From ISACA.org.

Karina Korpela, CISA, CISM, CISSP, PMP, IT Audit Manager, AltaLink

Let me say in advance that you will not learn a new audit or data analytics technique from this article. It is purely to demonstrate the power of data analytics on a massive scale. My goal is to inspire you.

A few months ago I attended a conference that featured Dr. Neil deGrasse Tyson as the keynote speaker. And yes, he is that guy from the Cosmos: A Spacetime Odyssey TV show.

He was hilarious and engaged the audience, receiving a standing ovation from the data geeks. He inspired me to make even more use of data analytics.

His first comment was about Pluto:  “It is not a planet. Get over it.” And then he said:  “We demoted Pluto because we had more data.” Whaaat? That sentence resonated with me so much that I started researching about the data that demoted Pluto.

I learned how powerful new ground and space-based observatories have completely changed our understanding of the outer solar system. As these tools have evolved over the past generation, so too has our picture of the universe. New capabilities have provided new understandings about our place in the cosmos, but they have also unleashed a baffling torrent of data. Amazing discoveries might be right in front of us, yet hidden within all that information.

Since 2000, the Sloan Digital Sky Survey at Apache Point Observatory in New Mexico has imaged more than one-third of the night sky, capturing more than 930,000 galaxies and 120,000 quasars. Computational analysis of Sloan’s prodigious data set has uncovered evidence of some of the earliest known astronomical objects and has determined that most large galaxies harbor super massive black holes. It has even mapped out the three-dimensional structure of the local universe.

So it was just a question of time until someone started searching for large objects everywhere, including the Kuiper Belt. It was astronomer Mike Brown who was convinced by the data on the Belt that there must be many more nearby objects and that some of them were potentially larger than Pluto.

Bingo! In 2003 Brown thought he had found a new planet that was larger than Pluto. He named it Eris (EER-is). Instead of being the only planet in its region, like the rest of the solar system, Pluto and its moons are now known as just a large example of a collection of objects in the Kuiper Belt.

“You didn’t lose a Planet; you gained a new place in the universe.” Dr. Neil deGrasse Tyson

The Kuiper Belt data that led to Pluto’s demotion came from routine observations at Mount Palomar Observatory in California. These data are stored at many repositories, including the National Optical Astronomy Observatory (NOAO) in the United States. The NOAO collects a large quantity and variety of scientific data products, including images, spectra, catalogs, etc., from many instruments deployed on two continents. Wow!

The NOAO has archived all data from their telescopes, accumulating about 10 terabytes of data annually. These data are now available to the public, which is actually an exciting discovery for a data geek like me.

The key to maximizing knowledge extracted from this massive amount of data is the successful application of data mining and knowledge discovery techniques. The data can help classify stars, galaxies and planetary nebulae based on images and spectral parameters, forecasting of sunspots and geomagnetic storms from solar winds, antimatter search in cosmic rays, etc.

Astronomy professor Robert Brunner said:  “Before Sloan, individual researchers or small groups dominated astronomy. You’d go to a telescope, get your data and analyze it. Then Sloan came along and suddenly there was this huge data set designed for one thing, but people were using it for all kinds of other interesting things.” Brilliant!

There you go—factual big data demoted Pluto and not some technicality pushed by a small group of scientists.

I hope you search for interesting ways to use the data available to you. Perhaps to revise long-standing decisions and notions formed when data and easy-to-use analytics tools were less reliable. What truth is hidden on your data just waiting to be set free? You may want to reflect on how much of this all applies to corporate environments.