Art and Science of Dashboard Design

The Art and Science of Dashboard Design

Lee Lukehart Chief Data Visualist SavvyData

Why am *I* here?

§  data geek, interface & UX designer

§  trainer & curriculum author

§  dataviz enthusiast

Assumptions about You

§  Not a formally trained graphic designer

§  Do UI/UX design; perhaps also a DBA

§  Work for management vs. marketing*

*If the latter, see How to Lie with Charts, How to Lie with Statistics, etc.

This session…

will cover:

A bit of theory

Common “gotchas”

Useful resources

will not cover:

Schema design

Technique demos

Specific tools

First, a survey:

Do you…

…design for the desktop, mobile devices, or both?

…pull data locally, remotely from servers, or both?

…work with Big Data?

…have to satisfy multiple types of users?

First, a survey:

Do you…

…design for the desktop, mobile devices, or both?

…pull data locally, remotely from servers, or both?

…work with Big Data?

…have to satisfy multiple types of users?

Hmmm…

“I’ll pause

for a moment

so you can let

this information

sink in.”

New Yorker Magazine, 12/6/2010

The Visualization Landscape

http://www.visual-literacy.org/periodic_table/periodic_table.html

Why so many types?

Visual is our dominant modality

§  We evolved biologically to rely primarily on sight

§  >50% of the brain is used for visual processing

§  We use visual metaphors to understand our world

§  Visualization is everywhere we look! (pun intended)

Common Data Graph Types

§  Bar

§  Horizontal Bar

§  Line

§  Area

§  Pie

Composite Data Graph Types

§  Bullet

§  Sparkline

§  Horizon

§  Gauge

Purpose of Data Graphs

§  Discern relationships between data points or series

§  Identify patterns, trends and exceptions

§  Evoke a story about the data

§  Engage » Inform » Induce Action/Decision

To be compelling displays of meaningful and unambiguous data

Purpose of Dashboards

“…visual display of the most important information

needed to achieve one or more objectives,

consolidated and arranged on a single screen

so the information can be monitored at a glance.”

– Stephen Few, Perceptual Edge

What is the objective?

1960 Plymouth vs. 1960 Corvette


2011 Tesla Model S


2008 Lamborghini Reventón

Purposes of Dashboards

§  Measure performance / conditions

§  Gauge progress toward business goals (KPIs)

§  Align execution with strategy

§  Engage » Inform/Indicate » Alert » Induce Action

To be actionable displays of meaningful and unambiguous data

Performance Dashboards, Wayne Eckerson

dri

ll d

ow

n

dri

ll d

ow

n

Strategic

Tactical Operations

“The 3 Threes”

Strategic Dashboard

§  Monitor trends


§  NOT real-time

Strategic Dashboard

Tactical Dashboard

§  Manage performance against preset target

§  Analyze — link results to actions

§  Discover problems & opportunities

Tactical Dashboard

§  Measure performance

§  Gauge progress toward business goals (KPIs)


Operations Dashboard

§  Measure performance

§  Detailed insights / respond as needed

§  Real-time (or almost so)

Operations Dashboard

Dashboard Example

Potential Problems

§  Can be Confusing

§  Can be Boring

§  Can be Inaccurate and Misleading

§  Can be Ineffective and Worthless (or worse)

When I am working on a problem,

I never think about beauty.

But when I have finished

if the solution is not beautiful,

I know it is wrong.

– Buckminster Fuller

❞

❝

Effective Data Visualization

1.  Know when not to (a table or list may be preferable)

2.  Know your data (source, scope… clean & complete?)

3.  Consider your audience (their needs & familiarity)

4.  Determine chart’s message or focus

5.  Select an effective chart type (to best convey message)

6.  Construct data transforms (aggregate/augment, as needed)

7.  Conduct pre-flight checklist (for QA & K.I.S.S. testing)


1.  Know when not to use graphs

52%*

of 2010 class is female *dataset 98% complete

the chart in this example is a waste of space

2.  Know your data

§  Source

§  History

§  Scope & Scale

§  Hygiene

§  Aggregated

§  Atomic

Avoid GIGO (Garbage In, Garbage Out)

— How was data created/collected/imported; is it reliable? Should include on chart for credibility? What is unit of measure?

