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Here are potential matches between the evidence and charts: 1. Quarterly revenue comparison for star products - Line Chart 2. States with the lowest level of inequality - Choropleth 3. Market share of our new deodorant - Pie Chart 4. Correlation between obesity and consumption of doughnuts - Scatterplot 5. My income compared against everyone in Malaysia - Histogram The line chart is best for showing changes over time, which is needed for the quarterly revenue comparison. A choropleth map can show values for different states/regions. A pie chart works well for comparing relative market shares. A scatterplot allows viewing correlations between two variables. And a histogram can show the distribution of one

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Storytelling with Data Day 3
About this Day Data Storytelling is combination of a strong narrative and an effective visual Data Storytelling Narrative (Morning) Visual (Afternoon)
01 02 03 04 05 06 Introduction to Data Storytelling Narrative: 30-Second Story Narrative: Consider the Audience & Call to Action Visual: Choosing a Chart Visual: Design Aesthetics Visual: Data Visualization Best Practice About this Course: Table of Contents Data Storytelling is a 2-day Course
Storytelling with Data Morning
Introduction to Data Storytelling • What is Data Storytelling? • Business Impact of Data Storytelling • About this Course • Learning how to Learn Chapter 1
What is Data Storytelling? Data Storytelling is growing in importance – but we don’t have a formal definition yet “Complex ideas communicated with clarity, precision and efficiency.” – Edward Tufte author of “The Visual Display of Quantitative Information, 1983 “Data stories explore and explain how and why data changes over time, usually through a series of linked visualizations” – Gartner, 2018 “Data storytelling is the process of translating data analyses into layman's terms in order to influence a business decision or action” – SearchCIO, 2015
Making Data Mean More Through Storytelling Video: 5 minutes
So, how do I learn Data Storytelling?
Learning How to Learn – (1) Dissect Model Visuals Why does this chart/visual work so well?
Learning How to Learn – (2) Dissect Poor Visuals What makes this a poor visual? Source: http://www.perceptualedge.com/example19.php Go to the source to read Stephen Few’s full dissection of why this is not a good chart • Which represents a larger amount: Mortgage Lending 2007 or NHS? • How much greater is Mortgage Lending 2007 than State Pensions? • Does State Pensions compared to Tesco Revenue look like the difference between 62 and 59, or much greater? • Which is bigger: Income Support or Police?
Learning How to Learn – (2) Dissect Poor Visuals What makes this a poor visual?
Learning How to Learn – (3) Makeover Poor Visuals The best way to learn is to practice Source: https://www.makeovermonday.co.uk/makeovers/
Learning How to Learn – (3) Makeover Poor Visuals The best way to learn is to practice Source: https://www.makeovermonday.co.uk/makeovers/
Learning How to Learn – (3) Makeover Poor Visuals The best way to learn is to practice Source: https://www.makeovermonday.co.uk/makeovers/
Oil & Gas Examples
What is this visual trying to tell us? BP 2017 Annual Report
Beautiful Visual without a cohesive story Too much information, too small a space u Cognitive Overload v Uninformative Title w Proportions not comparable x Axis not clear
Suggested re-design – Refine the message Make ’Renewables’ the focus of the visual & build a visual hierarchy
What is this visual trying to tell us? New Zealand Oil & Gas 2015 Annual Report
What is this visual trying to tell us? New Zealand Oil & Gas 2015 Annual Report u Segments not comparable v Uninformative Title w Confusing color scheme x Wasted space on axes
Learning Resources 1. Makeover Mondays: makeovermonday.co.uk 2. BBC Visual and Data Journalism: https://www.bbc.com/news/world-32209370 3. Tableau Public Gallery: https://public.tableau.com 4. Perceptual Edge: http://www.perceptualedge.com/ 5. https://viz.wtf/ Theory is covered in this course but practice is what solidifies learning
End of Chapter Activity – (30 minutes) Spend 10 minutes to think back to your work or personal life: • What is the last data story that you may have tried to tell? • Describe the situation • How effective was the story? • What do you think could have been improved? One or two participants will be asked to share their experience with the class What is the last data story you might have tried to tell?
Narrative: 30-Second Story • Point, Evidence, Explanation • Pearls of Insight • What am I trying to say? • End of Chapter Activity Chapter 2
Pearls of Insight Show the pearls, not the clams
Point, Evidence, Explanation - Point • Most data is still processed as a table • We can easily be tempted to show them all the clams we picked up (rows & columns) • Remember! Only the pearls are valuable (insight) Exercise What can you tell from the table about the three categories? It’s simple to forget the fundamentals when overwhelmed by Big Data
I think that the data shows me…
Point, Evidence, Explanation – Evidence What did you think about the three categories? Lets flesh out some of the concepts discussed Where is the evidence to support the argument or statement that you have made?
Point, Evidence, Explanation – Evidence The evidence is your data!
It’s not just my opinion – the data says so! Remember – you aren’t always there to explain the visual
Point, Evidence, Explanation – Evidence Explanation is your commentary!
What am I trying to say? The “So What” Test – Don’t spend more than 10 seconds to think of the “So What”
What am I trying to say? The “So What” Test
End of Chapter Activity – (1 hour) • Download and take a look at your data of choice • Do some simple initial analysis and answer some of the following questions • What kind of narrative can I build on this? • What would be the point of my story? • Is there evidence in the data to prove this? • What kind of explanation would I have to provide? One or two participants will be required to share their examples of P-E-E with the class Remember – sift through all the clams to find the pearls of insight
End of Chapter Activity – (1 hour) Example from “Electricity Generation Mix” Dataset • What kind of narrative can I build on this? • What would be the point of my story? • Is there evidence in the data to prove this? • What kind of explanation would I have to provide? • Data is in GWh – A quick Google search reveals that this means GigaWatt hours • There are six different categories of fuel sources • There is data for the years 1990 – 2017 • I could either focus on one of the energy sources or I could look at the overall consumption pattern over time • I work for an oil and gas company – I might be more interested in those data points? • The total energy generation could also be interesting as an indicator of market supply
End of Chapter Activity – (1 hour) Example from “Electricity Generation Mix” Dataset • What kind of narrative can I build on this? • What would be the point of my story? • Is there evidence in the data to prove this? • What kind of explanation would I have to provide? • I could talk about the pattern for all five energy types • I could show the audience all the energy categories and the total to allow them to make their own conclusions • Maybe I should focus on the oil & gas fuels since those are the ones my company is most concerned about • Should I focus on growth over time or relative proportion in a given year? • I will focus on the rise and fall of gas as a fuel for electricity generation
End of Chapter Activity – (1 hour) Example from “Electricity Generation Mix” Dataset • What kind of narrative can I build on this? • What would be the point of my story? • Is there evidence in the data to prove this? • What kind of explanation would I have to provide? 0 10000 20000 30000 40000 50000 60000 70000 80000 1990199119921993199419951996199719981999200020012002200320042005200620072008200920102011201220132014201520162017 Chart Title Hydro Gas Coal Oil Diesel Others
End of Chapter Activity – (1 hour) Example from “Electricity Generation Mix” Dataset • What kind of narrative can I build on this? • What would be the point of my story? • Is there evidence in the data to prove this? • What kind of explanation would I have to provide? • Why are gas and coal such important fuel for electricity generation in Malaysia? • Did anything happen in 1997 and 2011 to cause a drop in use of gas as a fuel type? Some quick research reveals the following potential causes: • The Asian financial crisis in 1997 could be a major contributing factor to a fall in fuel usage • Malaysia produced a lot less petroleum in 2011 (down 10.3%) could the lower supply have caused the drop in popularity as a fuel in that year?
