Data Visualization 101 for UX Designers
How might we tell a story through data visualization?
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. I anticipate that there will be a shift in how we communicate our & tell our stories as designers.
To best understand our users, it is essential to collect data about user needs. In my 2017 autumn quarter at the University of Washington, I acquired the 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. In my project, I tell the story of how fidget spinner 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.
WHAT I LEARNED
WHAT I READ
STEPHEN FEW NOW YOU SEE IT (amazing book!)
Avoid using pie charts
Use color to emphasis points on the graph
Use size to emphasis points on the graph
WHAT ARE THE BEST PRACTICES FOR TELLING STORIES THROUGH DATA?
FORM KEY TAKEAWAYS & ORIENT THE AUDIENCE
When presenting data, think from the perspective of the viewer. What story is this graph trying to tell?
FIRST ORIENT THE AUDIENCE and ask yourself…
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 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
WHAT IT SHOWS
- Trends, Variability, Rate of change, Co-variation, Cycles, 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
- 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
- Shfiting 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
Types of GRAPHS
- Pie charts
- Bar Graphs
- Dot Plots
- Pareto charts
- Grouping categorical items in an ad hoc manner
- Using Pareto Charts with percentile scales
- Re-expressing values to solve quantitative scaling problem
- Using line graphs to view ranking changes through time
3. CORRELATION (P.245)
How quantitative variables relate to and affect one another
- Scatterplots for comparing two variables
- Scatterplot Matrices for comparing multiple pairs of variables
- Table Lens for comparing more than two variables simultaneously
- Optimizing aspect ratio and quantitative scales
- Removing fill color to reduce over plotting
- 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 over plotting
- Using grid lines to enhance comparison between scatterplots
- Frequency polygons
- Strip plots
- Stem-and-leaf Plots
- Keeping intervals consistent
- Selecting the best interval
- Using measure that are resistant to outliers
There is a lot we can learn from Stephen Few’s Book Now You See It. I am continuing to learn Tableu, and working on improving the way I tell my own stories with data. I truly believe data visualization is a skill we as designers can acquire. I’ve summarized the best practices of the most frequently used graphs, and briefly touched upon ways we could improve how we tell our stories through data.