Leveraging Data. Data Visualization Best Practices

Transcription

1 Leveraging Data Data Visualization Best Practices 1

2 Introductions Greg Kiefer Kiefer Consulting, Inc. Chris Hughes Oracle Bradley Burch Kiefer Consulting, Inc. Paul Flanigan California Department of Motor Vehicles 2

3 Agenda A B C D i Introductions / Goals Theory of Data Visualization Putting Data Visualization into Practice Use Case: DMV Appendix: Sample Rules and Selections 3

4 A) Introductions and Goals Introductions Goals Bibliography 4

5 Bibliography: Reference 1 Reference 1 Data Visualization: a successful design process Andy Kirk 5

6 Bibliography: Reference 2 Reference 2 Designing Data Visualizations Intentional Communication from Data to Display Noah Illinsky & Julie Steele 6

7 B) Theory of Data Visualization Definitions Basic Rules Visual Grammar (Do s and Don ts) 7

8 Theory of Data Visualization Key Definitions Visualizations will have a core focus or intent: to Explain, Explore, or Exhibit There are 2 main data entities in a data set for a visualization: Data Visualization helps people understand data using visual analysis Patterns, trends and correlations are much easier to understand visually Dimensions: groupings or attributes Facts: numeric measurement or observations There are 4 main data types in a visualization: quantitative, ordinal, categorical, or relational 8

9 Theory of Data Visualization Basic Best Practices 1. Know your Goals What are the goals of your agency? What are the goals of your visualization? 4. Format & Consumption How will they consume? Tooling (Interactive/Static) Form Factors (Mobile/Desktop/Paper) 2. Know your Audience Why will they consume your visualization? What might they ask for next? 5. Develop Proper Context Balanced Data Model to develop multiple contexts Create Facts and Dimensions Data Timeliness / Latency 3. Decide on the Intent Explanatory Exploration Exhibition 6. Apply Visual Grammar Be Truthful / Direct Maximize Data Ink Use Statistical Ink Vary Color, Shape, Width Time and Movement 9

10 Theory of Data Visualization 3. Decide Your Intent: Explanatory Example Explanatory data visualization is about conveying information to a reader in a way that is based around a specific and focused narrative. It requires a designer-driven, editorial approach: What are your requirements in delivering information? How do you synthesize and perfect the information and encode the data to provide key insights to your reader What are the most important analytical dimensions you want to convey? - Kirk, Data Visualization: A Successful Design Process 10

11 Theory of Data Visualization 3. Decide Your Intent: Exploration Examples Exploratory data visualizations are appropriate when you have a whole bunch of data and you re not sure what s in it. - Kirk, Data Visualization: A Successful Design Process 11

12 Theory of Data Visualization 3. Decide Your Intent: Exhibition Example 12

13 Theory of Data Visualization 3. Decide Your Intent: Some can be all three 13

14 Theory of Data Visualization 6. Visual Grammar: Encoding Techniques Apply visual encoding to your data types Some techniques are better suited to different data types and quantities There are a common set of rules or guidelines you should follow Most Visualization tools will let you cycle through the encodings quickly to test Type Examples Quantitative % Ordinal M/T/W/R/F 1,2,3,4,5 Categorical Relational Good/Bad/Bigly Red/Blue Input/Output Contains/Inside 14

15 Theory of Data Visualization 6. Visual Grammar: Maximize Data and Statistical Ink avg: ? max: 5786 avg: 5229 A thing of beauty, no? Remove non-pertinent ink (labels, color, grid, 3D) We look at the W/U Curve then Days of Week Reduce canvas height Reorder by Metric ascending Remove all Grids Modify Scale of the Graph* Some Redundancy might help Keep data sort as ordinal Remove Color Use Statistical Ink for Redundancy purposes Review your Scales 15

16 Theory of Data Visualization 6. Visual Grammar: Do s and Don ts Packed bubble chart is nothing more than eye-candy or bling Stephen Few, data visualization expert The only thing worse than one pie chart is lots of them. Edward Tufte Professor emeritus of political science, statistics, and computer science at Yale University 16

17 Theory of Data Visualization Conclusion Talk To Me Listen to Bradley My Office Hours Anytime Oracle Data Visualization Your Homework Edward Tufte Ralph Kimball Stephen Few Mike Bostock / D3 Slide Review of Tufte s Maxims ColorBrewer Desktop Mobile Cloud Embedded Lets See What Bradley has Put into Practice Download it or signup for Cloud for 30 day evaluation 17

