THE UX OF DATA VISUALIzation Part 1

This past quarter at the University of Washington I took a class on Data Visualization.  As a multifaceted Designer, I see the importance of Big Data in Future of Informing Design Decisions. Data is a powerful mechanism for telling stories. There will be a shift in how we communicate our Design stories.  

To best understand our users, it is essential to collect data about user needs. In my Autumn quarter at the University of Washington, I learned a lifelong skill of Data Visualization.  In this class, we learned how to tell stories through graphs, when and why we should use specific charts to display data.  We worked with I.M.D.B data sets to tell stories about the movie industry.  Also, for my project, I told the story of how products sold on Amazon. 

I dove deep into the statistics, and I constructed Time-Series Analysis of product Sales Ranks over time.  I made table lenses, maps, bar graphs, line graphs, and scatter plots.  I performed distribution analysis, time-series, correlations, part-to-whole rankings, and multivariate analysis.  I know now how to present data intentionally.   Giving people access to their own data is a valuable gift.  I could bring this new angle to your design team as you tackle challenging problems of today.

I am currently working on a small project that will help UX designers present data 

What I Learned

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What I read 

Stephen Few Now you see it

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What Story I told with Data

 

What are the Best Practices for Telling stories through data? 

Forming key takeaways & Orient the Audience

When you present data, you always should think from the perspective of the viewer.  What story is this graph trying to tell?  

First you should orient the audience

What does each axis mean?

What does one point on the graph represent?

What do the colors mean in the context of your graph?

Second you should address key takeaways on your graph

What type of graph is this?

What story is the data telling us?

 

Below are a few Guidelines we can stick to as UX designers as we present data

 

1. Time Series Analysis

Final How to sell Fidget Spinners on Amazon-2.png

What is shows

  • Trends, Variability, Rate of change, Co-variation, Cycle, Exceptions

Types of Graphs

  • Line graphs for Analyzing patterns and exceptions

  • Bar graphs for emphasizing and comparing individual values

  • Dot plots for analyzing irregular intervals

  • Radar graphs for comparing cycles

  • Heat-maps for Analyzing high volume cyclical patterns and exceptions

  • Box-Plots for analyzing distribution changes

  • Animated scatterplots for analyzing correlation changes

Best practices (pg.163)

  • Aggregating to various time intervals

  • Viewing time periods in context

  • Grouping related time intervals

  • Using running averages to enhance perception of high-level patterns

  • Omitting missing values from a display

  • Optimizing a graphs aspect ratio

  • Using logarithmic scales to compare rates of change

  • Overlapping time scale to compare cyclical patterns

  • Using cycle plots to examine trends and cycles together

  • Combining individual and cumulative values to compare actuals to a target

  • Shiting time to compare leading and lagging indicators

  • Stacking line graphs to compare multiple variables

  • Expressing time as a 0-100% compare asynchronous processes



 

2. Part to whole/Ranking Analysis (p.189)

 

Graphs

  • Pie charts

  • Bar Graphs

  • Dot Plots

  • Pareto charts

Key takeaways

  • Uniform

  • Uniformly different

  • Non-uniformly different

  • Increasingly different

  • Decreasingly different

  • Alternating differences

  • Exceptional

Best practices

  • Grouping categorical items in an ad hoc manner

  • Using Pareto Charts with percentile scales

  • Re-expressing values to solve quantitative scaling problems

  • Using line graphs to view ranking changes through time

 

3. Correlation (p.245)

How quantitative variables relate to and affect one another

Final How to sell Fidget Spinners on Amazon (3).png

 

Graphs (p.261)

  • Scatterplots for comparing two variables

  • Scatterplot Matrices for comparing multiple pairs of variables

  • Table Lens for comparing more than two variables simultaneously

Key Takeaways (p.247)

  • Direction

  • Strength

  • Shape

Best practices (p.266)

  • Optimizing aspect ratio and quantitative scales

  • Removing fill color to reduce overplotting

  • Comparing data to reference regions

  • Visually distinguishing data sets when they're divided into groups

  • Using trend lines to enhance perception of correlations shape, strength, and outliers

  • Using multiple trend lines to see categorical differences

  • Removing the rough to see the smooth more clearly

  • Using treillis and crosstab displays to reduce complexity and overplotting

  • Using grid lines to enhance comparison between scatterplots

 

4. Distribution

 

Final How to sell Fidget Spinners on Amazon (2).png

Graphs

  • Histograms

  • Frequency polygons

  • Strip plots

  • Stem-and-leaf Plots

Key takeaways

  • Spread

  • Center

  • Shape

Best Practices

  • Keeping intervals consistent

  • Selecting the best interval

  • Using measure that are resistant to outliers

 

Other Tips

  • Avoid using Pie Charts!

  • Use color to emphasis points on graph

  • Use size to emphasis points on graph

 

Conclusion

In sum, there is a lot we can learn from Stephen Few's Book Now You See It.  I am currently still in the process of learning Tableu, and working on improving the way I tell my own stories with data.  I truly believe that this is a skill we as designers should acquire.  I've summarized the best practices of the most frequently used graphs, and briefly touched up ways we could improve how we tell our stories through data.  As you read this, I am probably in process of creating something that will help UXer's learn of Data Visualization.