Data Visualization Design Best Practices WorkshopJSI
This introduction was presented as part of a workshop at the Measurement and Accountability for Results in Health Summit at the World Bank (June 2015). The workshop focused on simple ways anyone working with data can improve their presentations, and included visualization redesign activity to put these principles in practice.
Data visualizations make huge amounts of data more accessible and understandable. Data visualization, or "data viz," is becoming largely important as the amount of data generated is increasing and big data tools are helping to create meaning behind all of that data.
This SlideShare presentation takes you through more details around data visualization and includes examples of some great data visualization pieces.
North Raleigh Rotarian Katie Turnbull gave a great presentation at our Friday morning extension meeting about data visualization. Katie is a consultant at research and advisory firm, Gartner, Inc.
Data Visualization Design Best Practices WorkshopJSI
This introduction was presented as part of a workshop at the Measurement and Accountability for Results in Health Summit at the World Bank (June 2015). The workshop focused on simple ways anyone working with data can improve their presentations, and included visualization redesign activity to put these principles in practice.
Data visualizations make huge amounts of data more accessible and understandable. Data visualization, or "data viz," is becoming largely important as the amount of data generated is increasing and big data tools are helping to create meaning behind all of that data.
This SlideShare presentation takes you through more details around data visualization and includes examples of some great data visualization pieces.
North Raleigh Rotarian Katie Turnbull gave a great presentation at our Friday morning extension meeting about data visualization. Katie is a consultant at research and advisory firm, Gartner, Inc.
This is a presentation I gave on Data Visualization at a General Assembly event in Singapore, on January 22, 2016. The presso provides a brief history of dataviz as well as examples of common chart and visualization formatting mistakes that you should never make.
Best Practices for Killer Data VisualizationQualtrics
There’s something special about simple, powerful visualizations that tell a story. In fact, 65% of people are visual learners.
Join Qualtrics and Sasha Pasulka from Tableau as we illuminate the world of data visualization and give you clear takeaways to help you tell a better story with data. Getting executive buy-in or that seat at the table may come down to who can visualize data in a way that excites and enlightens the audience.
Introduction on Data Visualization. Importance of Data Visualization. Data Representation Criteria. Groundwork for data visualization. Some Data Visualization tools to start with
Data Visualization 101: How to Design Charts and GraphsVisage
Learn to design effective charts and graphs.
Your data is only as good as your ability to understand and communicate it. The right visualization is essential to incite a desired action, whether from customers or colleagues. But most marketers aren’t mathematicians or adept at data visualization. Fortunately, you don’t need a PhD in statistics to crack the data visualization code.
This slide deck gives a general overview of Data Visualization, with inspiring examples, the strength and weaknesses of the human visual system, a few technical frameworks that may be used for creating your own visualizations and some design concepts from the data visualization field.
Advanced data visualization (ADV) is a rapidly emerging concept in business and society and has a lofty goal of transforming data into information. But how do we get there?
This is a presentation I gave on Data Visualization at a General Assembly event in Singapore, on January 22, 2016. The presso provides a brief history of dataviz as well as examples of common chart and visualization formatting mistakes that you should never make.
Best Practices for Killer Data VisualizationQualtrics
There’s something special about simple, powerful visualizations that tell a story. In fact, 65% of people are visual learners.
Join Qualtrics and Sasha Pasulka from Tableau as we illuminate the world of data visualization and give you clear takeaways to help you tell a better story with data. Getting executive buy-in or that seat at the table may come down to who can visualize data in a way that excites and enlightens the audience.
Introduction on Data Visualization. Importance of Data Visualization. Data Representation Criteria. Groundwork for data visualization. Some Data Visualization tools to start with
Data Visualization 101: How to Design Charts and GraphsVisage
Learn to design effective charts and graphs.
Your data is only as good as your ability to understand and communicate it. The right visualization is essential to incite a desired action, whether from customers or colleagues. But most marketers aren’t mathematicians or adept at data visualization. Fortunately, you don’t need a PhD in statistics to crack the data visualization code.
This slide deck gives a general overview of Data Visualization, with inspiring examples, the strength and weaknesses of the human visual system, a few technical frameworks that may be used for creating your own visualizations and some design concepts from the data visualization field.
