Columbia University · Spring 2021 · Course: 20211APAN5800KH02
This course is at the intersection of many professional disciplines: professional writing, data science, visualization, and design, to name a few. Bringing these all together well is difficult but rewarding.
The students who will get most from my course, for whom I have in mind as my students, are curious active learners:
An active learner asks questions, considers alternatives, questions assumptions, and even questions the trustworthiness of the author or speaker. An active learner tries to generalize specific examples, and devise specific examples for generalities.
An active learner doesn’t passively sponge up information — that doesn’t work! — but uses the readings and lecturer’s argument as a springboard for critical thought and deep understanding.
This course isn’t meant to be an end, but a beginning, giving you hand-selected, seminal and cutting-edge references for the concepts discussed. Go down these rabbit holes, following citations and studying the discussed material. Becoming an expert in storytelling with data also requires practicing. Indeed,
Learners need to practice, to imitate well, to be highly motivated, and to have the ability to see likenesses between dissimilar things in [domains ranging from creative writing to mathematics].
Berys Gaut, “Educating for Creativity,” in The Philosophy of Creativity: New Essays, ed. Elliot Samuel Paul (New York: Oxford University Press, 2014), 265–87. You may find some concepts difficult or vague on a first read or discussion. For that, I’ll offer encouragement from Robert P Abelson Statistics as Principled Argument (Psychology Press, 1995).,
I have tried to make the presentation accessible and clear, but some readers may find a few sections cryptic …. Use your judgment on what to skim. If you don’t follow the occassional formulas, read the words. If you don’t understand the words, follow the music and come back to the words later.
This first topic introduces the communication process and the scaffolding of the course. We will also get to know each other and share some of our professional background and experiences. Then, we begin discussing the scope of an analytics project, audiences, and challenges we face in communicating with them.
Install R and RStudio before our first discussion. To become familiar with these tools, review “getting started” in Kieran Healy Data Visualization (Princeton University Press, 2018). online at https://socviz.co/gettingstarted.html#gettingstarted.
Scott Spencer, “Data in Wonderland” (https://ssp3nc3r.github.io/data_in_wonderland, 2021), secs. 1.1–1.2.
Scott Berinato, “Data Science & the Art of Persuasion,” Harvard Business Review, December 2018, 1–13.; Chris Brady, Mike Forde, and Simon Chadwick, “Why Your Company Needs Data Translators,” MIT Sloan Management Review, March 2017, 1–6.; Louise Maynard-Atem and Ben Ludford, “The Rise of the Data Translator,” Impact 2020, no. 1 (January 2020): 12–14, https://doi.org/10.1080/2058802X.2020.1735794.; Minda Zetlin, “What Is a Chief Analytics Officer? The Exec Who Turns Data into Decisions,” CIO, November 2017.
Andrew J. Friedland, Carol L. Folt, and Jennifer L. Mercer, Writing Successful Science Proposals, Third edition (New Haven: Yale University Press, 2018).; Matthew Friedman, “Citi Bike Racks Continue to Go Empty Just When Upper West Siders Need Them,” News, West Side Rag, August 2017.; National Science Foundation, A Guide for Proposal Writing / National Science Foundation, Directorate for Education and Human Resources, Division of Undergraduate Education (National Science Foundation, 1998).; A Yavuz Oruc, Handbook of Scientific Proposal Writing (CRC Press, 2011).; Sandra Oster and Paul Cordo, Successful Grant Proposals in Science, Technology and Medicine: A Guide to Writing the Narrative (Cambridge ; New York: Cambridge University Press, 2015).; Juan Francisco Saldarriaga, “CitiBike Rebalancing Study” (Spatial Information Design Lab, Columbia University, 2013).; Joshua Schimel, Writing Science: How to Write Papers That Get Cited and Proposals That Get Funded (Oxford ; New York: Oxford University Press, 2012).
Starting with the links to resources from slides this week, gather and explore at least 5 different data sets and identify the variables and number of observations in each.
This is your start to researching available data for a topic that interests you that you will be able to graphically and statistically analyze in a way that provides insight into a decision some organization (of your choice) would make.
During this lecture, we will, first, discuss data and its role in data analytics and communication. Secondly, we will explore elements of writing, work through example memos as a means of communicating an idea for a data analytics project to an analytics executive. As a group exercise, we will revise an analytics proposal writeup for a new audience.
