My name is Kurt Cagle. I am the new Community Editor for Data Science Central, or DSC as it is known by its fans.
I’m one of those fans. Twelve years ago, Vincent Granville and Tim Matteson created a new site devoted to a passion they had: Data Science. In 2012, the term data science, and the practitioners, data scientists, were just beginning to come into vogue, specifically referring to the growing importance of a role that had been around for some time, the erstwhile data analyst, with the idea being that this particular role was different from a traditional programmer’s role, though it borrowed many of the same tools.
Traditionally, an analyst, any analyst, has been someone who looks at information within a specific subject domain and, from their analysis, can both identify why things are the way they are and to a certain extent predict where those same things will be in the future.
Analysts have been around for a long time, and have always had something of a mystical air to them. As an example, in early Imperial Rome, there were a number of celebrated priests called Augurs who were said to be able to predict the future from the flight of birds. They had a surprisingly high success rate, and were usually in great demand by both military leaders needing strategic advice and merchants looking to better deploy their fleets and land agents.
At first, the correlation between bird flight patterns and sound trade policy advice would seem low at best, but as with any good magical trick, it was worth understanding what was going on in the background. Why does one watch the sky for bird flight? Easy. Certain types of birds, such as homing pigeons, can carry messages from ships or caravans to various outposts, and from there such information can be relayed via both birds and other humans to central gathering points. In other words, the Augurs had managed to build a very sophisticated, reasonably fast intelligence network tracking ships, troop movements, plague spots, and so forth, all under the cover of watching the skies for birds. Even today, the verb to augur means to predict, as a consequence.
In the eight years since Data Science Central published its first post, the field has grown up. Statistical and stochastic functions have become considerably more sophisticated. The battle royale between R and Python has largely been resolved as “it doesn’t matter”, as statistical toolsets make their way to environments as diverse as Scala, Javascript ,and C#. The lone data scientist has become a team, with fields as diverse as data visualization to neural network training to data storytellers staking their claims to the verdant soil of data analysis.
What is even more exciting is that this reinvention is moving beyond the “quants” into all realms of business, research, and manufacturing organizations. Marketing, long considered to be the least “mathematical” of disciplines within business, now requires at least a good grounding in statistics and probability, and increasingly consumes the lion’s share of a company’s analytics budget. Neural nets and reinforcement learning are now topics of discussion in the board room, representing a situation where heuristic or algorithmic tools are being supplemented or even replaced with models with millions or even billions of dimensions. The data scientist is at the heart of organizational digital transformations.
Let me bring this back to DSC, and give to you, gentle reader, a brief bio of me, and what I hope to be able to bring to Data Science Central. I have been a consulting programmer, information architect, and technological evangelist for more than thirty years. In that time I have written twenty-some-odd books, mainly those big technical door stoppers that look really good on bookshelves. I’ve also been blogging since 2003 in one forum or another, including O’Reilly Media, Jupiter Publishing, and Forbes. I spent a considerable amount of time trying to push a number of information standards working with the W3C, and have, since the mid-2000s, focused a lot of time and energy on data representation, metadata, semantics, data modeling, and graph technology.
I’m not a data scientist. I do have a bachelor’s degree in astrophysics, and much of a master’s degree in systems theory. What that means is that I was playing with almost all of the foundational blocks of modern data science back about the time when the cutting edge processors were the Zylog-80 (known as the Z80) and 6502 chips within Apple II+ systems. I am, to put it bluntly, an old fart.
Yet when the opportunity to take over DSC came up, I jumped at it, for a very simple reason: context. You see, it’s been my contention for a while that we are entering the era of Contextual Computing, eventually to be followed by Metaphorical Computing (in about twelve years, give or take a few). Chances are, you haven’t heard the term Contextual Computing bandied about very much. It’s not on Gartner’s hype cycle, because it’s really not a “technology” per se. Instead, you can think of contextual computing as the processing of, and acting on, information that takes place when systems have a contextual understanding of the world around them.
There are several pieces to contextual computing. Data Science is a big one. So is Graph Computing. Machine Learning, AI, the Internet of Things, the Digital Workplace, Data Fabric, Autonomous Drones, the list is long and getting longer all the time. These are all contextual – who are you, where are you, why are you here, what are you doing, why does it matter?
Data Science Central has become an authority in the world. My hope, my plan at this point, is to expand its focus moving into the third decade of this century. I’m asking you as readers, as writers, as community members, to join me on this journey, to help shape the nature of contextual computing. DSC is a forum to share technology but also to share asking deep questions about ethics and purpose, the greater good and with an eye towards opportunities. I hope to take Vincent and Tim’s great community and build it out, with your help, observations, and occasional challenges.