Advanced data visualization (ADV) is a rapidly emerging concept in business and society and has a lofty goal of transforming data into information. But how do we get there?
Shared at "Data-Driven Design for User Experience" with Le Wagon Tokyo, 25 Aug
https://www.meetup.com/ja-JP/Le-Wagon-Tokyo-Coding-Station/events/280067831/
In UX design, data means the voice of users (customers) and actionable insights that are beyond just numbers. Hearing these voices through user research and usage analytics is a critical process of building a human-centric design. Based on data-driven design, UX designers, product managers, and even senior management can listen to the inner voice of users and extrapolate those to discover a user journey for clear call-to-action and unwavering customer loyalty.
At this webinar, our guest speaker Emi Kwon, UX Design Director at Metlife, will walk you through the basics of data-driven design as well as share some tips and tricks for making data-driven design your value proposition as a product manager/ UX specialist.
Agenda:
✔️ Data ecosystem — Data lake, data warehouse…what does it mean for UX?
✔️ Small data and big data — the opportunities and pitfalls
✔️ Research method basics — qualitative, quantitative or triangulated
✔️ Usage analytics and A/B testing
✔️ What about COVID-19 and remote usability testing?
This will explain you what is data visualization,why we need it,what are the technologies in it ,tools available for it and it ends up with how can we get the excellence in visualization
1Dr. LaMar D. Brown PhD, MBAExecutive MSITUnivEttaBenton28
1
Dr. LaMar D. Brown PhD, MBA
Executive MSIT
University of the Cumberlands
Course: 2019-SPR-IG-ITS530-21: 2019_SPR_IG_Analyzing and Visualizing Data_21
Chapter Readings Reflections Journal
Chapter 1: Defining Data Visualization
Summary
In Chapter 1, the author Mr. Kirk describes about the concept of Data Visualization. Data visualization was defined as the visual analysis and communication of data. The chapter also included the historical background survey definition of data visualization by various other authors.
Also, in the book was a set of fascinating recipes that of the components in that involve in the definition. The type of data that is required to be visually analyzed is important before it is being subjected to further processing before visualization.
Mr. Kirk also emphasized the significance of the art and science of making data analysis a fun filled technical and an analytical reading that encourages the use of human perception to make decisions in assistance of visual treats that come in the form of graphs, pie charts among others. The science of data visualization is defined with the implication of truth, evidence and rules that govern the process of visualizing a set of data that can be quintessential in determining the path of an enterprise or an organization.
Highlights:
Upon reading the chapter 1 in this book that was in depth into data visualization, I was able to grasp essential technical and analytical definitions and can say they are quiet telling in terms of the importance on the concept and visual representation of the definitions. The use of some of the citations was a key indicator that data visualization can be defined in various ways and can assist in technical improvements if used in way that is beneficial to all parties.
Ideas and thoughts:
The chapter was a thorough analysis of the concept. However, I was also keen on looking for live examples of visual tools or results of analysis inculcated in this defining place of the book. The big positive is the use of the concept of science and art that can be implemented in the day to day activities to introduce data visualization in any area and can help in making decisions that can set a trend for the growth of an organization. In terms of the course, it was a great read to write this review journal and can hopefully add a firm base to the things to come.
Application:
The concept of data visualization can be implemented in my current work environment. As an IT personnel, I deal with the network infrastructure and constantly come across large chunk of data that will need to be analyzed for its usage stats, bandwidth, performance and benefits of choosing the hardware or software accordingly. To best impact this, the monitoring tools such a s NetFlow helps us in verifying bandwidth over utilization or underutilization to perform a set of tasks before troubleshooting any related issues. Now, the concept of data visualization can be implemented here ...
Data visualization trends in Business Intelligence: Allison Sapka at Analytic...Fitzgerald Analytics, Inc.
Allison Sapka's presentation at the Analytics and Data in Financial Services Meetup in Dec 2012. Alison discusses trends in Data Visualization, including why visualization is so powerful when implemented well, and confusing or misleading when done badly
Overview on data collection methods and a deep dive on data (primary Vs secondary, qualitative and quantitative). Bias. Data processing and structured, unstructured, semistructured data. Databases jargon.