Spencer, “Data in Wonderland,” secs. 1.1–1.3.
Jean-Luc Doumont, “Fundamentals,” in Trees, Maps, and Theorems, Effective Communication for Rational Minds (Principiæ, 2009).; John D Kelleher and Brendan Tierney, “What Are Data, and What Is a Data Set?” in Data Science (MIT Press, 2018).; Giorgia Lupi, “DATA HUMANISM: The Revolution Will Be Visualized,” Print 70, no. 3 (2016): 76–85.
Scott Spencer, “To Inform Rebalancing, Let’s Explore Bike and Docking Availability in the Context of Subway and Weather Information,” Memo, February 2019.; Scott Spencer, “Our Game Decisions Should Optimize Expectations; Let’s Test the Concept by Modeling Decisions to Steal,” Memo, February 2019.
Consider the 124-word blog post about the proposed traffic video analysis in Jakarta, its audience and purpose. Then research the head of data analytics for the City of Jakarta, and consider how you would re-write the blog post for the head of analytics for a new purpose — to gain approval to write a proposal, moving the project a step forward. You may use the post-project writeup to help you include additional detail within a 250-word constraint. During class next week, we will work in groups to re-write this together, and all groups will share their versions.
Identify minimal available data for a topic that interests you that you will be able to get started graphically and statistically analyzing in a way that provides insight into a decision some organization (of your choice) would make. You will be able to continue supplementing your data from other sources as the semester progresses.
We will discuss the elements of effective business writing for a particular audience and purpose, and learn about the role of revision and getting feedback on your writing. Continuing our group project started last week, we will apply new tools in our exercise.
Spencer, “Data in Wonderland,” sec. 1.3.
Wayne C Booth et al., “Revising Style: Telling Your Story Clearly,” in The Craft of Research, Fourth (University of Chicago Press, 2016).; Matthew Butterick, “Butterick’s Practical Typography” (https://practicaltypography.com/, 2018).; Tali Sharot, “(Priors) Does Evidence Change Beliefs?” in The Influential Mind, What the Brain Reveals about Our Power to Change Others (Henry Holt and Company, 2017).; William Zinsser, “The Lead and the Ending,” in On Writing Well, Sixth, The Classic Guide to Writing Nonfiction (Harper Resource, 2001).
Joao Caldeira et al., “Improving Traffic Safety Through Video Analysis in Jakarta, Indonesia,” in Nd Conference on Neural Information Processing Systems NeurIPS, 2018, 1–5.; Spencer, “To Inform Rebalancing, Let’s Explore Bike and Docking Availability in the Context of Subway and Weather Information”.; Spencer, “Our Game Decisions Should Optimize Expectations; Let’s Test the Concept by Modeling Decisions to Steal”.; R J Andrews, “Focus Attention,” in Info We Trust: How to Inspire the World with Data (Wiley, 2019).; R J Andrews, “Imagination to Image,” in Info We Trust: How to Inspire the World with Data (Wiley, 2019).; R. J. Andrews, “Stories of Space, Time, and Data,” {{YouTube}} (https://youtu.be/rjI6IfSQZyw, April 2020).
Draft and submit your memo, details in section 3.1.
Building on the principles of effective business writing covered in the previous session, we will learn how to apply visual design and graphics to a multi-component document containing text and data. Good design and compelling visuals can make a memo, report, or presentation more powerful and persuasive. Different layers of storytelling go into an effective visual narrative. In a multimodal document, tables and data-driven graphics can expand upon and illustrate the text, and vice versa. On another level, the graphics themselves involve a series of small visual decisions that enable them to express a point or draw attention to an insight in the data. This module will discuss how layout design and typography can work together to integrate visuals with narrative content, and will serve as a segue into a deeper exploration of data-driven visual design.
Spencer, “Data in Wonderland,” sec. 1.4.