Narrative: Consider the Audience & Call to Action • Selfish Storytellers • Who is my audience? • Bias & Objectivity • End of Chapter Activity Chapter 3
Selfish Storytelling Remember that stories are meant to be shared
Who is my audience? These personas help when tailoring a message to a particular audience Source: https://hbr.org/2013/04/how-to-tell-a-story-with-data
Building a Persona of the Audience Questions to frame your narrative • Who is this person? • She is detail-oriented • She is time-poor • She is higher management and mostly concerned with financials – not a technical person • What is this person trying to accomplish? • She wants to understand the trend of crude oil prices over the past 5 years • She recently entered the role and wants to prove herself with quick wins • What is a pain point for this person? • She is under pressure to decide if the company should continue with its off-shore exploration activities but has to do so with limited information available • How might this person resist? • She might not believe in analysis results because her gut instinct told her otherwise • She does not trust the opinion of her lower level analysts
Exercise: Think of a recent audience for your data story Answer the questions below • Who is this person? • What is this person trying to accomplish? • What is a pain point for this person? • How might this person resist?
Avoid Bias: Be Objective & Offer Balance • Avoid misleading audience about the underlying data • Viewers & Decision Makers will eventually find inconsistencies which will cause the designer to lose trust & credibility, even with a great story Avoid misleading the audience
Avoid Bias: Be Objective & Offer Balance (Examples) Avoid misleading the audience
How to Guide: Misleading the audience (1) 1. Having a non-zero baseline on a bar chart without informing the audience 2. Using 3D effects to skew visual perception of a graph 3. Selectively removing outliers for a “smoother” visual 4. Inconsistent time intervals to hide patterns in time-series data 5. Arbitrary maximum or minimum values to obscure data Many ways to achieve the same outcome
How to Guide: Misleading the audience (2) 1. Having a non-zero baseline on a bar chart without informing the audience 2. Using 3D effects to skew visual perception of a graph 3. Selectively removing outliers for a “smoother” visual 4. Inconsistent time intervals to hide patterns in time-series data 5. Arbitrary maximum or minimum values to obscure data Many ways to achieve the same outcome
How to Guide: Misleading the audience (3) 1. Having a non-zero baseline on a bar chart without informing the audience 2. Using 3D effects to skew visual perception of a graph 3. Selectively removing outliers for a “smoother” visual 4. Inconsistent time intervals to hide patterns in time-series data 5. Arbitrary maximum or minimum values to obscure data Many ways to achieve the same outcome
How to Guide: Misleading the audience (4) 1. Having a non-zero baseline on a bar chart without informing the audience 2. Using 3D effects to skew visual perception of a graph 3. Selectively removing outliers for a “smoother” visual 4. Inconsistent time intervals to hide patterns in time-series data 5. Arbitrary maximum or minimum values to obscure data Many ways to achieve the same outcome
How to Guide: Misleading the audience (5) 1. Having a non-zero baseline on a bar chart without informing the audience 2. Using 3D effects to skew visual perception of a graph 3. Selectively removing outliers for a “smoother” visual 4. Inconsistent time intervals to hide patterns in time-series data 5. Arbitrary maximum or minimum values to obscure data Many ways to achieve the same outcome
Call-to-Action Call to actions instruct the audience on what is expected of them having seen the visual Call-to-actions are most effective when they: • Use action words • Articulate the consequences of non- action • Instill Urgency
Action Words Call-to-actions can be scary to implement – at worst you start a dialog, at best you start change
Storytelling with Data Afternoon
Visual: Choosing a Chart • Information Encoding • What information am I trying to convey? • Data Storyteller’s Toolbox • End of Chapter Activity Chapter 4
McGill & Cleveland (1984) Elementary Perceptual Tasks – The study of human efficiency in perceiving different signals • Some charts are easier to understand than others – this is because the perceptual tasks involved are easier • Lets play a game to understand how we perceive difficulty in perceptual tasks
How much longer is A compared to B? Answer in multiples – 12 times! 20 times! A B 2X
How much steeper is A compared to B? Answer in multiples – 12 times! 20 times! 6X A B
How much larger is A compared to B? Answer in multiples – 12 times! 20 times! 10X A B
Perceptual Tasks Ranking Difficulty of perceptual task influences interpretability of charts
Charts should be selected based on precision required Different Perceptual Tasks are used in each chart
Perceptual Tasks on Charts One chart can sometimes have multiple information signals – primary and secondary Comparing length of bars - Easy Compare angle in the middle - Medium Difficulty Compare 2D area - Difficult
What am I trying to convey?
Recall from Morning – Point, Evidence, Explanation What is your evidence? Is it a change over time? Is it a distribution of numbers? A category comparison? Deviation Distribution Correlation Change over Time Ranking Magnitude Part-to- Whole Source: https://ft-interactive.github.io/visual-vocabulary/
Choosing a Visual: Change over Time Give emphasis to changing trends Deviation Distribution Correlation Change over Time Ranking Magnitude Part-to- whole Source: https://ft-interactive.github.io/visual-vocabulary/
Choosing a Visual: Part-to-Whole A bit like a recipe – how much is one group as a percentage of the entire population Deviation Distribution Correlation Change over Time Ranking Magnitude Part-to- Whole Source: https://ft-interactive.github.io/visual-vocabulary/
Choosing a Visual: Magnitude Show size comparisons – these can be relative or absolute Deviation Distribution Correlation Change over Time Ranking Magnitude Part-to- whole Source: https://ft-interactive.github.io/visual-vocabulary/
Exercise: Match the “Evidence” the to “Chart” – (10mins) Sometimes multiple charts can be used – justify! Bar Chart Pie Chart Stacked Bar Chart Choropleth Histogram Scatterplot Line Chart 1. Quarterly revenue comparison for star products 2. States with the lowest level of inequality 3. Market share of our new deodorant 4. Correlation between obesity and consumption of doughnuts 5. My income compared against everyone in Malaysia
Data Storyteller’s Toolbox – Line Chart 0 2 4 6 8 10 12 14 16 18 20 1-2017 7-2017 1-2018 7-2018 1-2019 7-2019 Sales Revenue (RM mil) Malaysian Refined Petroleum Revenue 2017-19 Eastern Region Revenues rose the most in Q1 2017 on the back of a government-led township development initiative in Sabah & Sarawak Do • Choose meaningful axes tick intervals • Label lines directly • Draw attention to lines of interest through use of color • Lighten gridlines to enable focus on data • Include annotations or labels to provide context Don’t • Create a spaghetti graph by keeping everything in multiple colors • Use a line chart for categorical data as the lines imply continuity Line Charts are best used for change over time Eastern Northern Central Southern Govt. Subsidy Introduced Eastern supply lines disrupted
Data Storyteller’s Toolbox – Bar Chart Do • Limit the number of sub-categories to three • Change the spacing between bars – visual grouping can have a big effect on perception • Use Data Labels if accuracy is needed • Include context such as a benchmark or average if necessary Don’t • Make the bars touch one another – that is a different chart altogether known as a histogram Bar Charts are used to compare between different categories 4.3 2.5 3.5 4.5 2.4 1.2 2.4 4.4 1.8 2.8 1.9 0.6 2.0 2.0 3.0 5.0 2.5 0.5 Oil Gas Renewables Hydro Coal Nuclear Billion tonnes of oil equivalent Malaysian Energy Consumption Mix 2019 Nuclear energy is an energy source that is being under-used in Malaysia due to the strict regulation around its production Northern Southern Central Previous Year Average
Bar Chart What went wrong here?