Linked Data and examples, why they matter. Data driven strategies. Data mining: laws and applications. Data aggregation and fundamentals of data representation (table, bar chart, histogram, pie chart, line graph, scatter plot). Data science definition and job roles (who does what).
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
As Europe's leading economic powerhouse and the fourth-largest hashtag#economy globally, Germany stands at the forefront of innovation and industrial might. Renowned for its precision engineering and high-tech sectors, Germany's economic structure is heavily supported by a robust service industry, accounting for approximately 68% of its GDP. This economic clout and strategic geopolitical stance position Germany as a focal point in the global cyber threat landscape.
In the face of escalating global tensions, particularly those emanating from geopolitical disputes with nations like hashtag#Russia and hashtag#China, hashtag#Germany has witnessed a significant uptick in targeted cyber operations. Our analysis indicates a marked increase in hashtag#cyberattack sophistication aimed at critical infrastructure and key industrial sectors. These attacks range from ransomware campaigns to hashtag#AdvancedPersistentThreats (hashtag#APTs), threatening national security and business integrity.
🔑 Key findings include:
🔍 Increased frequency and complexity of cyber threats.
🔍 Escalation of state-sponsored and criminally motivated cyber operations.
🔍 Active dark web exchanges of malicious tools and tactics.
Our comprehensive report delves into these challenges, using a blend of open-source and proprietary data collection techniques. By monitoring activity on critical networks and analyzing attack patterns, our team provides a detailed overview of the threats facing German entities.
This report aims to equip stakeholders across public and private sectors with the knowledge to enhance their defensive strategies, reduce exposure to cyber risks, and reinforce Germany's resilience against cyber threats.
StarCompliance is a leading firm specializing in the recovery of stolen cryptocurrency. Our comprehensive services are designed to assist individuals and organizations in navigating the complex process of fraud reporting, investigation, and fund recovery. We combine cutting-edge technology with expert legal support to provide a robust solution for victims of crypto theft.
Our Services Include:
Reporting to Tracking Authorities:
We immediately notify all relevant centralized exchanges (CEX), decentralized exchanges (DEX), and wallet providers about the stolen cryptocurrency. This ensures that the stolen assets are flagged as scam transactions, making it impossible for the thief to use them.
Assistance with Filing Police Reports:
We guide you through the process of filing a valid police report. Our support team provides detailed instructions on which police department to contact and helps you complete the necessary paperwork within the critical 72-hour window.
Launching the Refund Process:
Our team of experienced lawyers can initiate lawsuits on your behalf and represent you in various jurisdictions around the world. They work diligently to recover your stolen funds and ensure that justice is served.
At StarCompliance, we understand the urgency and stress involved in dealing with cryptocurrency theft. Our dedicated team works quickly and efficiently to provide you with the support and expertise needed to recover your assets. Trust us to be your partner in navigating the complexities of the crypto world and safeguarding your investments.
Opendatabay - Open Data Marketplace.pptxOpendatabay
Opendatabay.com unlocks the power of data for everyone. Open Data Marketplace fosters a collaborative hub for data enthusiasts to explore, share, and contribute to a vast collection of datasets.
First ever open hub for data enthusiasts to collaborate and innovate. A platform to explore, share, and contribute to a vast collection of datasets. Through robust quality control and innovative technologies like blockchain verification, opendatabay ensures the authenticity and reliability of datasets, empowering users to make data-driven decisions with confidence. Leverage cutting-edge AI technologies to enhance the data exploration, analysis, and discovery experience.
From intelligent search and recommendations to automated data productisation and quotation, Opendatabay AI-driven features streamline the data workflow. Finding the data you need shouldn't be a complex. Opendatabay simplifies the data acquisition process with an intuitive interface and robust search tools. Effortlessly explore, discover, and access the data you need, allowing you to focus on extracting valuable insights. Opendatabay breaks new ground with a dedicated, AI-generated, synthetic datasets.
Leverage these privacy-preserving datasets for training and testing AI models without compromising sensitive information. Opendatabay prioritizes transparency by providing detailed metadata, provenance information, and usage guidelines for each dataset, ensuring users have a comprehensive understanding of the data they're working with. By leveraging a powerful combination of distributed ledger technology and rigorous third-party audits Opendatabay ensures the authenticity and reliability of every dataset. Security is at the core of Opendatabay. Marketplace implements stringent security measures, including encryption, access controls, and regular vulnerability assessments, to safeguard your data and protect your privacy.