Jean-Luc Doumont, “Effective Written Documents,” in Trees, Maps, and Theorems, Effective Communication for Rational Minds (Principiæ, 2009).; Jane E. Miller, The Chicago Guide to Writing about Multivariate Analysis, Second edition, Chicago Guides to Writing, Editing, and Publishing (Chicago: University of Chicago Press, 2013).; Jane E. Miller, “Organizing Data in Tables and Charts: Different Criteria for Different Tasks,” Teaching Statistics 29, no. 3 (August 2007): 98–101, https://doi.org/10.1111/j.1467-9639.2007.00275.x.; Jane E. Miller, “Seven Basic Principles,” in The Chicago Guide to Writing about Multivariate Analysis, Second edition, Chicago Guides to Writing, Editing, and Publishing (Chicago: University of Chicago Press, 2013), 13–33.; Edward R. Tufte, “Aesthetics and Technique in Data Graphical Design,” in The Visual Display of Quantitative Information (Graphics Press, 2001), 176–90.
Robert L. Harris, Information Graphics: A Comprehensive Illustrated Reference (New York: Oxford University Press, 1999).; George Lakoff and Mark Johnson, Metaphors We Live by (Chicago: University of Chicago Press, 1980).; Josef Müller-Brockmann, Grid Systems in Graphic Design, A Visual Communication Manual for Graphic Designers, Typographers, and Three Dimensional Designers (ARTHUR NIGGLI LTD., 1996).; Richard Rutter, Web Typography, A Handbook for Designing Beautiful and Effective Responsive Typography (Ampersand Type, 2017).; Colin Ware, Information Visualization: Perception for Design, Fourth (Philadelphia: Elsevier, Inc, 2020).
Become familiar with software that can adjust document layout (e.g., borders, font size, leading, paragraph spacing, etc.). Butterick, “Butterick’s Practical Typography”. provides instruction for many of these for Microsoft Word, Apple Pages, and basic CSS/html. Apply layout principles to your memo.
Start to become familiar with a couple of graphics tools. Here are two places to start:
Healy, Data Visualization.; Ryan Sleeper, Practical Tableau: 100 Tips, Tutorials, and Strategies from a Tableau Zen Master, 2018.
Pick a basic data set, load it into your graphics software (e.g., R or Tableau — I recommend trying both), create a table, and try modifying the table display attributes. Then, try to use color on one part of the data that would be used to link to narrative. For help, consult:
Hao Zhu, kableExtra: Construct Complex Table with ’Kable’ and Pipe Syntax, Manual, 2020.; Ryan Sleeper, Innovative Tableau: 100 More Tips, Tutorials, and Strategies, 2020.
Begin working on proposal, details in section 3.2.
To effectively communicate data-driven insights visually requires an understanding of basic design principles and the ways the human brain naturally perceives visual information. This discussion explores fundamentals of graphical perception and the conceptual foundations of design. Finally, we discuss the fundamentals of mapping data values to visual channels.
Spencer, “Data in Wonderland,” secs. 2–2.1.3.
Colin Ware, “Visual Salience: Finding and Reading Data Glyphs,” in Information Visualization: Perception for Design, Fourth (Philadelphia: Elsevier, Inc, 2020), 143–82.; Colin Ware, “Static and Moving Patterns,” in Information Visualization: Perception for Design, Fourth (Philadelphia: Elsevier, Inc, 2020), 183–244.; Lisa Charlotte Rost, “What I Learned Recreating One Chart Using 24 Tools,” Code in {{Journalism}}, Source, December 2016.; Lisa Charlotte Rost, “One Chart, Nine Tools Revisited,” Lisa Charlotte Rost, October 2018.
Continue working on proposal, details in section 3.2. Try to include one or two relevant small tables or graphics using the techniques we’ve discussed so far.
Just as written language has a grammar, so too do data graphics. We explore the grammar of graphics and its relation to Bertin’s visual channels and attributes while conducting exploratory data analysis on one of our example projects. We apply the fundamental principles of communication to data encodings, then consider color’s components, and wrap up considering the differences between thinking about data encodings as graphics versus charts.
Spencer, “Data in Wonderland,” secs. 2.1.3.1–2.2.3.
Edward R. Tufte, “Color and Information,” in Envisioning Information (Graphics Press, 1990).; Edward R. Tufte, “Layers and Separation,” in Envisioning Information (Graphics Press, 1990).; Edward R. Tufte, “Data-Ink and Graphical Redesign,” in The Visual Display of Quantitative Information, Second (Graphics Press, 2001).; Edward R. Tufte, “Data-Ink Maximization and Graphical Design,” in The Visual Display of Quantitative Information (Graphics Press, 2001), 1–15.; C. Wilke, Fundamentals of Data Visualization: A Primer on Making Informative and Compelling Figures, First edition (Sebastopol, CA: O’Reilly Media, 2019).
