book

Storytelling with Data: A Data Visualization Guide for Business Professionals

by Cole Nussbaumer Knaflic

November 2015

Beginner to intermediate

288 pages

5h 43m

English

Wiley

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Note
Bad graphs are everywhereWe aren’t naturally good at storytelling with dataWho this book is written forHow I learned to tell stories with dataHow you’ll learn to tell stories with data: 6 lessonsIllustrative examples span many industriesLessons are not tool specificHow this book is organized
Exploratory vs. explanatory analysisWho, what, and howWhoWhatHowWho, what, and how: illustrated by exampleConsulting for context: questions to askThe 3-minute story & Big IdeaStoryboardingIn closing
Simple textTablesGraphsPointsLinesBarsAreaOther types of graphsTo be avoidedIn closing
Cognitive loadClutterGestalt principles of visual perceptionLack of visual orderNon-strategic use of contrastDecluttering: step-by-stepIn closing
You see with your brainA brief lesson on memoryPreattentive attributes signal where to lookSizeColorPosition on pageIn closing
AffordancesAccessibilityAestheticsAcceptanceIn closing
Model visual #1: line graphModel visual #2: annotated line graph with forecastModel visual #3: 100% stacked barsModel visual #4: leveraging positive and negative stacked barsModel visual #5: horizontal stacked barsIn closing

The magic of storyConstructing the storyThe narrative structureThe power of repetitionTactics to help ensure that your story is clearIn closing
Lesson 1: understand the contextLesson 2: choose an appropriate displayLesson 3: eliminate clutterLesson 4: draw attention where you want your audience to focusLesson 5: think like a designerLesson 6: tell a storyIn closing
CASE STUDY 1: Color considerations with a dark backgroundCASE STUDY 2: Leveraging animation in the visuals you presentCASE STUDY 3: Logic in orderCASE STUDY 4: Strategies for avoiding the spaghetti graphCASE STUDY 5: Alternatives to piesIn closing
Where to go from hereBuilding storytelling with data competency in your team or organizationRecap: a quick look at all we’ve learnedIn closing

Content preview from Storytelling with Data: A Data Visualization Guide for Business Professionals

chapter 1 the importance of context

This may sound counterintuitive, but success in data visualization does not start with data visualization. Rather, before you begin down the path of creating a data visualization or communication, attention and time should be paid to understanding the context for the need to communicate. In this chapter, we will focus on understanding the important components of context and discuss some strategies to help set you up for success when it comes to communicating visually with data.

Exploratory vs. explanatory analysis

Before we get into the specifics of context, there is one important distinction to draw, between exploratory and explanatory analysis. Exploratory analysis is what you do to understand the data and figure out what might be noteworthy or interesting to highlight to others. When we do exploratory analysis, it’s like hunting for pearls in oysters. We might have to open 100 oysters (test 100 different hypotheses or look at the data in 100 different ways) to find perhaps two pearls. When we’re at the point of communicating our analysis to our audience, we really want to be in the explanatory space, meaning you have a specific thing you want to explain, a specific story you want to tell—probably about those two pearls.

Too often, people err and think it’s OK to show exploratory analysis (simply present the data, all 100 oysters) when they should be showing explanatory (taking the time to turn the data into information that can be consumed ...