3. INTRODUCTION
HELLO!
‣ Who are you?
‣ What do you do?
‣ What’s your learning goal for today?
‣ Is there a topic you’d like to
visualize in the exercise today?
3
4. Sections:
1) What is Data Visualization?
2) Data Visualization Purposes
3) Data and Design
4) People and Process
5) Examples to Discuss
6) Class Exercise
7) Resources and Conclusions
4
5. CLASS EXERCISE PRELIMINARIES
DISCUSSION
Toward the end of class, we’re going to split up into groups and create data visualization concept
designs. As we go through each section, think about applying the ideas we cover to a project you might
choose.
Topic suggestion for the final exercise - create a visualization that shows how a series of events
unfolds over time. Be creative. It doesn’t have to be just a timeline on an x-axis.
This can be applied to many areas including - business (e.g., patterns of timing from VC funding to
IPO), sports (e.g., changes ball possession during a game), medicine (e.g., the spread of an epidemic)
START THINKING…
5
6. KEY QUESTIONS TO ADDRESS IN YOUR PROJECTS
‣ What is the purpose/value of the visualization?
‣ Who are the intended users?
‣ How was the data selected and acquired?
‣ What design elements were used and why?
CLASS EXERCISE PRELIMINARIES 6
7. ! We’re only scratching the
surface of every topic
presented here
! The main goal is for you to
look at data visualization
with a holistic perspective
! Whatever your levels of
skill and experience are,
you have something to
offer
KEEP IN MIND… 7
9. 9
VISUALIZATIONS MAKE IT EASIER TO SEE
PATTERNS IN DATA
SECTION 1: WHAT IS DATA VISUALIZATION?
http://data.oecd.org/healthcare/child-vaccination-rates.htm
10. The key to effectively exposing
meaningful patterns in data comes
down to thoughtful visual encoding.
http://www.gapminder.org/
SECTION 1: WHAT IS DATA VISUALIZATION? 10
14. Design decisions have a
big impact on what
people will see in the
data.
SECTION 1: WHAT IS DATA VISUALIZATION? 14
720349656089226535931140790070
720349656089226535931140790070
15. A substantial portion of the human brain is devoted to visual processing
Source:
http://www.flickr.com/photos/orangeacid/234358923/
Creative Commons Attribution License
Source:
http://en.wikipedia.org/wiki/File:Brodmann_areas_17_18_19.png
GNU Free Documentation License
WE ARE WIRED FOR VISUALIZATION
10 Million Bits
Per Second
Source:
Current Biology (July 2006) by Judith McLean
and Michael A. Freed
SECTION 1: WHAT IS DATA VISUALIZATION? HUMAN BRAIN 15
16. TAPPING IN TO OUR PERCEPTUAL POWERS
The pop-out effects are due to your brain’s pre-attentive processing
SECTION 1: WHAT IS DATA VISUALIZATION? PRE-ATTENTIVE PROCESSING 16
COLOR HUE ORIENTATION TEXTURE POSITION & ALIGNMENT
COLOR BRIGHTNESS COLOR SATURATION SIZE SHAPE
17. What is easier to
distinguish here - color
or shape differences?
Some attributes pop out more
than others.
17SECTION 1: WHAT IS DATA VISUALIZATION? PRE-ATTENTIVE PROCESSING
20. SECTION 1: DATA VISUALIZATION PROCESS AND PRACTICES
Adapted from Stephen Few.
20
21. PUTTING THE PIECES TOGETHER
The components of visualizations fit into a larger context of goals, users,
and the media in which they are presented.
SECTION 1: WHAT IS DATA VISUALIZATION? BUILDING OUT 21
23. Overview first, zoom and filter, then details-on-demand.
‣ Time Series and Event Sequences
‣ Part-to-Whole
‣ Geospatial
‣ Ranking
‣ Distribution
‣ Correlation
‣ Deviation
‣ Nominal Comparison
There can be overlaps in what can be shown and related
in one visualization
I CAN RELATE!
