Is the Wait-State Closing in for Analytics?

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Is the Wait-State Closing in for Analytics?

By: Kevin Fugate

In a computer, a wait-state occurs when a particular process is placed on hold while it waits for the completion of other events.  Only after memory is available, the data arrives, and a processor slice is again made available, can a processor do it’s magic.  Analytics itself has been in a wait-state, but advances in processing power, communications, storage, and programming languages have now converged, closing the wait-state for analytics to tackle the current data demands.

When I started my career in computer technology nearly 30 years ago, our goal was to keep our systems running so they could do calculations faster than an engineer with a slide rule.  Later, desktops were interconnected, and we began the battle for storage space.  In the early 2000’s, processing power prices dropped out and pizza-box sized servers filled our datacenters.  We soon accumulated gigabytes, then terabytes, and eventually petabytes of data.  We pushed this data from one location to another with high-speed processors and complex networks, but we weren’t really using the data.  Then came data mining. We knew there was value in the data; we just needed the tools to analyze it.

Today, the term “Analytics” is on the minds of everyone in business, whether they understand the term or not.  However, large businesses are beginning to learn that analytics can be a corporate, or monetization strategy, understanding it’s the data which will eventually feed AI; but is the complexity of analytics leaving small businesses behind?  Is the successful extrapolation of information from the quantum of existing data going to create barriers between SMB’s and corporate giants?  Most likely not, as SMB’s are more agile and accepting of newer Open Source technologies.

Open Source may place the “Big Data” game on a level playing field for all size businesses, providing the tools needed to crunch and analyze data.  As the technical landscape is radically changing directions, the need for new and adaptive software has never been more in demand.  Open Source programming languages such as Julia, Python, R, and Scala are at the forefront for developers analyzing data, and each can provide incomes over $100k, proving there is money in this data top to bottom.  Open Source solutions go beyond the languages and into the core of Big Data as well.  New database technologies are being created around unstructured data, NoSQL being a good example. The platforms they are running on are adapting as well.  Linux is the operating system of choice for massaging Big Data; I love it when I hear the name “Watson” thrown around as the definition of analytics; it’s not the machine, it’s the agility of the software behind it.  “Watson” may be the giant for Big Data and analytics, and it’s most likely going to be difficult for SMB’s to gain access to it, but it’s not the only platform which eats data.  The Open Source technology for clustering those pizza box servers spoke of earlier are the key components for Docker Containers or Oracle’s OVM technology, both of which are big players in the “Cloud” we hear so much about in relation to analytics.

Hardware is also contributing to the field of analytics. Major players such as Intel and Cisco are developing hardware solutions specifically designed for analytical operations.  Intel unveiled it’s new solid –state drive technology this last March, designed specifically for heavy database loads, and one of the leading analytics company SAS, has partnered with Cisco to deliver an IoT analytical platform.

Charles Baggage created the first mechanical computer in 1837, but we had to wait for the vacuum tube to get to the ENIAC, then we waited for integrated transistors to get us to today.  In 1937, Claude Shannon developed digital circuit design and bandwidth limitations before we were using digital circuits and digital communication lines. They were all in a wait-state before the technologies could converge and produce the computing systems we have today.  The same type of cycle is happening today as we are on the verge of technology opening a world of information through analytics.


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