reading this book you'll be crying looking at the thousands of colorful pie charts (with tens of similar size sectors colored using all possible colors) posted in the internet by people who know how to create a chart using a software, but don't understand (yet) what is the purpose of this data transformation into a visual object. Read the book, and make the world of data visualization better. If you need more practice, read another book of the same author - "Storytelling with Data: Let's Practice!". Cole is a great teacher of the science (and art) called data visualization.

Simple to read, lots of tips and information that can help anyone working with data visualisation.

It is difficult (if not impossible) to manage what cannot be measured. Yes, there are “intangibles” (e.g. empathy) that have unique value and importance. Quantifying the so-called “soft skills” (e.g. listening) also poses challenges. However, the fact remains that data visualization can enrich and extend a narrative’s impact, especially when the objective is persuasion or resolution rather than entertainment.What Cole Nussbaumer Knaflic provides in this volume is an abundance of information, insights, and counsel that can help almost anyone to master the skills needed, in Knaflic's words, “to visualize data and tell stories with it” in order to turn the data “into information that can be used to drive better decision making.”These are among the several dozen passages of greatest interest and value to me, also listed to suggest the scope of Knaflic’s coverage:o Thinking like a designer (Pages 15-16 and 127-150)o Importance of context (19-33)o Selecting visuals that will be effective (35-69)o Graphs (43-49)o Bar charts (50-59, 156-158, 161-162, and 236-237)o Visuals to avoid (61-68)o Voiding clutter (71-98)o Gestalt Principles of Visual Perception (74-81)o Lack of visual order (81-86)o Focusing on an audience’s attention (99-126)o Preattentive attributes (102-116)o Affordances (128-138)o Hierarchy of Information (135-138)o Accessibility (138-145)o Storytelling (165-185)o Storytelling with data process (187-205 and 242-255)o Case Study 2: Leveraging animation in the visuals you present (210-218)o Case Study 4: Strategies for avoiding the spaghetti graph (227-234)I agree with Knaflic: “There is a story in your data. But your tools don’t know what that story is. That’s where it takes you — the analyst or commentator of the information — to bring that stay visually and contextually to life. That process is the focus of this book.”These are the specific learning objectives on which she focuses, each preceded by “How to….”o Understand the context in which the story is presentedo Select an appropriate visual display of the datao Eliminate cluttero Focus attention where it is most neededo Think like a designero Tell the story (setting. characters, plot, conflicts, resolution, etc.)Presumably Cole Nussbaumer Knaflic agrees with me that the most effective storytellers are aware of an unspoken question that every member of the given audience has in mind: “Why should I care?” or perhaps “What’s in it for me?” The story format will help to engage their interest but there must also be substantive support of the message. That’s where the data component is decisive, for better or worse. If you need help with creating visualizations “that are thoughtfully designed to impart information and incite action,” look no further.

I was recommended this book, but unfortunately, it doesn't live up to expectations. This will be helpful for you if you have no common sense and have never met a good graph before. If you're looking to improve from creating basic graphs, this won't help you. The overviews of the different graph types were basic, I would argue many of the author's examples of a 'good graph' could still be improved, she tries to argue pie charts are rubbish through showing an awful 3D pie chart, then explaining 3D in itself is rubbish (duh!). Then also tries to argue for ethics in data on the principle that you shouldn't mislead because you'll be figured out by a smart audience member (not because it's principally wrong). Big book with big text, not much substance and will be reselling. Would highly recommend 'Better Data Visualizations' by Jonathan Schwabish instead.