Scott Spencer, “Approximating the Components of Lupi’s Nobels, No Degrees,” P( Ssp3nc3r | Columbian ), March 2019.; Giorgia Lupi, “Visual Data - La Lettura,” 2016.
Josef Albers, Interaction of Color (Yale University Press, 2006).; Alexei Boronine, “Color Spaces for Human Beings,” HSLuv.org, March 2012.; Ware, Information Visualization.; Hadley Wickham, “A Layered Grammar of Graphics,” Journal of Computational and Graphical Statistics 19, no. 1 (January 2010): 3–28, https://doi.org/10.1198/jcgs.2009.07098.; Leland Wilkinson, The Grammar of Graphics, Second (Springer, 2005).
Pick several variables from your data project, and create several exploratory graphics, each time choosing a different combination of multiple attributes (position, size, luminance, hue, orientation, angle) Bertin described of points, lines, and areas. Try this with multiple graphics tools.
Finalize and submit your proposal, details in section 3.2.
We consider our audience in redesigning exploratory graphics for purposes of communication, discuss how to address complexity of graphics for the audience, and begin to more substantially consider graphics as driving a narrative.
Spencer, “Data in Wonderland,” secs. 2.2.1, 2.2.4.
Jean-Luc Doumont, Trees, Maps, and Theorems, Effective Communication for Rational Minds (Principiæ, 2009).; Martin Krzywinski and Alberto Cairo, “Storytelling,” Nature Publishing Group 10, no. 8 (August 2013): 687–87.; Yarden Katz, “Against Storytelling of Scientific Results,” Nature Publishing Group 10, no. 11 (November 2013): 1045–45.; Martin Krzywinski and Alberto Cairo, “Reply to: Against Storytelling of Scientific Results,” Nature Publishing Group 10, no. 11 (November 2013): 1046–46.; Steve Haroz, Robert Kosara, and Steven L. Franconeri, “The Connected Scatterplot for Presenting Paired Time Series,” IEEE Transactions on Visualization and Computer Graphics 22, no. 9 (September 2016): 2174–86, https://doi.org/10.1109/TVCG.2015.2502587.; Elijah Meeks, “If Data Visualization Is So Hot, Why Are People Leaving?” Blog, Medium (https://medium.com/@Elijah_Meeks/why-people-leave-their-data-viz-jobs-be1a7ab5dddc, March 2017).
Andrew Gelman, “Ethics and Statistics: Honesty and Transparency Are Not Enough,” CHANCE 30, no. 1 (April 2017): 1–3.; Nadieh Bremer, Data Sketches A Journey of Imagination,Exploration, and Beautiful Data Visualizations. (Milton, UNITED KINGDOM: A K Peters/CRC Press, 2021).; Jonathan Corum, “Design for an Audience,” 13pt Information Design (http://style.org/ku/, April 2018).; Jonathan Corum, “See, Think, Design, Produce 3,” 13pt Information Design (http://style.org/stdp3/, March 2016).; Juuso Koponen and Jonatan Hildén, Data Visualization Handbook, First (Finland: Aalto Art Books, 2019).; David McCandless, “What Makes a Good Visualization?” Information Is Beautiful (https://informationisbeautiful.net/visualizations/what-makes-a-good-data-visualization/, n.d.).; Jonathan A. Schwabish, Better Data Visualizations: A Guide for Scholars, Researchers, and Wonks (New York: Columbia University Press, 2021).; Wilke, Fundamentals of Data Visualization.; Elijah Meeks, “You Can Design a Good Chart with R: But Do R Users Invest in Design?” July 2018.
We will gather into groups and redesign a published data visual using principles we’ve been discussing. Each group will contribute at least one redesign of the graphic to a shared document. Then, as a class, we will consider the collection of redesigns.
Begin work on data-driven, visual narrative (information graphic), details in section 3.4.
We summarily review design principles covered so far during the course. Then, we more formally build a framework for critique and criticism of data-driven, visual narratives. From there, we discuss the importance of, and ways for, distinguishing data from estimates in visuals. Finally, we discuss ideas in sequencing and pacing for an audience.