SECTION 2: DATA VISUALIZATION PURPOSES 23
24. 24
TIME-SERIES GRAPH
SECTION 2: DATA VISUALIZATION PURPOSES
http://www.businessinsider.com/india-and-america-come-meet-mum-2015-1
30. 30
FOR A DEEPER DIVE INTO
TEMPORAL DATA VIS..
http://www.oreilly.com/pub/e/3139
http://uxmag.com/articles/its-about-time
SECTION 2: DATA VISUALIZATION PURPOSES
31. Overview first, zoom and filter, then details-on-demand.
PART-TO-WHOLE: A TREEMAP OF TITANIC PROPORTIONS
SECTION 2: DATA VISUALIZATION PURPOSES 31
Overview first, zoom and filter, then details-on-demand.
Source: http://blog.visual.ly/the-whole-story-on-part-to-whole-relationships/
32. PART-TO-WHOLE: OTHER EXAMPLES
SECTION 2: DATA VISUALIZATION PURPOSES 32
* Source: http://blog.visual.ly/the-whole-story-on-part-to-whole-relationships/
**
Pie Stacked Area
Parallel Sets Sankey Diagram
33. FRUIT TREEMAPS: HIERARCHY AND PROPORTIONS
SECTION 2: DATA VISUALIZATION PURPOSES 33
Source: Data Insights: New Ways to Visualize and Make Sense of Data, by Hunter Whitney, Morgan Kaufmann; 2012."
34. 34SECTION 2: DATA VISUALIZATION PURPOSES
GEOSPATIAL: THE POLITICAL LANDSCAPE
40. SECTION 2: DATA VISUALIZATION PURPOSES 40
NOMINAL COMPARISON: BAR CHART
Source: Data Insights: New Ways to Visualize and Make Sense of Data, by Hunter Whitney, Morgan Kaufmann; 2012."
41. 41
DIFFERENT PERSPECTIVES: NOMINAL COMPARISON AND
PART-TO-WHOLE
Source: Data Insights: New Ways to Visualize and Make Sense of Data, by Hunter Whitney, Morgan Kaufmann; 2012."
SECTION 2: DATA VISUALIZATION PURPOSES
42. CLASS EXERCISE (KEEP IN MIND)
DISCUSSION KEY QUESTIONS TO ADDRESS
‣ What are the main functions
(e.g., exploratory, tracking,
explanatory, etc.?)
‣ What kinds of design elements
might you want to use?
‣ What level of interactivity
might be good to include?
For whichever subject area you choose, think about the
basic design elements and functions that might work
best. These questions will come into sharper focus as
you learn more about the goals of the users.
CONSIDERATIONS FOR YOUR CLASS PROJECT
42
43. SECTION 3: DATA AND
DESIGN
INTRODUCTION TO DATA VISUALIZATION 43
45. THE MARRIAGE OF DESIGN AND DATA
DATA CAN BE BROKEN INTO TWO MAJOR CLASSES: DISCRETE AND CONTINUOUS
45
Source: Data Insights: New Ways to Visualize and Make Sense of Data, by Hunter Whitney, Morgan Kaufmann; 2012."
SECTION 3: DATA AND DESIGN
46. THE MARRIAGE OF DESIGN AND DATA
46
Source: Data Insights: New Ways to Visualize and Make Sense of Data, by Hunter Whitney, Morgan Kaufmann; 2012."
SECTION 3: DATA AND DESIGN
47. Nominal Scale: This is simply putting items
together without ordering or ranking them (e.g.,
an apple, an orange, and a tomato).
Ordinal Scale: Elements of the data describe
properties of objects or events that are ordered by
some characteristic.
THE MARRIAGE OF DESIGN AND MEASUREMENTS
47
Source: Data Insights: New Ways to Visualize and Make Sense of Data, by Hunter Whitney, Morgan Kaufmann; 2012."
SECTION 3: DATA AND DESIGN
48. Interval Scale: These are data that are
measured on some kind of scale, often
temporal (e.g., the days of the week, hours of
the day).
THE MARRIAGE OF DESIGN AND MEASUREMENTS
Ratio Scale: An ordered series of numbers
assigned to items (objects, events, etc.)
that allow for estimating and comparing
different measures in terms of multiples,
such as “half as many” or “four times as
heavy.”
