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The Pillars of Successful Data Visualization
- 1. The Pillars ofSuccessful 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 Foundedin 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 BestPractices 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 weuse data visualization?
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- 7. “The visual providesexpression where words fail.” - “Unflattening” by Nick Sousanis
- 8. A woman walkingdown the street. c.s-microsoft.comi.pinimg.comflickr.com
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- 10. DataVisualization Has NoHome 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 visualizationto convey key insights effectively. The intersection of data, design, and psychology. It’s not just about making data beautiful
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- 13. Getting to GoodData Data Visualization Data Collection Measurement Plan Business Requirements Goals = Metrics Develop Trust Build Reporting
- 14. Our Process ForBuildingVisualizations 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 • Providesa 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
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- 18. BuildingYourTeam SME Data Designer Engineer Knows which questions toask Knows how to get the data Knows how to present the data
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- 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 toPositioning • 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 forComparisons • 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 withShapes • 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 ofPie 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 Numberof 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 withColor • 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
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- 30. Data Storytelling Annotation Sequencing Data Journalism Lowerthe 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
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- 35. Use color for emphasis Complexdata organized for easy consumption Clean design with low data/ink ratio
- 36. Takeaways / Q&As Thedata 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 JFKBlvd., Suite 201 Philadelphia, PA 19103 p: (215) 545-1515 www.maassmedia.com