Spencer, “Data in Wonderland,” secs. 2.3–2.4.
Doumont, Trees, Maps, and Theorems, Revising the document.; Tony Chu, “Animation, Pacing, and Exposition” (OpenVis Conf 2016, May 2016).; Schwabish, Better Data Visualizations, sec. 6.; Michael Correll, Dominik Moritz, and Jeffrey Heer, “Value-Suppressing Uncertainty Palettes,” in Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems - CHI ’18 (Montreal QC, Canada: ACM Press, 2018), 1–11.
Thomas Lin Pedersen and David Robinson, Gganimate: A Grammar of Animated Graphics, Manual, 2021.; Matthew Kay, Ggdist: Visualizations of Distributions and Uncertainty, Manual, 2021, https://doi.org/10.5281/zenodo.3879620.; Wilke, Fundamentals of Data Visualization.
Together, we critique several information graphics and consider sequence and pacing.
Continue working on data-driven, visual narrative, details in section 3.4 . Prepare and submit critique of exemplary information graphic, details in section 3.3.
In our discussion, we consider the foundations of modern user interaction in the context of data-driven, visuals and narratives. Such interactions may include scrolling, overview, zoom, filter, details-on-demand, relation, history, extraction; brushing and linking; hovering; clicking; selecting; and gestures. Part of allowing interaction results in authors having less control over the intended narrative and, thus, may think about interaction as giving the audience some level of co-creation in the narrative.
Spencer, “Data in Wonderland,” secs. 3–3.1.
Christian Tominski and Heidrun Schumann, Interactive Visual Data Analysis, First (Boca Raton: CRC Press, 2020), secs. 4–4.1.; Fred Hohman et al., “Communicating with Interactive Articles,” Distill 5, no. 9 (September 2020): 10.23915/distill.00028, https://doi.org/10.23915/distill.00028.
B. Shneiderman, “The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations,” in Proceedings 1996 IEEE Symposium on Visual Languages (Boulder, CO, USA: IEEE Comput. Soc. Press, 1996), 336–43, https://doi.org/10.1109/VL.1996.545307.; Jeffrey Heer and Ben Shneiderman, “Interactive Dynamics for Visual Analysis: A Taxonomy of Tools That Support the Fluent and Flexible Use of Visualizations,” Queue 10, no. 2 (February 2012): 30–55, https://doi.org/10.1145/2133416.2146416.; Ji Soo Yi et al., “Toward a Deeper Understanding of the Role of Interaction in Information Visualization,” IEEE 13, no. 6 (November 2007): 1224–31.
Review examples of various interactive documents cited in Hohman et al., “Communicating with Interactive Articles”. and think critically about what audience interactions are enabled, and whether you feel the interaction works intuitively.
Continue polishing your data-driven, visual narrative, details in section 3.4.
Interactivity is inherently technology dependent. We discuss modern approaches to interactivity as related to data-driven, visual graphics and narratives. As such, we will introduce a modern technology stack that includes html, css, svg, and javascript, within which technologies like d3.js (a javascript library) and processing operate. Most award-winning interactive graphics, especially from leading news organizations, use this stack.
But we can enable interactivity through interfaces, too, from R and Python interactive and markdown notebooks and code, to htmlwidgets, Shiny, and plotly. Then, we have drag-and-drop alternatives like Lyra2 or Tableau. While Tableau is functionally much more limited, a use-case for Tableau may be, for example, when a client has a license for it, wants basic interactivity for someone else to setup the visual with interactive options. The range of tools is becoming ever larger. More generally, knowing a range of tools will help us not only build our own interactive graphics, but will also help us work with specialists of these technologies.
Spencer, “Data in Wonderland,” sec. 3.2.
Tominski and Schumann, Interactive Visual Data Analysis, sec. 4.
Spencer, “Data in Wonderland,” sec. 1.1.1.2.; Interactive version of Ride against the flow: https://ssp3nc3r.github.io/publications/citibike-interactive-graphic.html.