48
Source: Data Insights: New Ways to Visualize and Make Sense of Data, by Hunter Whitney, Morgan Kaufmann; 2012."
SECTION 2: DATA VISUALIZATION PURPOSES
49. STATISTICAL SUMMARIZATION AND ANALYSIS
Visualizations can clarify or obscure the statistical summarization of
http://blog.visual.ly/using-visual-reasoning-to-understand-numbers/
49SECTION 3: DATA AND DESIGN
50. 50
Source: Reprinted in Data Insights: New Ways to Visualize and Make Sense of Data, by Hunter Whitney, Morgan Kaufmann; 2012."
SECTION 3: DATA AND DESIGN
52. Think about good design practices: selective labeling
52
Schwabish, Jonathan A. 2014. "An Economist's Guide to Visualizing Data." Journal of Economic Perspectives, 28(1): 209-34. DOI: 10.1257/jep.28.1.209
SECTION 3: DATA AND DESIGN
53. Which one
is bigger?
A B
A
B
53
Think about good design practices: proximity
SECTION 3: DATA AND DESIGN
54. Think about good design practices: multiples
54
Schwabish, Jonathan A. 2014. "An Economist's Guide to Visualizing Data." Journal of Economic Perspectives, 28(1): 209-34. DOI: 10.1257/jep.28.1.209
SECTION 3: DATA AND DESIGN
55. 55SECTION 3: DATA AND DESIGN
Source: Data Insights: New Ways to Visualize and Make Sense of Data, by Hunter Whitney, Morgan Kaufmann; 2012."
COLOR AND VALUE
http://blog.visual.ly/building-effective-color-scales/
58. Idea: Forms or patterns transcend the stimuli used to
create them.
Why do patterns emerge?
Under what circumstances?
Principles of Pattern Recognition:
“Gestalt” is German for “pattern” or “form,
configuration”.
GESTALT PRINCIPLES
http://sixrevisions.com/web_design/gestalt-principles-applied-in-design/http://graphicdesign.spokanefalls.edu/tutorials/process/gestaltprinciples/gestaltprinc.htm
58SECTION 3: DATA AND DESIGN
59. What do you see here?
http://sixrevisions.com/web_design/gestalt-principles-applied-in-design/
59SECTION 3: DATA AND DESIGN
60. ‣ How do you design the “perfect” visualization?
‣ There’s no perfect visualization: the design space is just too big!
‣ But it’s up to you to design the one that fits...
60SECTION 3: DATA AND DESIGN
61. ! Visualization Display Choices
http://scitechdaily.com/scientists-manage-flood-big-data-space/ http://www.steema.com/tags/mobile
61SECTION 3: DATA AND DESIGN
64. SECTION 4: PEOPLE AND PROCESS 64
http://cnr.ncsu.edu/geospatial/wp-content/uploads/sites/6/2014/02/earth_observation-574_crop1-1500x600.jpg
65. VISUALIZATION IS ONLY THE TIP
OF THE ICEBERG
Data visualization is only a part of a
much larger process that includes
identifying the purpose of the
visualization, the kinds of people who
will use it, the types of data that can
be collected and analyzed, and good
design choices.
65SECTION 4: PEOPLE AND PROCESS
66. VISUALIZATION IS
PART OF AN
ITERATIVE PROCESS
66
Source: Data Insights: New Ways to Visualize and Make Sense of Data, by Hunter Whitney, Morgan Kaufmann; 2012."
SECTION 4: PEOPLE AND PROCESS
67. PERSPECTIVE: BIOTECHNOLOGY EXECUTIVE
67
‣ “We usually have an underlying narrative or hypothesis that is driving the
analysis, but even with that you have to be ready for a surprise. Be willing to
go where the data leads you, provided you have good data from multiple
sources.”
‣ “We try to have teams involved in the data collection and analysis process
‘from soup to nuts’. If people join only at the end of the process, you could be
setting yourself up for failure.”
‣ “If you rely on just one data set, you can be totally misled.”
SECTION 4: PEOPLE AND PROCESS
68. ROLE
• RESEARCHER
• PUBLIC
PRIOR KNOWLEDGE
• NONE
• SUBJECT EXPERT
USE FREQUENCY
• ONCE A DECADE
• EVERY HOUR
USERS
USER QUESTION 1 - WHO VIEWS THE DATA?
