Book Summary: Storytelling with Data by Cole Knaflic

Context

  • Who is the decision maker?
  • What’s your relationship with the audience?
  • Why they should care?
  • Check Big Idea by Nancy Duarte — so-what in a single sentence
  • What is the desired outcome?
  • Data is the supporting evidence of the story you will tell
  • Use tactics: 3-minute story and storyboarding (use Post-it to rearrange)

Charts

  • When reducing from multiple numbers down to a single one, think about what context may be lost
  • Great to a mixed audience who will each look for their particular row of interest
  • Not a good idea for live presentation
  • Always include a legend
  • Show relative increases and decreases or differences across various categories between the two data points
  • Go-to graph type for categorical data
  • Must have a zero baseline
  • Pull apart the pieces of a stacked bar chart to focus on one at a time
  • To show increases and decreases, and the resulting ending point
  • Visualize portions of a whole on a scale, for example: survey data collected along a Likert scale
  • Visualize numbers of vastly different magnitudes
  • Pie chart (replace with a bar chart; *other opinions in data community say, however, pie chart can be used if for just few categories and can clearly represent the percentage)
  • 3D
  • Secondary y-axis

Visual Principles

  • Avoid cognitive loading, processing that takes up mental resources but doesn’t help the audience understand the information
  • Maximize data-ink by Edward Tufte
  • People usually start reading from upper left of a chart
  • If there is only one thing that is really important, making that the only thing on the page
  • The more things we make different, the lesser the degree to which any of them stand out
  • Help direct your audience’s attention to where you want them to focus it
  • Create a visual hierarchy of elements to lead your audience through the information you want to communicate in they way you want them to process it
  • People can keep about four chunks of visual information in their short-term memory at a given time
  • Use preattentive attributes for explanatory story analysis, not for exploratory analysis
  • Should always be an intentional decision; identify one or two brand-appropriate colors to use as your “audience-look-here” cues
  • Use varying color saturation of a single color (a heatmap)
  • Design with colorblind in mind — 8% of men and half a percent of women are colorblind
  • Bolding is generally preferred over italics and underlining

Storytelling

  • Unite an idea with an emotion, arousing the audience’s attention and energy
  • McKee “subjective expectation meets cruel reality” — an event that throws things out of balance
  • Don’t communicate for yourself — communicate for your audience. What motivates your audience?
  • Bing (intro)
  • Bang (content)
  • Bongo (conclusion): call to action; repeat (“here is what we covered”)

Tactics

  • Have action titles (not descriptive ones)
  • Get feedback from your friends and relatives — fresh eyes

--

--

--

Looking at tech from all aspects

Love podcasts or audiobooks? Learn on the go with our new app.

Recommended from Medium

The pinnacle of data analysis in ticket sales

Understanding Linear Regression

Quantitative, Qualitative or Maybe Both?

Steps You Need To Get a Data Science Job

Anomaly Detection using Angle-based techniques

Inferential Statistics

How Much Do Data Scientists Make Part 2

Get the Medium app

A button that says 'Download on the App Store', and if clicked it will lead you to the iOS App store
A button that says 'Get it on, Google Play', and if clicked it will lead you to the Google Play store
Sylvia X

Sylvia X

Looking at tech from all aspects

More from Medium

My First Business Analytics Project - Hotel Booking Cancelation Analyze Part 1

What the data looks like when we import

How to Turn Data into Storytelling

Using Data to Analyze the Difference Between Two Customer Types at a Ride Sharing Company in…

WHEN DATA MEET STORIES