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

R for Business Managers - Simple Data Manipulations

Data Pre-processing tasks using python

Stock Market Outcomes Are Currently Bernoulli Distributed

Sampling Methodology In Research Proposal

Lost In (Machine) Translation

Forecasting Daily New Confirmed COVID-19 Cases in Maldives — Part 1

Azure Databricks Deep Dive Part I: Integration with Cognitive Service

Surveying the Economic Impact of COVID-19 in Africa

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

Converting Non-Date Time Dimensions in Tableau

Day 21: Sankey Diagrams & the Paradox of Choice

Save Historical Data in the Power BI Service with XMLA Endpoint (Incremental Refresh on ANY Source!)

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

What the data looks like when we import