These slides are from a live webinar that I gave on 10/3/17 for the Digital Analytics Association (DAA) on data visualization best practices, building a data viz team and data storytelling.

1. The Pillars of Successful DataVisualization Design
The RightTeam, Data andTool

2. Good Afternoon
Gregory Kaminski | Director of Analytics
MaassMedia employee since 2013
Experienced in all areas of marketing analytics
Passion for data visualization
Favorite visualization tool: pen and paper
Finance Retail Consulting
5Years 7Years 5Years

3. Boutique Digital Analytics
Founded in 2008
MaassMedia is an independent, specialty analytics consultancy based in Philadelphia.
We provide guidance and leadership to major global brands seeking to optimize their
investments in digital multi-channel content, marketing and customer service
initiatives.
Areas of expertise
Mobile, social, video & web analytics, data visualization and statistical analysis
Our clients & industry focus
Media, financial services, healthcare, technology, retail, and B2B
Comcast, Lenovo, ESPN, NBC, A&E,The Guardian, Brother, Penn Medicine

4. Agenda Overview
Introduction
Why &What
How
Best Practices
Data Storytelling
Closing / Q&As
Slides 2 -3 Slides 5 - 11 Slides 10 - 13 Slides 14 - 27 Slides 28 - 32 Slides 33 - 34

5. Why do we use
data visualization?

6. We’reAll Analysts

7. “The visual provides expression
where words fail.”
- “Unflattening” by Nick Sousanis

8. A woman walking down the street.
c.s-microsoft.comi.pinimg.comflickr.com

9. What is
data visualization?

10. DataVisualization Has No Home
Not a field unto itself1
2
3
4
5 There aren’t many CVOs - chief visualization officer
We hear things and just accept them
We learn the basics, but don’t go further
There aren’t hard and fast truths like math and science

11. We use data
visualization to convey
key insights effectively.
The intersection of data,
design, and psychology.
It’s not just about making data beautiful

12. Structure & Process

13. Getting to Good Data
Data
Visualization
Data
Collection
Measurement
Plan
Business
Requirements
Goals = Metrics Develop Trust Build Reporting

14. Our Process For BuildingVisualizations
RepeatStep 7Step 6Step 5Step 4Step 3Step 1 Step 2
Requirements
Gathering
Internal Review
Revisions / Modifications
Build
Setup data
manipulate, aggregate
Final Build
Refresh schedules
Distribution methods
Design Draft
Create data model
Develop Wireframe
Stakeholder Review
Revisions / Adjustments
Reviews Maintenance
User feedback
Adjust with business
Internal and client
Revisions / Modifications

15. WireframeTemplate
Cover Sheet
Executive
Summary
Management
View
AnalystView
• Provides a description of the dashboard
• Definitions of metrics and calculated fields
• Maintenance and access to data connections
• Snapshot of high-level metrics
• Descriptive data with comparisons to prior period
• Typically a static view, no filters or drill downs
• Views should show cause and effect relationships
• Metrics should be easily understood and actionable
• The story of the data should be clear
• Less aggregation, more “raw” view to encourage exploration
• Granularity can be down to the day or hour level
• Include multiple filters for 360° view of the dataset

17. The Data
VisualizationTeam

18. BuildingYourTeam
SME
Data
Designer
Engineer
Knows which
questions to ask
Knows how
to get the data
Knows how to
present the data

19. Best Practices

20. Remove the Noise
• Avoid drawing the attention of viewers to irrelevant elements
• Create figure/background contrast
• Goal is to display high “data-ink ratio”:
19 17 16 15 30 14 13
150
200
100
350
400
200
250
0
100
200
300
400
500
0
5
10
15
20
25
30
35
January February March April May June July
CaloriesBurnedfromDancing
CookiesConsumed
MonthlyActivity
Cookies Consumed Calories Burned from Dancing
19 17 16 15 30 14 13
150
200
100
350
400
200
250
0
100
200
300
400
500
0
5
10
15
20
25
30
35
January February March April May June July
MonthlyActivity
Cookies Consumed Calories Burned from Dancing
Before After
Data-ink
Total ink used to print the graph

21. Use Round Numbers
• Long numbers are hard to comprehend
• Utilize different units to clean up labels
• Use commas to help where necessary
Before After
0
2,000,000
4,000,000
6,000,000
8,000,000
10,000,000
12,000,000
14,000,000
16,000,000
Q1 Q2 Q3 Q4
The Office Facebook Fans
0
2
4
6
8
10
12
14
16
Q1 Q2 Q3 Q4
Millions
The Office Facebook Fans
“The Office had 14,184,419
Facebook fans in Q4”
“The Office had 14M
Facebook fans in Q4”

22. Pay Attention to Positioning
• Make sure labels are easy to read and not misleading
• Sort/order data appropriately, most often sequentially by value
Before Working Version After
0
5
10
15
20
25
30
35
40
PairsofCoolShoes
Pairs of Cool Shoes
0 10 20 30 40
Erin
Jim
Pam
Michael
Kelly
Creed
Pairs of Cool Shoes
15
19
20
25
30
34
Erin
Jim
Pam
Michael
Kelly
Creed
Pairs of Cool Shoes

