"Global Economic Dynamics project exists to help make complex economic dynamics transparent and understandable. The project examines the causes and effects of economic trends, as well as the connections linking one trend to another."

"Signal detection is actually getting harder with the advent of so-called Big Data. By its very nature, most Big Data will never be anything but noise. Collecting everything possible, based on the Big Data argument that the costs of doing so are negligible and that even data that you can’t imagine as useful today could become useful tomorrow, is a dangerous premise. The costs of collecting and storing everything extend far beyond the hardware that’s used to store it. People already struggle to use data effectively. This will become dramatically harder as the volume of data grows. Finding a needle in a haystack doesn’t get easier as you’re tossing more and more hay on the pile. (…)

When we rely on data for decision making, what qualifies as a signal and what is merely noise? In and of themselves, data are neither. Data are merely facts. When facts are useful, they serve as signals. When they aren’t useful, data clutter the environment with distracting noise.

For data to be useful, they must:

Address something that matters

Promote understanding

Provide an opportunity for action to achieve or maintain a desired state

When any of these qualities are missing, data remain noise."

—

Stephen Few

"In the 1970s, it was the protest songs. In the 1980s, it was the anti-war movies. Today, the protest is no longer happening in songs or movies. Today, it’s online, based on data, and using visualization.

How do you make people notice an issue? How do you get them to care? What if we’re no longer moved by songs (and the artists too comfy and reluctant to take sides) and no longer want to see movies about real issues (and Hollywood won’t take the risk of offending anybody)?

What if the new way to get us to care is with a visceral, raw display of data?"

"The following are three minimal criteria that any visualization has to fulfill to be considered a pragmatic visualization. A good visualization certainly has to do more, but these criteria are useful to draw the line between a lot of things that are often called visualization and what we consider visualization in this field.

Based on (non-visual) data. A visualization’s purpose is the communication of data. That means that the data must come from something that is abstract or at least not immediately visible (like the inside of the human body). This rules out photography and image processing. Visualization transforms from the invisible to the visible.

Produce an image. It may seem obvious that a visualization has to produce an image, but that is not always so clear. Also, the visual must be the primary means of communication, other modalities can only provide additional information. If the image is only a small part of the process, it is not visualization.

The result must be readable and recognizable. The most important criteria is that the visualization must provide a way to learn something about the data. Any transformation of non-trivial data into an image will leave out information, but there must be at least some relevant aspects of the data that can be read. The visualization must also be recognizable as one and not pretend to be something else."

"The visual representation of data has gone through a number of phases, with its goals switching back and forth between analysis and presentation over time. Many introductions to visualization tend to portray historical examples as all being done for the same purpose. That, I argue in this short, incomplete, and cherry-picked history, is not true."

"Ultimately, I believe the solution is a two-way street. First, anyone building a data visualisation must go to great lengths not only to link to sources and to fully explain any caveats relating to the data or graphic itself, but to state the degree to which their work should - or more importantly shouldn't - be taken as scientific fact. And second, we must better inform the wider public of the danger signs to watch out for before spreading what may turn out to be misinformation."

"We live in the Age of the Algorithm, where computer models save time, money and lives. Gone are the days when labyrinthine formulae were the exclusive domain of finance and the sciences - nonprofit organisations, sports teams and the emergency services are now among their beneficiaries. Even romance is no longer a statistics-free zone.

But the very feature that makes algorithms so valuable - their ability to replicate human decision-making in a fraction of the time - can be a double-edged sword. If the observed human behaviours that dictate how an algorithm transforms input into output are flawed, we risk setting in motion a vicious circle when we hand over responsibility to The Machine."

Unlike any time before in our lives, we have access to vast amounts of free information. With the right tools, we can start to make sense of all this data to see patterns and trends that would otherwise be invisible to us. By transforming numbers into graphical shapes, we allow readers to understand the stories those numbers hide. In this practical introduction to understanding and using information graphics, you’ll learn how to use data visualizations as tools to see beyond lists of numbers and variables and achieve new insights into the complex world around us. Regardless of the kind of data you’re working with–business, science, politics, sports, or even your own personal finances–this book will show you how to use statistical charts, maps, and explanation diagrams to spot the stories in the data and learn new things from it. 

Este mundo de esquemas, diagramas, redes, organigramas, sociogramas, cronogramas, graficos de flujos, etcetera, no disminuira en absoluto sino que, al contrario, estos son el signo de su crecimiento, con el progreso tecnologico y la creatividad grafica como lenguaje universal. Este libro nos descubre una perspectiva inedita: la visualizacion de los "fenomenos invisibles." Es el dominio de los lenguajes graficos hoy reunidos y estudiados sistematicamente, por primera vez, por Joan Costa, a la luz de la nueva ciencia de la comunicacion visual, la esquematica, a la que el autor ha definido como el "tercer lenguaje," despues de la imagen y el signo.

A comprehensive yet quick guide to the best approaches to designing data visualizations, with real examples and illustrative diagrams. Whatever the desired outcome ensure success by following this expert design process. This book is for anyone who has responsibility for, or is interested in trying to find innovative and effective ways to visually analyze and communicate data.

“I don’t think the goal of vizualization should be that precog understanding, giving you something so quickly that you don’t have to think about it; I think the goal should be that you do have to think about it,”

“I want people to have a brain, to have a say. I think it’s becoming only more important as we tackle more nuanced information. I don’t want the computer or the creator to tell me what to believe. I want an opening to say, ‘let me look into that machine.’”

Kim Rees, Periscopic co-founder

"We live in exciting and promising times. The flood of data we are collecting will yield new and earth-changing insights, some of which will be made by enthusiastic volunteers at hackathons. Let's lay the foundation for their success by bringing together world-class teams to ask the right questions, collaborating on the best interpretations of the data, and striving, always, to be sensitive. Data isn't just a spreadsheet or a database: It's us. It's the people we care about. It's our world. Let's not just hack it."

Jack Porway

Friday’s jobs report is the second-to-last of the presidential campaign.

Same data, democrat versus republican views…

We live in the era of Big Data, with storage and transmission capacity measured not just in terabytes but in petabytes (where peta- denotes a quadrillion, or a thousand trillion). Data collection is constant and even insidious, with every click and every "like" stored somewhere for something. This book reminds us that data is anything but "raw," that we shouldn't think of data as a natural resource but as a cultural one that needs to be generated, protected, and interpreted. The book's essays describe eight episodes in the history of data from the predigital to the digital. Together they address such issues as the ways that different kinds of data and different domains of inquiry are mutually defining; how data are variously "cooked" in the processes of their collection and use; and conflicts over what can -- or can't -- be "reduced" to data. Contributors discuss the intellectual history of data as a concept; describe early financial modeling and some unusual sources for astronomical data; discover the prehistory of the database in newspaper clippings and index cards; and consider contemporary "dataveillance" of our online habits as well as the complexity of scientific data curation.

We have on stage: Kim Rees co-founder of Periscopic, a data visualization company guided by the motto: “do good with data” and Jake Porway, founder of Data Kind, an organization that brings together data scientists and social organizations. We discuss about the challenges of working in this crazy world of big data opportunities and counterbalance this with risks and subtle potentially negative implications.

The remarkable thing about the data revolution is not that it has changed our lives so much, but that it hasn’t changed it enough.