Data Storyteller’s Toolbox – Stacked 100% Bar Chart Do • Limit the number of sub-categories to three – the non-zero baseline makes it difficult to compare • Use data labels – this is a must for stacked bar charts • Add a meaningful scale to remind the audience that this is normalized data • Use a landscape orientation especially when category names are long Don’t • Use category labels at an angle – they are difficult to read • Overwhelm with too many super categories – remember cognitive overload Stacked bar charts compare categories & sub-categories 4.3 2.5 3.5 4.5 2.4 1.2 2.4 4.4 1.8 2.8 1.9 0.6 2 2 3 5 2.5 0.5 0% 20% 40% 60% 80% 100% Oil Gas Renewables Hydro Coal Nuclear Billion tonnes of oil equivalent Malaysian Energy Consumption Mix 2019 Nuclear energy is an energy source that is being under-used in Malaysia due to the strict regulation around its production Northern Southern Central
Stacked Bar Chart What went wrong here?
Data Storyteller’s Toolbox – Pie Chart / Donut Chart Do • Add Data Labels Directly onto the Chart • Use colors that are clearly distinguishable from one another • Try to limit the number of sub-categories being used • Choose an appropriate amount of white space in the middle of the donut Don’t • Use multiple layers of Donuts – this is not a Croissant • Add Donut Explosion • Use 3D effects – it doesn’t add any value to the message Compare Proportions to Whole Oil 2 Gas 2 Renewables 3 Hydro 5 Coal 2.5 Nuclear 0.5 Malaysian Energy Consumption Mix 2019 Nuclear energy is an energy source that is being under-used in Malaysia due to the strict regulation around its production [billion tonnes of oil equivalent]
Data Storyteller’s Toolbox – Impact Metrics Do • Use size & bolding for a visual hierarchy on your impact metric • Support the impact metric with sufficient context • Provide context through non-disruptive background images Don’t • Distract from the impact metric by using the same bold/size for supporting elements on the visual • Try to provide too much explanation – impact metrics by nature need to be short and sweet Report on success or key achievements/figures from a report https://www.theedgemarkets.com/article/bringing-talent-home
Data Storyteller’s Toolbox – Impact Metrics Report on success or key achievements/figures from a report Source: Storytelling with Data – Cole Nussbaumer Knafflic
Impact Metric What went wrong here?
Data Storyteller’s Toolbox – Tables / Heatmaps Do • Provide sufficient spacing between columns for easy interpretation • Use borders meaningfully – they do not always need to be included • Select alignment of text carefully – similar metrics should be aligned in the same way • Consider introducing sparklines, bars, heatmap coloring to break monotony of visual Don’t • Use bold/thick gridlines that obscure the data being presented • Present a table with too many rows & columns – remember the audience wants the pearls not the clams • Include unnecessary decimal points Tables and Heatmaps should be avoided in presentations but can be used as last resort
Tables / Heatmaps What went wrong here? X Candy Central 15.1234% 123.1 0.4232 Y Apple Northern 20.5234% 198.1 3.134 C Jack Central / Eastern 10.461% 212.2 8.4123 G Tom Southern / Central 25.134% 432.5 0.983
Data Storyteller’s Toolbox – Geographic Maps Do • Limit to just showing one kind of variable • Provide supporting elements such as a table or a bar chart below as an alternative to precision • Choose a color scale that is easy on the eyes (blue and oranges are recommended) • Consider adding a data label/annotation where appropriate • Normalize the data before plotting – absolute numbers might not visualize well Don’t • Use Choropleths alone to monitor subtle differences (color scale not easy for human eye) • Choose a confusing color scheme such as red-purple-blue Great to show clear regional patterns in the data Malaysian Unemployment Rates (2017) Northern states experienced highest rates of unemployment in 2019 Local economy recession in Northern Regions
Geographic Maps / Choropleths What went wrong here?
Example – Malaysia Electricity Generation Fuel Mix Choosing an appropriate chart 0 10000 20000 30000 40000 50000 60000 70000 80000 1990199119921993199419951996199719981999200020012002200320042005200620072008200920102011201220132014201520162017 Future of Malaysian Electricity Generation is Hydroelectric Proposal to build water dams in East Malaysia will generate 10,000 GwH of Hydroelectricity a year saving the government an estimated RM 400,000 monthly through cheaper energy. Hydro Gas Coal Oil Diesel Others
Visual: Data Visualization Best Practice • Chart Junk • Visual Hierarchy • Leveraging Color • End of Chapter Activity Chapter 5
Less is More: Eliminating Chart Junk Maximizing the amount of signal sending “data ink” on the visual “Good graphical representations maximize data-ink and erase as much non-data-ink as possible,” Edward Tufte - author of Visual Display of Quantitative Information (1983) Ink used to send information to audience Total Ink Used Data-Ink Ratio
Cognitive Load • Every element added to a blank screen takes up cognitive load for the audience • Our brain has limited “space” to process information • The reason why we didn’t like to look at the previous visual is that there is excessive cognitive load • Cognitive load culprits: • Clutter • Lack of Visual Hierarchy Minimize the amount of “Brain Space” being used by your visual
Example of Poor Data to Ink Ratio Repeated content is not always bad – but it should be done purposefully Example of non-data ink • Legend repeats information already on axis • Bananas in background distracting and doesn’t send information • Extra decimal places • 3D effect doesn’t add any additional information as all bars are same width
Visual: Data Visualization Step by Step
Guided Redesign Less is more!
Guided Redesign Less is more!
Guided Redesign Less is more!
Guided Redesign Less is more!
Guided Redesign Less is more!
Guided Redesign Less is more!
Guided Redesign Less is more!
Guided Redesign Less is more!
Guided Redesign Less is more!
Guided Redesign Less is more! section 03
Guided Redesign Less is more!
