Data has always been an integral part of computing, but it has only been in the last decade or so that we have reached a point of data ubiquity. What that means in practice comes down to a fundamental notion about what exactly data is.

Data isn’t really a “thing” per se. Rather, you can think of data as being the digitized artifact that a process produces as it changes state. It is not just a signal, but a signal with some attached semantics that describes what a thing has become. Human civilization has produced such signals for a long, long time, but in most cases, only the crudest of these signals were interpretable, usually at a significant cost.

Increasingly, however, everything around us has begun emitting state signals that can be encoded and transmitted clear across the planet. If the temperature in my house falls below 65 degrees, the thermostat not only acts by turning up the heat, but it also informs me that the house is too cold. Amazon informs me when a particular book I’ve been wanting to read comes out, but it also now tells me when the cost of that book falls below a certain threshold … and can even purchase the book on my behalf without me even being in the loop.

This is having a profound impact upon our society in ways we’re only just beginning to realize. One effect is that it provides a natural deflationary pressure on financial transactions even in the midst of the supply chain disruptions from the pandemic that would ordinarily be strongly inflationary. It is even mitigating those disruptions by providing a better idea about where alternatives can be found in real-time, a process that normally would be prohibitively complex.

We’re beginning to discover that data societies are mediated societies, where the mediation is less through human interaction and more through algorithms and gradient modeling (machine learning). The next decade will see refinements of this mediation, as the theoretical work being done today becomes embedded in self-modifying code that is able to reduce the friction of human interactions. This is what data scientists do, ultimately, and while there are advantages to this, there are also deep ethical and technical questions that need to be worked out as we draw the line about how much automated mediation is too much.

This is why we run Data Science Central, and why we are expanding its focus to consider the width and breadth of digital transformation in our society. Data Science Central is your community. It is a chance to learn from other practitioners, and a chance to communicate what you know to the data science community overall. I encourage you to submit original articles and to make your name known to the people that are going to be hiring in the coming year. As always let us know what you think.

In media res,
Kurt Cagle
Community Editor,
Data Science Central