— Have any parts been adjusted or converted? Have key attributes changed

(exchange rates, inflation-adjusted, remapped sales territories)? — What are min/max, density, precision? Any collection shortfalls? Enough

data to be meaningful? Value extremes that complicate display? — How clean, consistent, and normalized is it? — Any data already totaled or averaged; trend line calc or data mart output? — Sufficient granularity to change sort for different types of summaries?


3.  Consider your audience

§  Appropriate prior subject knowledge

§  Expertise level: novice, general, or expert

§  Internal or external

§  Whether already motivated to view your chart

§  Explicit and unstated audience expectations

§  Presentation environment and conditions

Prepare for communication


4.  Determine data’s message & chart’s focus

§  Ranking comparison §  Categorical/Nominal comparison §  Time series, Ordered intervals §  Proportion of the Whole (contribution/composition)

§  Variance/Deviation (to goal, historical or other benchmark)

§  Distribution (histograms, etc.)

§  Correlation (scatter plots, bubble charts, etc.) §  GeoSpatial (maps with data overlays, linked to location)

Eight types of relationships between data


5.  Select best chart type for the message

§  Bar, Vertical

§  Bar, Horizontal

§  Line

§  Area

§  Pie

To rank items, show counts, magnitudes, discrete frequency distributions; to compare different categories or one category under varied conditions; Horizontal especially suited for displaying many categories or when category labels are lengthy

To show contiguous change and other functional relationships over time; good for multiple data series; slope of line between points conveys “shape”; Area charts additionally suggest cumulative values

To represent proportions relative to the whole; inherently conveys composition and contribution


5.  Select best chart type for the message

Dozens of guides are available; see resource page (at end) for examples and links.

Graph Selection Matrix

Time Series Ranking Part-to-Whole Deviation Distribution Correlation Nominal Comparison

Values display how some- thing changed through time (yearly, monthly, etc.)

Values are ordered by size (descending or ascending)

Values represent parts (ratios) of a whole (for example, regional portions of total sales)

The difference between two sets of values (for example, the variance between actual and budgeted expenses)

Counts of values per interval

from lowest to highest (for example, counts of people in an organization by age intervals of 10 years each)

Comparison of two paired sets of values (for example, the heights and weights of several people) to determine if there is a relation-ship between them

A simple comparison of values for a set of unor- dered items (for example,products or regions)

Bar Graph(vertical)

Yes (to feature individual values and support their comparisons; quantitative scale must begin at zero)

Yes (quantitative scale must begin at

zero)

Yes Yes (quantitative scale

must begin at zero)

Yes Yes (quantitative scale must begin at zero)

Bar Graph(horizontal)

Yes (quantitative

scale must begin at

zero)


must begin at zero)


must begin at zero)

Line Graph

Yes (to feature overall

trends and patterns and support their comparisons)

Yes (only when also

featuring a time series or single distribution)

Yes (to feature the overall

shape of the distribution)

Dot Plot (vertical)

Yes (when you do not

have a value for every interval of time)

Yes

Yes

Dot Plot (horizontal)

Yes

Yes

Strip Plot (single)

Yes

Strip Plot (multiple)

Yes (only when also fea-

turing distributions) Yes (when comparing multiple

distributions and you want

Scatter Plot

Yes

Box Plot (vertical)

Yes (only when also featuring distributions)

Yes (when comparing multiple distributions)

Box Plot (horizontal)

Yes (when comparing multiple distributions)

GraphRel

atio

nship

perceptualedge

Copyright © Stephen Few 2009

(quantitative

scale must begin at

zero)

(quantitative

scale must begin at

zero)

www.PerceptualEdge.com (Derived from the book Show Me the Numbers, Stephen Few, Analytics Press, 2004)

to see each value)

(when you want to see

each value)

along a quantitative scale

(quantitative scale must

begin at zero)

(quantitative scale must

begin at zero)


6.  Construct data transforms as needed

§  Aggregate: summarized total, count, average, running average

§  Segment: derive subset attributes (e.g. month name, price tier)

§  Factor: inflation-adjusted, year-to-year change, time-shifting

§  Augment: extend data with truly new data (via WSDLs, etc.)

