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science and need to know about data - you need to now understand about technologies
and the technology evolution to help you shape [strategy]. … There are some big changes
happening or about to come about … in how computation can be done and what sorts of
algorithms are now scalable … . Problems that were exponentially hard to solve or al-
gorithms that were exponentially hard to run but would really solve a problem correctly for
you - a Data Science problem, or an optimization problem - it's going to become a reality
that you can perform those computations. Now you need to understand how new types of
computers work, in addition to understanding how new storage paradigms and data-repres-
entation paradigms work, and also still have at your core the understanding of statistics,
machine learning, and data science.”
Downs also sees the practice of Data Science spreading well beyond primary questions
of markets, products, and customer service, and expanding dramatically to address more
and more questions related to in-house efficiencies and cost-cutting. At such firms as Gen-
eral Motors, Data Science is already as much about safety, production practices, quality
control, and human resources issues as it is about marketing, advertising, and customer
demographics.
Another major area of new innovation will be the art and tools related to performing
Data Science on information generated by the so-called “Internet of Things” and wearable
technologies. Here we are talking about tools and skills to help us manage and wrangle data
related to ubiquitous location awareness along with location context. As Downs says: “It's
more about the [location] sensing going everywhere with people and with physical things
and [the proliferation of] that. It is about contextualizing that in time, which we're kind of
used to, but also in physical or virtual space.”
Throughout all of this, the definition of a Data Scientist - that rare Unicorn of the busi-
ness world - will continue to be a creative “mash-up” of skills: hacker, analyst, program-
mer, statistician, communicator, market researcher, and - at times - clairvoyant.
In the final analysis few fields are as well positioned for exponential growth as is Data
Science. The flood waters of Big Data will never stop rising - and with them both the prom-
ise and the problems of their enormity, velocity, and variety.
In other words, the promise and role of Data Science shall progress in step with the ad-
vance of technologies for generating, harvesting, and manipulating unstructured data. And
these show every sign of conforming to the famous “Moore's Law,” originally promulgated
decades ago by Gordon W. Moore, cofounder of Intel and Fairchild Semiconductor, which
says, generally, that the speed and capabilities of data processing roughly double every two
years. Thus far, Moore's Law has held up.
Ernest Dimnet has commented: “Too often we forget that genius, too, depends upon
the data [knowledge] within its reach, that even Archimedes could not have devised Edis-
