By 2020, some analysts predict there will be roughly 30 billion connected IoT devices – including refrigerators, racecars, robots and many other interesting items.  From connected thermostats, coffee pots, medical devices, lighting, audio speakers, TVs, factories, buildings and remote controls nearly everything will have an IP address capable of transmitting data for analysis.  

 So, if one major retailer already generates 2.5 petabytes of data in an hour of transaction data – more than 167 times the amount of data stored in the Library of Congress – what will become of the data deluge?  Should we simply wait for all of the yottabytes (1 trillion gigabytes) to show up in Bunblehive?  Or maybe wait for data science unicorns and PhD statisticians to analyze and make sense of the ever-growing mountain data? 

 

Of course not. Those who wait too long may not be in business tomorrow.

 

Emerging trends

 

These four key trends are disrupting entire industries, and challenging the status quo:

 

  • The rise of the citizen data scientist: These line of business workers typically have a deep understanding of the business and a penchant for analytics. But, they’re not mathematicians.  They use numbers to make informed decisions to help drive their organization forward.  The challenge will be how to ensure best practices and enable reuse within organizations.
  • IoT analytics at the edge: To be successful, businesses can’t rely on the age-old practice of sending all the data back to the mothership for analysis.  Organizations need to deploy and monitor entire data prep and analytic workflows to gateways and endpoints so decisions can be made near the point of impact – as soon as feasibly possible.  Sure, analytic results may be sent upstream and combined with other output for more sophisticated analytics. But, sending sub-second data to tell me the light bulb is still on just doesn’t make sense.
  • Vertical market use cases: To remain competitive, companies need flexible, open, scalable, secure and customizable analytics technology platforms so they can innovate and solve specific, challenging problems within their domain.   Organizations that achieve this will gain a significant competitive advantage with their “analytic secret sauce”.
  • Analytics innovation:  These platforms combine best-of-breed methodologies and algorithms from analytic marketplaces (such as Apervita, Algorithmia and Experfy), and leverage open source technology, including R, Python and Spark.   Reaching beyond the expertise within your company and harnessing the “collective intelligence” of the world accelerates innovation.

 

To address these challenges, Tim Alosi of Sanofi stated that one of the big benefits will help the “compression of time[1]” and allow organizations to act on the data sooner.  

 

How Statistica 13.1 helps your organization

 

The latest version of Statistica provides your organization with several new features so you can more easily adapt to these trends. We’ll talk a bit more specifically about Statistica 13.1 in subsequent blogs.  But, the key capabilities and enhancements of this newest version include:

 

  • Streamlined workflows for the citizen data scientist
  • Edge scoring for IoT analytics
  • Extended in-database analytics
  • Network analytics

 

Interested in learning more?  Register now for our May 12th webcast about Statistica 13.1. to gain a deeper understanding of how this release will benefit your organization.



[1] IoT Analytics And Dell Statistica Deliver Time Compression.  Gill Press, Forbes. April 15, 2016.

David Sweenor

About David Sweenor

From requirements to coding, reporting to analytics, I enjoy leading change and challenging the status quo. Over 15 years of experience spanning the analytics spectrum including semiconductor yield characterization, enterprise data warehousing, reporting/analytics, IT program management, as well as product marketing and competitive intelligence. Currently leading Analytics Product Marketing for the Dell Software Group.

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