Visual: Data Visualization Principles
Visual Hierarchy How many 3’s in the visual? Source: Storytelling with Data (pg.103)
Visual Hierarchy How many 3’s in the visual? Source: Storytelling with Data (pg.103)
Exercise: What did you see first? Building a Visual Hierarchy The order I viewed the elements is: 01. ________________ 02. ________________ 03. ________________ 04. ________________ 05. ________________ 06. ________________ Source: Data Points, Nathan Yau
Visual Hierarchy: How-to-Guide 12 Visual Hierarchy Design Tips 1. Size & Scale 2. Color & Contrast 3. Typographic Hierarchy 4. Spacing 5. Proximity 6. Negative Space 7. Alignment 8. Rule of Odds 9. Repetition 10. Leading Lines 11. Rules of Thirds 12. Perspective Source: https://visme.co/blog/visual-hierarchy/
Visual Hierarchy: How-to-Guide 12 Visual Hierarchy Design Tips 1. Size & Scale 2. Color & Contrast 3. Typographic Hierarchy 4. Spacing 5. Proximity 6. Negative Space 7. Alignment 8. Rule of Odds 9. Repetition 10. Leading Lines 11. Rules of Thirds 12. Perspective
Visual Hierarchy: How-to-Guide 12 Visual Hierarchy Design Tips 1. Size & Scale 2. Color & Contrast 3. Typographic Hierarchy 4. Spacing 5. Proximity 6. Negative Space 7. Alignment 8. Rule of Odds 9. Repetition 10. Leading Lines 11. Rules of Thirds 12. Perspective
Visual Hierarchy: How-to-Guide 12 Visual Hierarchy Design Tips 1. Size & Scale 2. Color & Contrast 3. Typographic Hierarchy 4. Spacing 5. Proximity 6. Negative Space 7. Alignment 8. Rule of Odds 9. Repetition 10. Leading Lines 11. Rules of Thirds 12. Perspective
Visual Hierarchy: How-to-Guide 12 Visual Hierarchy Design Tips 1. Size & Scale 2. Color & Contrast 3. Typographic Hierarchy 4. Spacing 5. Proximity 6. Negative Space 7. Alignment 8. Rule of Odds 9. Repetition 10. Leading Lines 11. Rules of Thirds 12. Perspective
Visual Hierarchy: How-to-Guide 12 Visual Hierarchy Design Tips 1. Size & Scale 2. Color & Contrast 3. Typographic Hierarchy 4. Spacing 5. Proximity 6. Negative Space 7. Alignment 8. Rule of Odds 9. Repetition 10. Leading Lines 11. Rules of Thirds 12. Perspective
Visual Hierarchy: How-to-Guide 12 Visual Hierarchy Design Tips 1. Size & Scale 2. Color & Contrast 3. Typographic Hierarchy 4. Spacing 5. Proximity 6. Negative Space 7. Alignment 8. Rule of Odds 9. Repetition 10. Leading Lines 11. Rules of Thirds 12. Perspective
Visual Hierarchy: How-to-Guide 12 Visual Hierarchy Design Tips 1. Size & Scale 2. Color & Contrast 3. Typographic Hierarchy 4. Spacing 5. Proximity 6. Negative Space 7. Alignment 8. Rule of Odds 9. Repetition 10. Leading Lines 11. Rules of Thirds 12. Perspective
Visual Hierarchy: How-to-Guide 12 Visual Hierarchy Design Tips 1. Size & Scale 2. Color & Contrast 3. Typographic Hierarchy 4. Spacing 5. Proximity 6. Negative Space 7. Alignment 8. Rule of Odds 9. Repetition 10. Leading Lines 11. Rules of Thirds 12. Perspective
Visual Hierarchy: How-to-Guide 12 Visual Hierarchy Design Tips 1. Size & Scale 2. Color & Contrast 3. Typographic Hierarchy 4. Spacing 5. Proximity 6. Negative Space 7. Alignment 8. Rule of Odds 9. Repetition 10. Leading Lines 11. Rules of Thirds 12. Perspective
Visual Hierarchy: How-to-Guide 12 Visual Hierarchy Design Tips 1. Size & Scale 2. Color & Contrast 3. Typographic Hierarchy 4. Spacing 5. Proximity 6. Negative Space 7. Alignment 8. Rule of Odds 9. Repetition 10. Leading Lines 11. Rules of Thirds 12. Perspective
Visual Hierarchy: How-to-Guide 12 Visual Hierarchy Design Tips 1. Size & Scale 2. Color & Contrast 3. Typographic Hierarchy 4. Spacing 5. Proximity 6. Negative Space 7. Alignment 8. Rule of Odds 9. Repetition 10. Leading Lines 11. Rules of Thirds 12. Perspective
Using Color Purposefully for Storytelling Tips on using Color 1. Reduce Color Saturation 2. Beware Existing Color Associations 3. Reduce Cognitive Load Color is an information encoder – we need to know how to use it
Using Color Purposefully for Storytelling Tips on using Color 1. Reduce Color Saturation 2. Beware Existing Color Associations 3. Reduce Cognitive Load 4. Complimentary Colors Color is an information encoder – we need to know how to use it https://playfairdata.com/3-storytelling-with-color-tips-to-improve-your-data-visualization/
Using Color Purposefully for Storytelling Tips on using Color 1. Reduce Color Saturation 2. Beware Existing Color Associations 3. Reduce Cognitive Load 4. Complimentary Colors Color is an information encoder – we need to know how to use it https://playfairdata.com/3-storytelling-with-color-tips-to-improve-your-data-visualization/
Using Color Purposefully for Storytelling Tips on using Color 1. Reduce Color Saturation 2. Beware Existing Color Associations 3. Reduce Cognitive Load 4. Complimentary Colors Color is an information encoder – we need to know how to use it
Using Color Purposefully for Storytelling Tips on using Color 1. Reduce Color Saturation 2. Beware Existing Color Associations 3. Reduce Cognitive Load 4. Complimentary Colors Color is an information encoder – we need to know how to use it
Using Color Purposefully for Storytelling Tips on using Color 1. Reduce Color Saturation 2. Beware Existing Color Associations 3. Reduce Cognitive Load 4. Complimentary Colors Color is an information encoder – we need to know how to use it
Note: Color blindness affects green & red most often Try to choose orange and blue colors instead
End of Day Activity – (2 hours) Task Redesign the visual from before. Remove chart junk and justify the use of every element on the visual – if it isn’t needed then remove it! Redesign the visual to have a strong visual hierarchy using the concepts discussed One or two will be required to share their findings with the audience Remember – sift through all the clams to find the pearls of insight
Example – Malaysia Electricity Generation Fuel Mix Building a visual hierarchy 0 10 20 30 40 50 60 70 80 1990 1995 2000 2005 2010 2015 Thousands Future of Malaysian Electricity Generation is Hydroelectric Proposal to build water dams in East Malaysia will generate 10,000 GwH of Hydroelectricity a year saving the government an estimated RM 400,000 monthly through cheaper energy. Coal Gas Hydro Others Diesel Oil Based on data from the Department of Statistics, Malaysia 1990-2017 1997 Asian financial crash led to a dramatic fall in local energy demand 2011 production of Gas was down 10.3% compared to the previous year Hydro has had a 400% increase in 2017 compared to 2016
Additional Exercise
Redesign this table using the Data Storytelling Process Start with the Narrative, and with the Visual Survey of student’s interest in science both before and after attending a pilot summer science camp
Sample Submissions Adapted from Storytelling with Data – Cole Nussbaumer Knafflic
Sample Submissions Adapted from Storytelling with Data – Cole Nussbaumer Knafflic
Sample Submissions Adapted from Storytelling with Data – Cole Nussbaumer Knafflic
Sample Submissions Adapted from Storytelling with Data – Cole Nussbaumer Knafflic
Sample Submissions Adapted from Storytelling with Data – Cole Nussbaumer Knafflic
Summary

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dataDay3storytelling

  • 1.
  • 2.
    About this Day DataStorytelling is combination of a strong narrative and an effective visual Data Storytelling Narrative (Morning) Visual (Afternoon)
  • 3.
    01 02 03 04 05 06 Introduction to Data Storytelling Narrative:30-Second Story Narrative: Consider the Audience & Call to Action Visual: Choosing a Chart Visual: Design Aesthetics Visual: Data Visualization Best Practice About this Course: Table of Contents Data Storytelling is a 2-day Course
  • 4.
  • 5.
