by Jock Mackinlay
Visual analysis is an iterative process for working with data that exploits the power of the human visual system. The formal core of visual analysis is the mapping of data to appropriate visual representations.
In this talk, you’ll learn: •What years of research by psychologists, statisticians and others have taught us about designing great visualizations •Fundamental principles for designing effective data views for yourself and others •How to systematically analyze data using your visual system
by Bitsy Hansen
I am frequently asked for advice about using data visualization to solve communication problems that are better served through improved information architecture. A nicely formatted bar chart won’t rescue you from a poorly planned user interface. When designing meaningful data experiences it’s essential to understand the problems your users are trying to solve.
In this case, I was asked to take a look at a global data-delivery platform with a number of issues. How do we appeal to a broad cross-section of business users? How do we surface information to our clients in a useful way? How do we facilitate action, beyond information sharing? How do we measure success?
A user-centered approach allowed us to weave together a more meaningful experience for our business users and usability testing revealed helpful insights about how information sharing and data analysis flows within large organizations.
Data visualization is a powerful tool for revealing simple answers to complex questions, but context is key. User-centered design methods ensure that your audience receives the information they need in a usable and actionable way. Data visualization and user experience practices are not mutually exclusive. They work best when they work together.
Since the early days of the data deluge, Lift Lab has been helping many actors of the ‘smart city’ in transforming the accumulation of network data (e.g. cellular network activity, aggregated credit card transactions, real-time traffic information, user-generated content) into products or services. Due to their innovative and transversal incline, our projects generally involve a wide variety of professionals from physicist and engineers to lawyers, decision makers and strategists.
Our innovation methods embark these different stakeholders with fast prototyped tools that promote the processing, recompilation, interpretation, and reinterpretation of insights. For instance, our experience shows that the multiple perspectives extracted from the use of exploratory data visualizations is crucial to quickly answer some basic questions and provoke many better ones. Moreover, the ability to quickly sketch an interactive system or dashboard is a way to develop a common language amongst varied and different stakeholders. It allows them to focus on tangible opportunities of product or service that are hidden within their data. In this form of rapid visual business intelligence, an analysis and its visualization are not the results, but rather the supporting elements of a co-creation process to extract value from data.
We will exemplify our methods with tools that help engage a wide spectrum of professionals to the innovation path in data science. These tools are based on a flexible data platform and visual programming environment that permit to go beyond the limited design possibilities industry standards. Additionally they reduce the prototyping time necessary to sketch interactive visualizations that allow the different stakeholder of an organization to take an active part in the design of services or products.
28th February to 1st March 2012