Engin Arslan, Learn Javascript with P5.js: Coding for Visual Learners (New York, NY: Springer Science+Business Media, 2018).; Joe Attardi, Modern CSS: Master the Key Concepts of CSS for Modern Web Development, 2020.; Amelia Bellamy-Royds, Kurt Cagle, and Dudley Storey, Using SVG with Css3 and Html5 : Vector Graphics for Web Design (O’Reilly, 2018).; Jon Duckett, HTML & CSS, Design and Build Websites (Wiley, 2011).; Jon Duckett, Gilles Ruppert, and Jack Moore, JavaScript & jQuery: Interactive Front-End Web Development (Indianapolis, IN: Wiley, 2014).; Colin Fay et al., Engineering Production-Grade Shiny Apps, First edition, R Series (Boca Raton: CRC Press, 2021).; David Gohel and Panagiotis Skintzos, Ggiraph: Make ’Ggplot2’ Graphics Interactive, Manual, 2021.; Philipp K. Janert, D3 for the Impatient: Interactive Graphics for Programmers and Scientists, First edition (Sebastopol, CA: O’Reilly Media, Inc, 2019).; Elijah Meeks, D3.js in Action, Second (Manning, 2018).; Scott Murray, Interactive Data Visualization for the Web, Second, An Introduction to Designing with D3 (O’Reilly, 2017).; Casey Reas and Ben Fry, Processing A Programming Handbook for Visual Designers and Artists, Second (The MIT Press, 2014).; Carson Sievert, Interactive Web-Based Data Visualization with R, Plotly, and Shiny (Boca Raton, FL: CRC Press, Taylor and Francis Group, 2020).; Ramnath Vaidyanathan et al., Htmlwidgets: HTML Widgets for r, Manual, 2020.; Hadley Wickham, “Create Elegant Data Visualisations Using the Grammar of Graphics Ggplot2” (https://ggplot2.tidyverse.org/, n.d.).
Finalize and submit your data-driven, visual narrative (information graphic), details in section 3.4.
From our discussions of the principles and tools of interactivity with data graphics, and ways we can give them organization, we begin considering how to include these tools and concepts to guide our audience in our communications. How should be think about organizing interactive graphics? Would a “dashboard” or “scrollytelling” be useful? Does it depend on our audience and their goals? How should we guide our readers both through the graphics and in their use of interactivity? Consider the ideas and examples from our readings.
Spencer, “Data in Wonderland,” sec. 3.3, writing in progress.
S. McKenna et al., “Visual Narrative Flow: Exploring Factors Shaping Data Visualization Story Reading Experiences,” Computer Graphics Forum 36, no. 3 (June 2017): 377–87, https://doi.org/10.1111/cgf.13195.; Pascal Schneiders, “What Remains in Mind? Effectiveness and Efficiency of Explainers at Conveying Information,” Media and Communication 8, no. 1 (March 2020): 218–31, https://doi.org/10.17645/mac.v8i1.2507.; Hohman et al., “Communicating with Interactive Articles”.
Spencer, “Data in Wonderland,” sec. 1.2.1.2.;David J Carr, “Data Is the New Oil: Dirty, Misunderstood, Polluting the World & Pulled from All the Wrong Places,” Medium | Redwhale, January 2018.; David J Carr, “What Value Do You Create? Marketings 3 Types of Value,” Medium | Marketing, January 2019.
Begin working on an interactive communication of data graphics, details in section 3.6.
Spencer, “Data in Wonderland,” sec. 4, writing in progress.
Doumont, Trees, Maps, and Theorems, Effective Oral Presentations.; Jonathan Schwabish, Better Presentations: A Guide for Scholars, Researchers, and Wonks (Columbia University Press, 2016).; Edward R. Tufte, “The Cognitive Style of PowerPoint: Pitching Out Corrupts Within,” in Beautiful Evidence (Graphics Press, 2006).; Edward R Tufte, “Smarter Presentations and Shorter Meetings,” in Seeing with Fresh Eyes: Meaning, Space, Data, Truth (Cheshire, Conn.: Graphics Press, 2020), 151–61.
Corum, “See, Think, Design, Produce 3”.; Lisa Charlotte Rost …;
Record presentation, details in section 3.5.
Bremer, Data Sketches A Journey of Imagination,Exploration, and Beautiful Data Visualizations.; Amy J. Ko, “Design Methods: What Design Is and How to Do It,” book (https://faculty.washington.edu/ajko/books/design-methods/, September 2020).; H. Rex Hartson and Pardha S. Pyla, The UX Book: Agile UX Design for a Quality User Experience, Second edition (Cambridge, MA: Morgan Kaufmann, 2019).; Sarah Richards, Content Design (Content Design London., 2017).