    Introduction to Data Storytelling •What is Data Storytelling? • Business Impact of Data Storytelling • About this Course • Learning how to Learn Chapter 1
  • 6.
    What is DataStorytelling? Data Storytelling is growing in importance – but we don’t have a formal definition yet “Complex ideas communicated with clarity, precision and efficiency.” – Edward Tufte author of “The Visual Display of Quantitative Information, 1983 “Data stories explore and explain how and why data changes over time, usually through a series of linked visualizations” – Gartner, 2018 “Data storytelling is the process of translating data analyses into layman's terms in order to influence a business decision or action” – SearchCIO, 2015
  • 7.
    Making Data MeanMore Through Storytelling Video: 5 minutes
  • 8.
    So, how doI learn Data Storytelling?
  • 9.
    Learning How toLearn – (1) Dissect Model Visuals Why does this chart/visual work so well?
  • 10.
    Learning How toLearn – (2) Dissect Poor Visuals What makes this a poor visual? Source: http://www.perceptualedge.com/example19.php Go to the source to read Stephen Few’s full dissection of why this is not a good chart • Which represents a larger amount: Mortgage Lending 2007 or NHS? • How much greater is Mortgage Lending 2007 than State Pensions? • Does State Pensions compared to Tesco Revenue look like the difference between 62 and 59, or much greater? • Which is bigger: Income Support or Police?
  • 11.
    Learning How toLearn – (2) Dissect Poor Visuals What makes this a poor visual?
  • 12.
    Learning How toLearn – (3) Makeover Poor Visuals The best way to learn is to practice Source: https://www.makeovermonday.co.uk/makeovers/
  • 13.
    Learning How toLearn – (3) Makeover Poor Visuals The best way to learn is to practice Source: https://www.makeovermonday.co.uk/makeovers/
  • 14.
    Learning How toLearn – (3) Makeover Poor Visuals The best way to learn is to practice Source: https://www.makeovermonday.co.uk/makeovers/
  • 15.
    Oil & GasExamples
  • 16.
    What is thisvisual trying to tell us? BP 2017 Annual Report
  • 17.
    Beautiful Visual withouta cohesive story Too much information, too small a space u Cognitive Overload v Uninformative Title w Proportions not comparable x Axis not clear
  • 18.
    Suggested re-design –Refine the message Make ’Renewables’ the focus of the visual & build a visual hierarchy
  • 19.
    What is thisvisual trying to tell us? New Zealand Oil & Gas 2015 Annual Report
  • 20.
    What is thisvisual trying to tell us? New Zealand Oil & Gas 2015 Annual Report u Segments not comparable v Uninformative Title w Confusing color scheme x Wasted space on axes
  • 21.
    Learning Resources 1. MakeoverMondays: makeovermonday.co.uk 2. BBC Visual and Data Journalism: https://www.bbc.com/news/world-32209370 3. Tableau Public Gallery: https://public.tableau.com 4. Perceptual Edge: http://www.perceptualedge.com/ 5. https://viz.wtf/ Theory is covered in this course but practice is what solidifies learning
  • 22.
    End of ChapterActivity – (30 minutes) Spend 10 minutes to think back to your work or personal life: • What is the last data story that you may have tried to tell? • Describe the situation • How effective was the story? • What do you think could have been improved? One or two participants will be asked to share their experience with the class What is the last data story you might have tried to tell?
  • 23.
    Narrative: 30-Second Story • Point,Evidence, Explanation • Pearls of Insight • What am I trying to say? • End of Chapter Activity Chapter 2
  • 24.
    Pearls of Insight Showthe pearls, not the clams
  • 25.
    Point, Evidence, Explanation- Point • Most data is still processed as a table • We can easily be tempted to show them all the clams we picked up (rows & columns) • Remember! Only the pearls are valuable (insight) Exercise What can you tell from the table about the three categories? It’s simple to forget the fundamentals when overwhelmed by Big Data
  • 26.
    I think thatthe data shows me…
  • 27.
    Point, Evidence, Explanation– Evidence What did you think about the three categories? Lets flesh out some of the concepts discussed Where is the evidence to support the argument or statement that you have made?
  • 28.
    Point, Evidence, Explanation– Evidence The evidence is your data!
  • 29.
    It’s not justmy opinion – the data says so! Remember – you aren’t always there to explain the visual
  • 30.
    Point, Evidence, Explanation– Evidence Explanation is your commentary!
  • 31.
    What am Itrying to say? The “So What” Test – Don’t spend more than 10 seconds to think of the “So What”
  • 32.
    What am Itrying to say? The “So What” Test
  • 33.
    End of ChapterActivity – (1 hour) • Download and take a look at your data of choice • Do some simple initial analysis and answer some of the following questions • What kind of narrative can I build on this? • What would be the point of my story? • Is there evidence in the data to prove this? • What kind of explanation would I have to provide? One or two participants will be required to share their examples of P-E-E with the class Remember – sift through all the clams to find the pearls of insight
  • 34.
    End of ChapterActivity – (1 hour) Example from “Electricity Generation Mix” Dataset • What kind of narrative can I build on this? • What would be the point of my story? • Is there evidence in the data to prove this? • What kind of explanation would I have to provide? • Data is in GWh – A quick Google search reveals that this means GigaWatt hours • There are six different categories of fuel sources • There is data for the years 1990 – 2017 • I could either focus on one of the energy sources or I could look at the overall consumption pattern over time • I work for an oil and gas company – I might be more interested in those data points? • The total energy generation could also be interesting as an indicator of market supply
  • 35.
    End of ChapterActivity – (1 hour) Example from “Electricity Generation Mix” Dataset • What kind of narrative can I build on this? • What would be the point of my story? • Is there evidence in the data to prove this? • What kind of explanation would I have to provide? • I could talk about the pattern for all five energy types • I could show the audience all the energy categories and the total to allow them to make their own conclusions • Maybe I should focus on the oil & gas fuels since those are the ones my company is most concerned about • Should I focus on growth over time or relative proportion in a given year? • I will focus on the rise and fall of gas as a fuel for electricity generation
  • 36.
    End of ChapterActivity – (1 hour) Example from “Electricity Generation Mix” Dataset • What kind of narrative can I build on this? • What would be the point of my story? • Is there evidence in the data to prove this? • What kind of explanation would I have to provide? 0 10000 20000 30000 40000 50000 60000 70000 80000 1990199119921993199419951996199719981999200020012002200320042005200620072008200920102011201220132014201520162017 Chart Title Hydro Gas Coal Oil Diesel Others
  • 37.
    End of ChapterActivity – (1 hour) Example from “Electricity Generation Mix” Dataset • What kind of narrative can I build on this? • What would be the point of my story? • Is there evidence in the data to prove this? • What kind of explanation would I have to provide? • Why are gas and coal such important fuel for electricity generation in Malaysia? • Did anything happen in 1997 and 2011 to cause a drop in use of gas as a fuel type? Some quick research reveals the following potential causes: • The Asian financial crisis in 1997 could be a major contributing factor to a fall in fuel usage • Malaysia produced a lot less petroleum in 2011 (down 10.3%) could the lower supply have caused the drop in popularity as a fuel in that year?
  • 38.
    Narrative: Consider the Audience &Call to Action • Selfish Storytellers • Who is my audience? • Bias & Objectivity • End of Chapter Activity Chapter 3
  • 39.