68SECTION 4: PEOPLE AND PROCESS
69. PURPOSE
HYPOTHESIS?
• WHAT ARE WE
TRYING TO LEARN OR
SHOW?
• HOW DO WE KNOW
IF WE ACHIEVED IT?
GOAL?
• WHAT ARE THE
BOUNDARIES?
PARAMETERS?
69SECTION 4: PEOPLE AND PROCESS
70. DATA QUESTION 1 - WHO OWNS IT?
PRIMARY
• YOU COLLECT IT
• YOU OWN IT
• NOBODY ELSE HAS IT
• OTHERS COLLECT IT
• OTHERS OWN IT
• OTHERS HAVE IT
SECONDARY
DATA
70SECTION 4: PEOPLE AND PROCESS
71. DATA QUESTION 2 - DOES IT CHANGE?
DYNAMIC
• CHANGES OFTEN
• COLLECTED OFTEN
• TIME WINDOW
MATTERS
• DOES NOT CHANGE
• COLLECT IT ONCE
• TIME WINDOW
MATTERS
STATIC
DATA
71SECTION 4: PEOPLE AND PROCESS
73. USER CONTROL:
HIGH
STATIC
EXPLAINEXPLORE
(e.g., data-intensive research
applications)
(e.g., print infographic
advocacy )
(e.g., interactive infographic
journalism)
(e.g., data-rich visualizations with
limited interactivity)
DYNAMIC
USER CONTROL:
LOW
73SECTION 4: PEOPLE AND PROCESS
75. SECTION 5: EXAMPLES TO DISCUSS 75
After Nate Silver moved on to other things,
New York Times filled the gap with a data-
centric journalism section called “The
Upshot.”
Let’s discuss, deconstruct, and critique a few
examples from the site. These are screen
shots to you may not have full context, but
let’s see how these visualizations stand up.
You might want to visit the site and play with
it more on your own and practice evaluation
it based on what we’ve already discussed.
http://www.nytimes.com/upshot/
82. ‣ Get into groups 4 or more, and discuss the ideas and examples you
have in mind.
‣ Then...
• Select the purpose, audience, and data you want to use for a
visualization
• Design the visualization on the provided poster paper
• Be ready to share your results and describe your thought process
EXERCISE IDEA: THINK TIME
82SECTION 6: CLASS EXERCISE
86. DATA VISUALIZATION RESOURCES
‣ Flowing Data (http://flowingdata.com/
‣ Fast Company Co.design (http://www.fastcodesign.com/)
‣ UX Magazine (http://uxmag.com/)
‣ The Human-Computer Interaction Lab (http://www.cs.umd.edu/hcil/)
‣ A Periodic Table of Visualization Methods (www.visual-literacy.org/
periodic_table/periodic_table.html)
Sites:
86SECTION 7: RESOURCES AND CONCLUSIONS
87. DATA VISUALIZATION BOOKS:
‣ Bertin, J. (2011). Semiology of graphics: Diagrams, networks, maps. (Berg, W. J., Trans.) Redlands, CA: Esri
Press. (Original work published 1965)
‣ Card, S. K., Mackinlay, J. D., & Shneiderman, B. (Eds.). (1999). Readings in information visualization: Using
vision to think. San Francisco, CA: Morgan Kaufmann Publishers.
‣ Few, S. C. (2009). Now you see it: Simple visualization techniques for quantitative analysis. Oakland, CA:
Analytics Press.
‣ Few, S. C. (2004). Show me the numbers: Designing tables and graphs to enlighten. Oakland, CA: Analytics
Press.
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87SECTION 7: RESOURCES AND CONCLUSIONS
89. CONCLUDING THOUGHTS
•Data visualization involves learning about the rules and the process
•Start with the problem, not with the data or the visualization
•Think big: find the data you need
•Visualize your data in multiple ways
•Know your audience and their goals
89SECTION 7: RESOURCES AND CONCLUSIONS
90. Keep in mind - the value of data depends on what you do with it
90
Source: Reprinted in Data Insights: New Ways to Visualize and Make Sense of Data, by Hunter Whitney, Morgan Kaufmann; 2012.
SECTION 7: RESOURCES AND CONCLUSIONS