Beautiful Data

Beautiful Data (O’Reilly Media, 2009) is a follow-up to Beautiful Code, and, like the earlier book, it was envisioned to be a work of sharing and exploration rather than an argument or treatise.

We like the wide-ranging scope of this work and the inherent curiosity it both curates and fosters. The book includes 20 chapters from 39 contributors, and is loosely organized in an arc covering data collection through storage, organization, retrieval, visualization and analysis. Beautiful Data also includes examples of computer codes that readers are welcome to use for their own analysis (and encouraged to do so).

Data touches every business and an every increasing proportion of our daily, personal lives—this is not a recent development, but one that was building slowly and steadily for decades. Still, a number of relatively recent advances in collection, storage, and processing technologies have broken down barriers and torn open new areas for data collection and exploitation by businesses, governments and private individuals and groups.

The authors and editors showcase real-world examples, from the Mars Lander, a Radiohead video, Oakland crime mapping, to communities of individuals who obsessively track and analyze their own actions and behaviors.

More and more powerful tools enable us to visualize and gather crucial insights from this data—or, in some cases, capture incorrect insights and reach faulty conclusions. The authors also explore the human drive to build conclusions from data and the many ways that data analysis can lead us astray if we aren’t careful.

Another undercurrent of particular interest to us is the tension between commercial investment and exploitation of data versus initiatives to use the vast sea of information and powerful analytics tools for the common good. Marketing and business development can and do benefit tremendously from a rigorous approach to data and analytics, but there are strong arguments that open source data and analytics tools should be also be leveraged to improve science, medicine, education and other areas that will generate substantial benefits to society. Co-editor Jeff Hammerbacher delves into this issue, and his views on this in this discussion with Charlie Rose:

The best minds of my generation are thinking about how to make people click ads. That sucks.

It strikes us that there is an opportunity to apply the principles of Mutuality to this question in particular, and this is an area we will explore further in future posts.

In the six years since the book was published the technologies have advanced and the knowledge (and popularity) of big data and data analysis have increased as well. Yet this work is worth reading for the approach it takes and its explorations of “data philosophy.”

Image source: O’Reilly Media

-- Yassine El Ouarzazi

Maua & Bloom -- putting mutuality theories to the test

Andala Learning model