Schedule: Interface sessions

A lesson we have learned from our work on the data portal DataMarket.com and custom projects we’ve done for a wide variety of customers is: Regardless of how interesting the underlying data or how ground-breaking the analysis is, most people only realize the value and see the potential once the data has been properly visualized.

Put another way: Visualization is where normal people fall in love with data, and – when done right – where they can understand the data at a glance.

We are by no means alone in realizing this. Data visualization has become a hot field, and a lot of statisticians, designers and computer professionals are taking their first steps, learning by example from things they’ve seen elsewhere. Some of these examples are colorful, pretty and praised but still don’t communicate the data properly – the real stories may even be obscured or distorted with badly parsed data or gratuitous visual fluff. Other examples are breaking new ground and advancing the field. But which is which?

Visually communicating data is not a new field. People have been honing data visualization skills since the 19th century, learning a lot about what works – and what doesn’t. It is possible to do things both “right” and beautiful at the same time. In this presentation we hope to explain how by showing the audience some of the very best examples of such work from the leaders in this field – and others that have not done as well.

After providing this background we will walk the audience step-by-step through one particular data visualization project we have worked on (possibly our Earthquake and Eruptions video), explaining the methods, tools and process involved in putting that together and the decisions that led to those particular choices.

Nowadays, major news events prompt millions of responses online. Every message passing through the internet has a voice. Aggregate analysis and visualization helps us see the roar of the crowd.

The Guardian first explored this last year with an award-winning graphic that replays World Cup games , condensing 90 minutes of tweets into 90 seconds of interactive animation. By juxtaposing match events with surges in word popularity, viewers can relive the ripples of human reaction passing through Twitter.

Asked to apply similar techniques to the News International saga, we partnered with Datasift to capture and display public responses during key events in the story. This talk steps through the process of recording, processing and displaying a large volume of tweets which enabled a small team to build complex pieces of interactive content at newsroom speeds.

Above all, the presentation will aim to portray the delicate balance of design, data and storytelling at the heart of interactive news content.

Good graphs are extremely powerful tools for communicating quantitative information clearly and accurately. Unfortunately, many of the graphs we see today are poor graphs that confuse, mislead or deceive the reader. These poor graphs often occur because the graph designer is not familiar with principles of effective graphs or because the software used has a poor choice of default settings. We point out some of these graphical mistakes including using unnecessary dimensions, not making the data stand out, making mistakes with scales, showing changes in one dimension by area or volume, and not making your message clear. In most cases very simple changes make the resulting graphs easier for the reader to understand. In addition, we show some common mistakes with tables. We end with some useful little-known graph forms that communicate the data more clearly than the everyday graphs that are more commonly used.

Data visualization is an important communication medium in personal and public conversation spheres. Its wide use in entertainment and business settings alike has encouraged the creation of tools and frameworks that allow anyone to create visualizations and share them with their audience. While these tools offer tried and true visualization metaphors they also pose risks such as missing important data points or creating meaningless visuals.

This talk will introduce the concept of “responsible data visualization” in the context of two distinct uses: exploration and narrative. Using personal and industry examples to show best and worst practices in each approach, this talk will offer practical suggestions to bringing data visualization into one’s data workflow.

Political campaigns and causes have added another powerful weapon to their messaging arsenal: graphs, charts, infographics and other forms of data visualization. Over just the last year, Barack Obama urged voters to distribute and share a bar graph of job losses, a line graph of labor costs by a New York Times columnist prompted an official graphical response from the government of Spain, and an organizational chart of a health care reform bill became the subject of a Congressional investigation in the United States. To be sure, a good graph has been used as an advocacy tool for years, but only recently, with the rise of the Internet, blogs, hardware and software advances, and freely available machine readable data, political data visualizations have exploded into political discourse. Conveying objective authority, yet the product of dozens of subjective design decisions, political infographics imply hard truths despite their inherently editorial nature. This talk, given by a political data scientist who has built persuasive data visualizations for political organizations, will dissect some of the most extraordinary and powerful examples of political data visualization used over the last election cycle, focusing upon the methods that make them work so well.

This is a talk aimed at people who know their data, and want to learn how to visualize it most effectively. If you have data, a need for answers, and a blank page, this is a great place to start.

We’ll start briefly addressing the value of visualization, and discuss the differences between visualization for analysis and presentation.

From there we’ll figure out what story to tell with your visualization by examining the holy visualization trinity: - your goals - your customer’s needs - the shape of your data

Once the story has been selected, we need to construct it. We’ll discuss key considerations to make good choices about: - selecting appropriate data - selecting appropriate axes - visually encoding the data

We’ll end with a brief discussion of some current tools, and look at some classic and innovative visualization examples.