    Selfish Storytelling Remember thatstories are meant to be shared
  • 40.
    Who is myaudience? These personas help when tailoring a message to a particular audience Source: https://hbr.org/2013/04/how-to-tell-a-story-with-data
  • 41.
    Building a Personaof the Audience Questions to frame your narrative • Who is this person? • She is detail-oriented • She is time-poor • She is higher management and mostly concerned with financials – not a technical person • What is this person trying to accomplish? • She wants to understand the trend of crude oil prices over the past 5 years • She recently entered the role and wants to prove herself with quick wins • What is a pain point for this person? • She is under pressure to decide if the company should continue with its off-shore exploration activities but has to do so with limited information available • How might this person resist? • She might not believe in analysis results because her gut instinct told her otherwise • She does not trust the opinion of her lower level analysts
  • 42.
    Exercise: Think ofa recent audience for your data story Answer the questions below • Who is this person? • What is this person trying to accomplish? • What is a pain point for this person? • How might this person resist?
  • 43.
    Avoid Bias: BeObjective & Offer Balance • Avoid misleading audience about the underlying data • Viewers & Decision Makers will eventually find inconsistencies which will cause the designer to lose trust & credibility, even with a great story Avoid misleading the audience
  • 44.
    Avoid Bias: BeObjective & Offer Balance (Examples) Avoid misleading the audience
  • 45.
    How to Guide:Misleading the audience (1) 1. Having a non-zero baseline on a bar chart without informing the audience 2. Using 3D effects to skew visual perception of a graph 3. Selectively removing outliers for a “smoother” visual 4. Inconsistent time intervals to hide patterns in time-series data 5. Arbitrary maximum or minimum values to obscure data Many ways to achieve the same outcome
  • 46.
    How to Guide:Misleading the audience (2) 1. Having a non-zero baseline on a bar chart without informing the audience 2. Using 3D effects to skew visual perception of a graph 3. Selectively removing outliers for a “smoother” visual 4. Inconsistent time intervals to hide patterns in time-series data 5. Arbitrary maximum or minimum values to obscure data Many ways to achieve the same outcome
  • 47.
    How to Guide:Misleading the audience (3) 1. Having a non-zero baseline on a bar chart without informing the audience 2. Using 3D effects to skew visual perception of a graph 3. Selectively removing outliers for a “smoother” visual 4. Inconsistent time intervals to hide patterns in time-series data 5. Arbitrary maximum or minimum values to obscure data Many ways to achieve the same outcome
  • 48.
    How to Guide:Misleading the audience (4) 1. Having a non-zero baseline on a bar chart without informing the audience 2. Using 3D effects to skew visual perception of a graph 3. Selectively removing outliers for a “smoother” visual 4. Inconsistent time intervals to hide patterns in time-series data 5. Arbitrary maximum or minimum values to obscure data Many ways to achieve the same outcome
  • 49.
    How to Guide:Misleading the audience (5) 1. Having a non-zero baseline on a bar chart without informing the audience 2. Using 3D effects to skew visual perception of a graph 3. Selectively removing outliers for a “smoother” visual 4. Inconsistent time intervals to hide patterns in time-series data 5. Arbitrary maximum or minimum values to obscure data Many ways to achieve the same outcome
  • 50.
    Call-to-Action Call to actionsinstruct the audience on what is expected of them having seen the visual Call-to-actions are most effective when they: • Use action words • Articulate the consequences of non- action • Instill Urgency
  • 51.
    Action Words Call-to-actions canbe scary to implement – at worst you start a dialog, at best you start change
  • 52.
  • 53.
    Visual: Choosing a Chart •Information Encoding • What information am I trying to convey? • Data Storyteller’s Toolbox • End of Chapter Activity Chapter 4
  • 54.
    McGill & Cleveland(1984) Elementary Perceptual Tasks – The study of human efficiency in perceiving different signals • Some charts are easier to understand than others – this is because the perceptual tasks involved are easier • Lets play a game to understand how we perceive difficulty in perceptual tasks
  • 55.
    How much longeris A compared to B? Answer in multiples – 12 times! 20 times! A B 2X
  • 56.
    How much steeperis A compared to B? Answer in multiples – 12 times! 20 times! 6X A B
  • 57.
    How much largeris A compared to B? Answer in multiples – 12 times! 20 times! 10X A B
  • 58.
    Perceptual Tasks Ranking Difficultyof perceptual task influences interpretability of charts
  • 59.
    Charts should beselected based on precision required Different Perceptual Tasks are used in each chart
  • 60.
    Perceptual Tasks onCharts One chart can sometimes have multiple information signals – primary and secondary Comparing length of bars - Easy Compare angle in the middle - Medium Difficulty Compare 2D area - Difficult
  • 61.
    What am Itrying to convey?
  • 62.
    Recall from Morning– Point, Evidence, Explanation What is your evidence? Is it a change over time? Is it a distribution of numbers? A category comparison? Deviation Distribution Correlation Change over Time Ranking Magnitude Part-to- Whole Source: https://ft-interactive.github.io/visual-vocabulary/
  • 63.
    Choosing a Visual:Change over Time Give emphasis to changing trends Deviation Distribution Correlation Change over Time Ranking Magnitude Part-to- whole Source: https://ft-interactive.github.io/visual-vocabulary/
  • 64.
    Choosing a Visual:Part-to-Whole A bit like a recipe – how much is one group as a percentage of the entire population Deviation Distribution Correlation Change over Time Ranking Magnitude Part-to- Whole Source: https://ft-interactive.github.io/visual-vocabulary/
  • 65.
    Choosing a Visual:Magnitude Show size comparisons – these can be relative or absolute Deviation Distribution Correlation Change over Time Ranking Magnitude Part-to- whole Source: https://ft-interactive.github.io/visual-vocabulary/
  • 67.
    Exercise: Match the“Evidence” the to “Chart” – (10mins) Sometimes multiple charts can be used – justify! Bar Chart Pie Chart Stacked Bar Chart Choropleth Histogram Scatterplot Line Chart 1. Quarterly revenue comparison for star products 2. States with the lowest level of inequality 3. Market share of our new deodorant 4. Correlation between obesity and consumption of doughnuts 5. My income compared against everyone in Malaysia
  • 68.
    Data Storyteller’s Toolbox– Line Chart 0 2 4 6 8 10 12 14 16 18 20 1-2017 7-2017 1-2018 7-2018 1-2019 7-2019 Sales Revenue (RM mil) Malaysian Refined Petroleum Revenue 2017-19 Eastern Region Revenues rose the most in Q1 2017 on the back of a government-led township development initiative in Sabah & Sarawak Do • Choose meaningful axes tick intervals • Label lines directly • Draw attention to lines of interest through use of color • Lighten gridlines to enable focus on data • Include annotations or labels to provide context Don’t • Create a spaghetti graph by keeping everything in multiple colors • Use a line chart for categorical data as the lines imply continuity Line Charts are best used for change over time Eastern Northern Central Southern Govt. Subsidy Introduced Eastern supply lines disrupted
  • 69.
