June 6, 2018 Loren Anderson

Help Wanted: Macroscope Developer…Luddites Need Not Apply

Welcome back!  In a previous blog (28 November 2017) we introduced the Macroscope, a powerful tool able to see the “strange creatures” in big data’s dimensions. Let’s look at how it works.

Luddite Definition

Its construction is a process of asking the right questions, exploring the data stores, enabling machine learning and analytic modeling, and focusing our gaze to answers unique to the datasets. These answers give the macroscope its luminescence and fails without it.  This does not mean the answers have to be obvious to all for the tools to be useful.  In most cases this clarity of vision comes from the Subject Matter Expert (the farmer/agronomist, forester, urban planner, geophysicist, etc.). The data scientist doesn’t know what signs a heifer exhibits when going into heat. But a dairy farmer does, and a macroscope like that translates into dollars for the farmer. The analyst doesn’t know why trees might be producing excess anthocyanins in leaves, but an arborist does, plus much more context about how other measurable points of interest lead to better conclusions.  The point is, Macroscopes should not be the domain of the data scientist, but rather the collective approach to a problem where data science can provide deeper insight.

We need a Macroscope to see the Big Data picture

Whether you know it or not, an industrial-data revolution is coming on the boot heels of this and other disruptive technologies.  Sectors of the economy like precision agriculture are being uprooted, shifted, and advanced more rapidly causing those left in the technical darkness to be stagnate or to evaporate overnight, and in the morning a “factory” dedicated to the new tech stands in its place.  History has never been on the side of the Luddite, who carries the very real fear of being stripped of his livelihood when a better, more efficient way to do the job has been found.  Technologies that spark industrial revolutions legitimize their emotional response. The lesson – you don’t need to be a data scientist to contribute to this disruption. Embrace it. Ignore it at your risk!

Well into the 20th century, access to information was a privilege offered only to the elite. Now, even those in far-flug locations on the periphery of the web  have access to the same information. The team of developers in Bangalore can exploit the same data that team of PhDs in Boston or Mountain View can access. Woe to those that ignore or fear the tsunami coming. The agile and data-savvy are headed for the high ground, a great place to setup a Macroscope.


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