23. Utilize Proximity for Comparisons
• Objects placed close to one another are perceived as a group
• Objects further apart seem less related
Before After
Insight: “Dwight’s Dundies collection was the only one that grew quarter over quarter”
Difficult to compare data across time periods
Gestalt Law of Similarity
0
2
4
6
8
10
Q1 Q2 Q3 Q4
Dundies (Awards) Received
Angela Dwight Ryan Phyllis
0
2
4
6
8
10
Angela Dwight Ryan Phyllis
Dundies (Awards) Received
Q1 Q2 Q3 Q4

24. Add Emphasis with Shapes
• Guide the viewer’s eye immediately to the point they should focus on
• Use shapes to create a distinct focal point
Before After
Insight: “Dwight’s Dundies collection was the only one that grew quarter over quarter”
Where’s Dwight on this graph?
Gestalt Law of Focal Point
0
2
4
6
8
10
Angela Dwight Ryan Phyllis
Dundies (Awards) Received
Q1 Q2 Q3 Q4
0
2
4
6
8
10
Angela Dwight Ryan Phyllis
Dundies (Awards) Received
Q1 Q2 Q3 Q4
0
2
4
6
8
10
Angela Dwight Ryan Phyllis
Dundies (Awards) Received
Q1 Q2 Q3 Q4

25. Be Cautious of Pie Charts
• Only familiar percentages (25%, 50%, 75%, 100%) can be easily gauged
• Difficult to compare other sizes/angles effectively
• Limit the number of variables (2-3 at most)
• Yes/No andTrue/False questions work well
Before After
15
14
10
9
Apples Consumed
Darryl Michael Toby Gabe
9
10
14
15
Gabe
Toby
Michael
Darryl
Apples Consumed

26. Limit the Number of Categories Displayed
• Exclude unnecessary categories if possible
• Line Charts: plot no more than 4 lines per chart
• Break up chart into separate graphs for better comparison
• Utilize a “other” category
Before
After
0
2
4
6
8
10
12
14
16
18
1 2 3 4 5 6 7 8 9
Month
Snacks Consumed
Chocolate Bars Candy Packs Bags of Chips
Yogurt Granola Bars Fruit
0
10
20
1 2 3 4 5 6 7 8 9 10
Month
Junk Food Snacks Consumed
Chocolate Bars Candy Packs Bags of Chips
0
5
10
15
20
1 2 3 4 5 6 7 8 9 10
Month
Healthy Snacks Consumed
Yogurt Granola Bars Fruit

27. Create Focus with Color
• Use color sparingly and consistently
• Utilize an accent color to highlight a significant data point
• Be mindful of red and green or colors with certain connotations
• Play with various shades of grey for additional differentiation
Before After
Q1 Q2 Q3 Q4
Andy -5% 2% 15% 20%
Stanley 7% -10% 18% 12%
Holly 2% 4% 9% 13%
Oscar 7% 10% 26% -11%
Meredith 10% 15% 17% -2%
Q1 Q2 Q3 Q4
Andy -5% 2% 15% 20%
Stanley 7% -10% 18% 12%
Holly 2% 4% 9% 13%
Oscar 7% 10% 26% -11%
Meredith 10% 15% 17% -2%
Growth in Twitter Followers Growth in Twitter Followers
4
5
6
8
15
Meredith
Oscar
Holly
Stanley
Andy
Comfy Sweaters Owned
4
5
6
8
15
Meredith
Oscar
Holly
Stanley
Andy
Comfy Sweaters Owned

28. Color Brewer - colorbrewer2.org
Developed at Penn State University by Professor Cynthia Brewer

29. Data Storytelling

30. Data Storytelling
Annotation
Sequencing
Data Journalism
Lower the threshold for understanding
Moderate level of guidance
Heavy handed direction, in your face
Concepts derived from Alberto Cairo, Professor at the University of Miami
Author of ‘The Truthful Art’

31. Annotation
$1.1
$0.6
$0.7
$0.7
$1.8
$1.9
$0.0 $0.5 $1.0 $1.5 $2.0
Other
Jewelry
Tanks
Shorts
Skirts
Coats
Revenue in Millions
Coats ContinueTo Be Sales Leader For FY2016
• Create a headline that draws in the viewer
• Use color, arrows, boxes, etc. to highlight what is important
• Group smaller numbers of categories into an ‘other’ bucket

32. Sequencing
Acquisition
• Channels
• Campaigns
• Attribution
Site Behavior
• Time series analysis
• Last 30 Days
• YoY and prior period
• Geo, device, visit type
• Entry and exit pages
Conversion
• Time series analysis
• Last 30 Days
• YoY and prior period
• Geo, device, visit type
• Funnel analysis
Returns
• Return Rates
• By line of business
• By product
• Satisfaction and loyalty
Customer Behavior & Purchase Funnel AlignWith Reporting

33. Data Journalism

34. FinalThoughts

35. Use color for
emphasis
Complex data
organized for easy
consumption
Clean design
with low data/ink
ratio

36. Takeaways / Q&As
The data should be complex, but not the design
1
2
3
4
5
Use best practices to guide you, and then build from there
Question bad design, but be kind with criticism
Create a repeatable process for building visualizations
Data storytelling comes in a few different flavors

37. ThankYou!
Gregory Kaminski
gkaminski@maassmedia.com
@Tan_Cardigan
1880 JFK Blvd., Suite 201
Philadelphia, PA 19103
p: (215) 545-1515
www.maassmedia.com