    Data Storyteller’s Toolbox– Bar Chart Do • Limit the number of sub-categories to three • Change the spacing between bars – visual grouping can have a big effect on perception • Use Data Labels if accuracy is needed • Include context such as a benchmark or average if necessary Don’t • Make the bars touch one another – that is a different chart altogether known as a histogram Bar Charts are used to compare between different categories 4.3 2.5 3.5 4.5 2.4 1.2 2.4 4.4 1.8 2.8 1.9 0.6 2.0 2.0 3.0 5.0 2.5 0.5 Oil Gas Renewables Hydro Coal Nuclear Billion tonnes of oil equivalent Malaysian Energy Consumption Mix 2019 Nuclear energy is an energy source that is being under-used in Malaysia due to the strict regulation around its production Northern Southern Central Previous Year Average
  • 70.
  • 71.
    Data Storyteller’s Toolbox– Stacked 100% Bar Chart Do • Limit the number of sub-categories to three – the non-zero baseline makes it difficult to compare • Use data labels – this is a must for stacked bar charts • Add a meaningful scale to remind the audience that this is normalized data • Use a landscape orientation especially when category names are long Don’t • Use category labels at an angle – they are difficult to read • Overwhelm with too many super categories – remember cognitive overload Stacked bar charts compare categories & sub-categories 4.3 2.5 3.5 4.5 2.4 1.2 2.4 4.4 1.8 2.8 1.9 0.6 2 2 3 5 2.5 0.5 0% 20% 40% 60% 80% 100% Oil Gas Renewables Hydro Coal Nuclear Billion tonnes of oil equivalent Malaysian Energy Consumption Mix 2019 Nuclear energy is an energy source that is being under-used in Malaysia due to the strict regulation around its production Northern Southern Central
  • 72.
    Stacked Bar Chart Whatwent wrong here?
  • 73.
    Data Storyteller’s Toolbox– Pie Chart / Donut Chart Do • Add Data Labels Directly onto the Chart • Use colors that are clearly distinguishable from one another • Try to limit the number of sub-categories being used • Choose an appropriate amount of white space in the middle of the donut Don’t • Use multiple layers of Donuts – this is not a Croissant • Add Donut Explosion • Use 3D effects – it doesn’t add any value to the message Compare Proportions to Whole Oil 2 Gas 2 Renewables 3 Hydro 5 Coal 2.5 Nuclear 0.5 Malaysian Energy Consumption Mix 2019 Nuclear energy is an energy source that is being under-used in Malaysia due to the strict regulation around its production [billion tonnes of oil equivalent]
  • 74.
    Data Storyteller’s Toolbox– Impact Metrics Do • Use size & bolding for a visual hierarchy on your impact metric • Support the impact metric with sufficient context • Provide context through non-disruptive background images Don’t • Distract from the impact metric by using the same bold/size for supporting elements on the visual • Try to provide too much explanation – impact metrics by nature need to be short and sweet Report on success or key achievements/figures from a report https://www.theedgemarkets.com/article/bringing-talent-home
  • 75.
    Data Storyteller’s Toolbox– Impact Metrics Report on success or key achievements/figures from a report Source: Storytelling with Data – Cole Nussbaumer Knafflic
  • 76.
  • 77.
    Data Storyteller’s Toolbox– Tables / Heatmaps Do • Provide sufficient spacing between columns for easy interpretation • Use borders meaningfully – they do not always need to be included • Select alignment of text carefully – similar metrics should be aligned in the same way • Consider introducing sparklines, bars, heatmap coloring to break monotony of visual Don’t • Use bold/thick gridlines that obscure the data being presented • Present a table with too many rows & columns – remember the audience wants the pearls not the clams • Include unnecessary decimal points Tables and Heatmaps should be avoided in presentations but can be used as last resort
  • 78.
    Tables / Heatmaps Whatwent wrong here? X Candy Central 15.1234% 123.1 0.4232 Y Apple Northern 20.5234% 198.1 3.134 C Jack Central / Eastern 10.461% 212.2 8.4123 G Tom Southern / Central 25.134% 432.5 0.983
  • 79.
    Data Storyteller’s Toolbox– Geographic Maps Do • Limit to just showing one kind of variable • Provide supporting elements such as a table or a bar chart below as an alternative to precision • Choose a color scale that is easy on the eyes (blue and oranges are recommended) • Consider adding a data label/annotation where appropriate • Normalize the data before plotting – absolute numbers might not visualize well Don’t • Use Choropleths alone to monitor subtle differences (color scale not easy for human eye) • Choose a confusing color scheme such as red-purple-blue Great to show clear regional patterns in the data Malaysian Unemployment Rates (2017) Northern states experienced highest rates of unemployment in 2019 Local economy recession in Northern Regions
  • 80.
    Geographic Maps /Choropleths What went wrong here?
  • 81.
    Example – MalaysiaElectricity Generation Fuel Mix Choosing an appropriate chart 0 10000 20000 30000 40000 50000 60000 70000 80000 1990199119921993199419951996199719981999200020012002200320042005200620072008200920102011201220132014201520162017 Future of Malaysian Electricity Generation is Hydroelectric Proposal to build water dams in East Malaysia will generate 10,000 GwH of Hydroelectricity a year saving the government an estimated RM 400,000 monthly through cheaper energy. Hydro Gas Coal Oil Diesel Others
  • 82.
    Visual: Data Visualization Best Practice •Chart Junk • Visual Hierarchy • Leveraging Color • End of Chapter Activity Chapter 5
  • 83.
    Less is More:Eliminating Chart Junk Maximizing the amount of signal sending “data ink” on the visual “Good graphical representations maximize data-ink and erase as much non-data-ink as possible,” Edward Tufte - author of Visual Display of Quantitative Information (1983) Ink used to send information to audience Total Ink Used Data-Ink Ratio
  • 84.
    Cognitive Load • Everyelement added to a blank screen takes up cognitive load for the audience • Our brain has limited “space” to process information • The reason why we didn’t like to look at the previous visual is that there is excessive cognitive load • Cognitive load culprits: • Clutter • Lack of Visual Hierarchy Minimize the amount of “Brain Space” being used by your visual
  • 85.
    Example of PoorData to Ink Ratio Repeated content is not always bad – but it should be done purposefully Example of non-data ink • Legend repeats information already on axis • Bananas in background distracting and doesn’t send information • Extra decimal places • 3D effect doesn’t add any additional information as all bars are same width
  • 87.
  • 88.
  • 89.
  • 90.
  • 91.
  • 92.
  • 93.
  • 94.
  • 95.
  • 96.
  • 97.
    Guided Redesign Less ismore! section 03
  • 98.
  • 99.
  • 100.
    Visual Hierarchy How many3’s in the visual? Source: Storytelling with Data (pg.103)
  • 101.
    Visual Hierarchy How many3’s in the visual? Source: Storytelling with Data (pg.103)
  • 102.
    Exercise: What didyou see first? Building a Visual Hierarchy The order I viewed the elements is: 01. ________________ 02. ________________ 03. ________________ 04. ________________ 05. ________________ 06. ________________ Source: Data Points, Nathan Yau
  • 103.
    Visual Hierarchy: How-to-Guide 12Visual Hierarchy Design Tips 1. Size & Scale 2. Color & Contrast 3. Typographic Hierarchy 4. Spacing 5. Proximity 6. Negative Space 7. Alignment 8. Rule of Odds 9. Repetition 10. Leading Lines 11. Rules of Thirds 12. Perspective Source: https://visme.co/blog/visual-hierarchy/
  • 104.