§  Find: full year, by category, include/omit “others”

§  Organize/Sort: for display, e.g. multiple years by month

Derive new data to tell the real story


§  Human factors

§  Data integrity

§  Data sorting

§  Scaling / precision

§  Data labeling

7.  Conduct pre-flight checklist

Inspect for top ten common design errors:

§  Chart type choice

§  Single über-chart

§  Chart title & legend

§  Visual formatting

§  ChartJunk*


§  Human factors

§  Data integrity

§  Data sorting

§  Scaling / precision

§  Data labeling

7.  Conduct pre-flight checklist

Inspect for top ten common design errors:

§  Chart type choice

§  Single über-chart

§  Chart titling

§  Visual formatting

§  ChartJunk*


Human Factors in Visual Perception

§  Optical perception issues

§  Cognitive illusions

§  Automatic (pre-attentive) behaviors

§  Cultural biases

Optical Perception Issues

8% of population is red-green color-blind

Simulation: What the color-blind see… (An Ishihara plate: What do you see?)

Full-range Color Vision

Can see the

number “74”

Protan Subtype

Reads the

number as “21”

Deutan Subtype

Cannot read any number

Normal eyesight

88%

Other 3%

Deuteranomaly 5%

Protanomaly 1%

Protanopia 1%

Deuteranopia 1%


Normal vision Simulated red-green blind

Usability resources:

Photoshop CS4+ Vischeck.com Colorschemedesigner.com Etre.com


Relative color hue Relative color density Q: Which square is the darkest? Q: Which 2 swirls are the same color?


Relative color hue Relative color density

A: Trick question. All 3 are identical.

Q: Which square is the darkest? Q: Which 2 swirls are the same color?

universally perceived due to “proximity effect”

A: The “green” and “blue” swirls are actually the same color.

Cognitive Illusions

Compensation Light direction and perspective

“Yes – 5 bumps and 1 dimple.”

We will now rotate the image 180°… “Obviously not!”

Q: Are there more bumps or more dimples?

Q: Are squares A & B the same shade?

Cognitive Illusions

Light direction and perspective

“Now there are more dimples.”


“Of course not!”

Compensation Q: Are squares A & B the same shade?

Cognitive Illusions

Compensation Light direction and perspective

“Now there are more dimples.”

Actually, this is the same image rotated 180°. “Ahem, I mean,

Yes.”


Q: Are squares A & B the same shade?

universally perceived due to real-world experience

Judgment Errors

We are poor at determining volumes and angles

How easily can you rank the following slices?

How about the bars?

Note: Slice ‘B’ should be easy… it is 25% — a right angle. But the 3D Pie makes it impossible to perceive it as such.

Automatic Behaviors

Awareness/Attention Consciously attentive Count the “F” characters:

Automatic Behaviors

Awareness/Attention Consciously attentive

Pre-attentive recognition of Color

Count the “F” characters:

Now count the “F” characters:

“Pre-attentive” came from cognitive psychology and is meant to describe those attributes we notice before noticing that we’ve noticed them.

Automatic Behaviors






Pre-attentive recognition of Position Now count the “F” characters:

Pre-attentive recognition of Size Now count the “F” characters:

Automatic Behaviors






Pre-attentive recognition of Position Now count the “F” characters:

Pre-attentive recognition of Size Now count the “F” characters:

Pre-attentive patterns, trends and exceptions in the data will out at you

Perception vs. Implied Attributes

Perception vs. Implied Attributes

non-zero Y-axis scale minimum

Misleading Accurate and Truthful

Charting Pre-flight Checklist

¨  Human factors considered

¨  Data checked for integrity

¨  Data sort correct

¨  Min/Max scales match plotted data

¨  Data labels are adequate and accurate

¨  Chart type choice matches message

¨  Multiple charts considered

¨  Chart title is fully informative

¨  Visual formatting

¨  Appropriate font face

¨  Pie charts have <6 slices

¨  Appealing to target audience

¨  Useful legend, if needed

¨  Source explained, if needed

¨  Last update & author info noted

¨  Good use of basic design principles

¨  Color is used consistently

¨  Text is appropriately large and legible

¨  No added chartjunk

¨  Color enhances rather than distracts

¨  Each element used serves a clear purpose

Resources §  Slide deck, via this session’s SVCC page:

http://siliconvalley-codecamp.com/Sessions.aspx?id=902

§  Slide deck & links list, via shared Evernote notebook:

https://www.evernote.com/pub/savvydata/SVCC-dashboard-design

§  Contact me at Lee Lukehart <[email protected]>

Art and Science of Dashboard Design

Design

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