Polish and submit your interactive communication of data graphics, details in section 3.6.
Students will complete six main assignments while taking an analytics project of the students’ choosing from beginning to end, first writing a memo to the analytics executive at the organization selected by the student, asking the executive to approve work on a detailed proposal for the project. Then the student drafts the proposal. As the student begins working on the project, they will develop information graphics for an external audience, an interactive communication with data graphics for a marketing executive, and finally present the value of further analytics work to the chief executive. These assignments flow with our weekly discussions, which include:
Similar to your group work, individually draft a 250-word (maximum) memo to your chosen organization’s head of analytics with the purpose of gaining approval to write a proposal and conduct an analysis with your data. Apply the ideas from the first two discussions to your memo.
Of note, your memo should cite your selected data source(s) — available to you — and its variables, which, again, you may supplement with additional sources as we move forward.
Draft a 750-word (maximum) proposal detailing the project described in your memo. Your audience, again, should be the analytics executive at the organization for which your project is meant.
Create one or two relevant small tables or graphics. When thinking of relevance at this stage, consider using them to show your important variables, perhaps with distributions, or from preliminary exploration describing your data and variables. Integrate them into the text of your 750-word proposal in a way that supports the proposal gaining approval from the analytics executive. Get feedback on the proposal, and revise for final submission.
Of note, as with your memo, you should cite your selected data source(s) — available to you — and its variables, which, again, you may supplement with additional sources as we move forward.
Select an information graphic you feel exemplifies what we’re looking for in an information graphic — i.e., a data-driven, visual narrative — and write a short critique of the information graphic using the language and ideas we’ve developed. Submit your critique. The critique should a) be a thoughtful, non-obvious example of a data-driven visual narrative for you and your peers in this course to learn from; b) effectively address narrative, data encodings / decodings, layout, and uses of color; c) offer thoughtful suggestions to that can be tested for improvement; and d) be written in objective, neutral language, and backed up by theoretical reasoning or empirical evidence we have discussed in this course.
Begin creating an information graphic designed to communicate your project and preliminary results to an external, general audience. In your narrative, consider Doumont’s discussion of mixed audiences. Arrange multiple graphics to help explain your story, considering the aesthetics and content from discussions.
Use feedback from your draft information graphic and the principles we’ve discussed for critique to refine your own data-driven, visual narrative and prepare for submission.
Present a short request (4-5 minutes) to the Chief Executive Officer of your organization to continue investing in data analytics and to approve any next-steps for your project. Use your data-project and insights this semester to demonstrate the general value for that investment. Use best practices in creating your presentation slides or visuals, given the ideas from our class discussions.
Create a visual communication with interactive, data graphics that enable the chief marketing executive at your selected organization to explore data for insights into at least two nontrivial questions concerning your project in the context of his or her CMO responsibilities at the organization.
For guidance on the responsibilities of a marketing executive — and their general knowledge of data and data graphics — review Spencer “Data in Wonderland”. Sec. 1.2.1.2 and the cited references Carr “What Value Do You Create? Marketings 3 Types of Value”. and Carr “Data Is the New Oil”. And as with previous audiences, conduct online research (google searches, linkedin, …) to understand your new audience.
For interactivity, employ three or more techniques discussed in class: e.g., linking multiple graphics to interactions like hovering, clicking, filtering, selecting, dragging and scrolling. These interactions should be relevant to — and directly support — the CMO exploring data for insights, their limits, and context to your research questions.
Your communication can be organized either as a guided “dashboard” or as a scrollytelling-style document.
Recall the three laws of communincation from Doumont “Fundamentals”. Your narrative should 1) direct your audience to your exploratory purpose (using the best communication and visualization practices we’ve been developing in the course), 2) guide them in how they should explore using your interactive data graphics (using messages in titles, (sub)headers, labels, annotations, and other concise text), and why they should explore.
For this assignment, it will generally be best to create your interactive graphics in Tableau or R markdown and R plus available packages like we’ve discussed in class. If you choose to use other interactive languages like Vega, Shiny, or D3.js directly, first consult with your professor to assess viability, given your time constraints.
This is an opportunity for you to gain experience combining all the techniques we’ve discussed this semester, and use interactive graphics to communicate with a third audience.