18 C) Putting Visualization into Practice Demonstration 18

19 We all work with data SHOW OF HANDS. Where is your data coming from? Microsoft Excel? Database? Application? Open Data Business intelligence tools allow you to take tabular data and create dynamic and interactive data visualizations. 19

20 The Process: How we built the Healthcare Services / Community Services Dashboards Tabular Data Leverage Power BI to create data visualizations Interactive Dashboards Data visualizations that allow users to filter and find specific information California Health & Human Services (CHHS) Open Data Power BI 20

21 What We Did We took this data: Community Services Resources Department of Community Services and Development publishes a list of resources and locations We Built: Interactive Resource Map Powered by Power BI Simple dashboard Works across all devices We took this data: Public Health Services Resources Department of Public Health publishes a list of resources and locations Pulling data from the CA Open Data Portal Data refreshes (up to date information) See the dashboard at: 21

22 Aligning With Best Practices Goals Audience Intent Format & Consumption Context Visual Grammar Make data more meaningful and accessible Citizens Doctors and providers that refer citizens to healthcare and community services Explanatory: Simple visualization to identify the services that are available Exploration: Demonstrate wide range of services and their distribution on a map Data visualization that can accessed on a tablet, phone or a desktop computer Easy to navigate dashboard that allows users to expand and filter based on specific criteria Use of color coding, maps and intuitive controls Use of icons to bring context Search features to enable users to find specific information 22

23 The Power of Dynamic Data What type of community services? Which Counties have community Services? Where are the community services? 23

24 Interactive Best Practices Exploratory Facts Descriptive Visual Grammar Icons Dimensions Categorical 24

25 The Value Easy to navigate dashboard Geolocation allows users to pinpoint services available in a geographic area Runs on any device Data is refreshed Help citizens find services they need Maps help visualize the services available in specific areas 25

26 The Value Delivering Information in a consumable format Easy to understand Relevant Information Quickly see what services are available from DPH and CSD Built using PowerBI 26

27 Benefits of Using Data Visualization Government Agencies Transparency in Government Delivering information to constituents Self-Service Give power to the user to analyze and model Interactive Dashboards Let BI help you deliver useful information Gain Insights Enable the enterprise to answer questions quickly Improve Data Quality Find inconsistencies or inaccuracies in a data set Better Understand Your Business Use historical data to understand your business, identify trends, and maximize efficiencies 27

28 D) Use Case: DMV An example 28

29 I HATE WAITING 29

30 Process Map TOTAL CUSTOMER TIME Customer Enters Field Office Start Here Window Processing Photo Test Tracked Time Tracked Time Hidden Wait Time Hidden Wait Time What We Can Control 30

31 # Customers Histogram BEFORE AFTER Total Time (Minutes) 45 Minutes

32 Pareto Chart of Time BEFORE AFTER Customer Enters DMV Customer Completes Transaction Before getting ticket (25%) Between getting ticket and Seeing MVR (48%) MVR (17%) Photo (10%) Between SH and first MVR Between arrival and SH MVR Photo 73% Wait Time 27% Action 32

33 # Customers Revised Histogram BEFORE AFTER Total Time (Minutes) 45 Minutes

34 # Minutes Bar Chart: Avg. Wait Times (Before & After) 80 Customer Flow Stages Average Wait Time (SH to MVR) Average Pre-SH Wait Average MVR Transaction Time Average Total Customer Time Old New 34

35 Zip Code Survey Based on zip codes from customers, shows distance customers may travel to get to San Jose post pilot. Red dots indicate field offices. Large red dot is San Jose.

36 Aligning With Best Practices Superior Customer Service. Goals Audience Intent Leadership General Organization Public Explanatory: Simple visualization to identify changes in processing. Format & Consumption Context KISS! Facts / Dimensions Visual Grammar Simplified presentation of data that drives conversation. Get buy-in that we can change. Drive desire to know more (data). Stop the waiting. 36

37 Boneyard Slides that may or may not be used 37

38 Theory of Data Visualization 6. Visual Grammar: Do s and Don ts The 3-D bars are impossible to read. The heavy grid lines offer nothing but distraction. The vertically-oriented labels (i.e., the resort names and years) are difficult to read. The years run from back to front, which is counterintuitive. The bright bar colors have been replaced with variations in gray-scale The three resorts have been arranged in order of rank, based on revenue, to highlight their comparative performance. The years have been arranged from left to right, which is intuitive. 38

39 Theory of Data Visualization 6. Visual Grammar: Do s and Don ts Color 3 Dimensions (Faux) Broken Scale 39