    Visual Hierarchy: How-to-Guide 12Visual Hierarchy Design Tips 1. Size & Scale 2. Color & Contrast 3. Typographic Hierarchy 4. Spacing 5. Proximity 6. Negative Space 7. Alignment 8. Rule of Odds 9. Repetition 10. Leading Lines 11. Rules of Thirds 12. Perspective
  • 105.
    Visual Hierarchy: How-to-Guide 12Visual Hierarchy Design Tips 1. Size & Scale 2. Color & Contrast 3. Typographic Hierarchy 4. Spacing 5. Proximity 6. Negative Space 7. Alignment 8. Rule of Odds 9. Repetition 10. Leading Lines 11. Rules of Thirds 12. Perspective
  • 106.
    Visual Hierarchy: How-to-Guide 12Visual Hierarchy Design Tips 1. Size & Scale 2. Color & Contrast 3. Typographic Hierarchy 4. Spacing 5. Proximity 6. Negative Space 7. Alignment 8. Rule of Odds 9. Repetition 10. Leading Lines 11. Rules of Thirds 12. Perspective
  • 107.
    Visual Hierarchy: How-to-Guide 12Visual Hierarchy Design Tips 1. Size & Scale 2. Color & Contrast 3. Typographic Hierarchy 4. Spacing 5. Proximity 6. Negative Space 7. Alignment 8. Rule of Odds 9. Repetition 10. Leading Lines 11. Rules of Thirds 12. Perspective
  • 108.
    Visual Hierarchy: How-to-Guide 12Visual Hierarchy Design Tips 1. Size & Scale 2. Color & Contrast 3. Typographic Hierarchy 4. Spacing 5. Proximity 6. Negative Space 7. Alignment 8. Rule of Odds 9. Repetition 10. Leading Lines 11. Rules of Thirds 12. Perspective
  • 109.
    Visual Hierarchy: How-to-Guide 12Visual Hierarchy Design Tips 1. Size & Scale 2. Color & Contrast 3. Typographic Hierarchy 4. Spacing 5. Proximity 6. Negative Space 7. Alignment 8. Rule of Odds 9. Repetition 10. Leading Lines 11. Rules of Thirds 12. Perspective
  • 110.
    Visual Hierarchy: How-to-Guide 12Visual Hierarchy Design Tips 1. Size & Scale 2. Color & Contrast 3. Typographic Hierarchy 4. Spacing 5. Proximity 6. Negative Space 7. Alignment 8. Rule of Odds 9. Repetition 10. Leading Lines 11. Rules of Thirds 12. Perspective
  • 111.
    Visual Hierarchy: How-to-Guide 12Visual Hierarchy Design Tips 1. Size & Scale 2. Color & Contrast 3. Typographic Hierarchy 4. Spacing 5. Proximity 6. Negative Space 7. Alignment 8. Rule of Odds 9. Repetition 10. Leading Lines 11. Rules of Thirds 12. Perspective
  • 112.
    Visual Hierarchy: How-to-Guide 12Visual Hierarchy Design Tips 1. Size & Scale 2. Color & Contrast 3. Typographic Hierarchy 4. Spacing 5. Proximity 6. Negative Space 7. Alignment 8. Rule of Odds 9. Repetition 10. Leading Lines 11. Rules of Thirds 12. Perspective
  • 113.
    Visual Hierarchy: How-to-Guide 12Visual Hierarchy Design Tips 1. Size & Scale 2. Color & Contrast 3. Typographic Hierarchy 4. Spacing 5. Proximity 6. Negative Space 7. Alignment 8. Rule of Odds 9. Repetition 10. Leading Lines 11. Rules of Thirds 12. Perspective
  • 114.
    Visual Hierarchy: How-to-Guide 12Visual Hierarchy Design Tips 1. Size & Scale 2. Color & Contrast 3. Typographic Hierarchy 4. Spacing 5. Proximity 6. Negative Space 7. Alignment 8. Rule of Odds 9. Repetition 10. Leading Lines 11. Rules of Thirds 12. Perspective
  • 115.
    Using Color Purposefullyfor Storytelling Tips on using Color 1. Reduce Color Saturation 2. Beware Existing Color Associations 3. Reduce Cognitive Load Color is an information encoder – we need to know how to use it
  • 116.
    Using Color Purposefullyfor Storytelling Tips on using Color 1. Reduce Color Saturation 2. Beware Existing Color Associations 3. Reduce Cognitive Load 4. Complimentary Colors Color is an information encoder – we need to know how to use it https://playfairdata.com/3-storytelling-with-color-tips-to-improve-your-data-visualization/
  • 117.
    Using Color Purposefullyfor Storytelling Tips on using Color 1. Reduce Color Saturation 2. Beware Existing Color Associations 3. Reduce Cognitive Load 4. Complimentary Colors Color is an information encoder – we need to know how to use it https://playfairdata.com/3-storytelling-with-color-tips-to-improve-your-data-visualization/
  • 118.
    Using Color Purposefullyfor Storytelling Tips on using Color 1. Reduce Color Saturation 2. Beware Existing Color Associations 3. Reduce Cognitive Load 4. Complimentary Colors Color is an information encoder – we need to know how to use it
  • 119.
    Using Color Purposefullyfor Storytelling Tips on using Color 1. Reduce Color Saturation 2. Beware Existing Color Associations 3. Reduce Cognitive Load 4. Complimentary Colors Color is an information encoder – we need to know how to use it
  • 120.
    Using Color Purposefullyfor Storytelling Tips on using Color 1. Reduce Color Saturation 2. Beware Existing Color Associations 3. Reduce Cognitive Load 4. Complimentary Colors Color is an information encoder – we need to know how to use it
  • 121.
    Note: Color blindnessaffects green & red most often Try to choose orange and blue colors instead
  • 122.
    End of DayActivity – (2 hours) Task Redesign the visual from before. Remove chart junk and justify the use of every element on the visual – if it isn’t needed then remove it! Redesign the visual to have a strong visual hierarchy using the concepts discussed One or two will be required to share their findings with the audience Remember – sift through all the clams to find the pearls of insight
  • 123.
    Example – MalaysiaElectricity Generation Fuel Mix Building a visual hierarchy 0 10 20 30 40 50 60 70 80 1990 1995 2000 2005 2010 2015 Thousands Future of Malaysian Electricity Generation is Hydroelectric Proposal to build water dams in East Malaysia will generate 10,000 GwH of Hydroelectricity a year saving the government an estimated RM 400,000 monthly through cheaper energy. Coal Gas Hydro Others Diesel Oil Based on data from the Department of Statistics, Malaysia 1990-2017 1997 Asian financial crash led to a dramatic fall in local energy demand 2011 production of Gas was down 10.3% compared to the previous year Hydro has had a 400% increase in 2017 compared to 2016
  • 124.
  • 125.
    Redesign this tableusing the Data Storytelling Process Start with the Narrative, and with the Visual Survey of student’s interest in science both before and after attending a pilot summer science camp
  • 126.
    Sample Submissions Adapted fromStorytelling with Data – Cole Nussbaumer Knafflic
  • 127.
    Sample Submissions Adapted fromStorytelling with Data – Cole Nussbaumer Knafflic
  • 128.
    Sample Submissions Adapted fromStorytelling with Data – Cole Nussbaumer Knafflic
  • 129.
    Sample Submissions Adapted fromStorytelling with Data – Cole Nussbaumer Knafflic
  • 130.
    Sample Submissions Adapted fromStorytelling with Data – Cole Nussbaumer Knafflic
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