Event Information:

• Mon

  22

  Feb

  2016

  IoT in 2016: The Game is Changing

  It looks like 2016 will indeed be the year when IoT will come into its own. To use a Wall Street term, every market signal is flashing a ‘Strong Buy’. All indicators, including market size, use cases, valuation, M&A, number of job postings, number of analysts covering the space, etc., point to an exponential growth in IoT in 2016. Further, most of the industry analysis confirms that the greatest driver of value will be deep analytics on data – with the other pieces: connectivity, instrumentation, bandwidth, and others becoming highly commoditized or being given away for free. And that is why it’s a great time to be at Glassbeam – one of the fastest growing IoT startups in the Silicon Valley. 

  For those unfamiliar with Glassbeam, it is an IoT Analytics company based in the Silicon Valley that I co-founded with Puneet Pandit in 2009. Our mission is simple, yet powerful: to derive structure and meaning from complex, unstructured machine data generated from IoT-connected devices, and to present these findings through an actionable UI that provides immediate and compelling benefits for numerous operational ideas: Support, Engineering, Product Management and Services.

  In the course of our journey, we developed our patent-pending language – Semiotic Parsing Language (SPL). SPL is the industry’s first ever tool to truly apply Semiotics – the science of assigning meaning and structure – to complex unstructured data. SPL made it 10X faster to convert unstructured data to a structured format as compared to existing tools like ETLs. However, we did have to utilize our professional services arm to engage with customers to author SPLs as the process required technical skills.

  Now, recently announced 'GLASSBEAM STUDIO' takes this process into a newer orbit by automating the process of creating SPL. With merely a few clicks, technical or non-technical users, can instantly model even the most dauntingly unstructured data, transform it into usable format, and prepare it for analysis and for display inside visualization tools. What was 10X faster is now 100X faster! This will have a material impact on the IoT Analytics industry. This innovation is game-changing because today Terabytes of data that contain a veritable goldmine of information are simply being discarded because there is simply no cost-effective way to transform the data. Glassbeam is breaking a new frontier with this innovation as now all this unstructured data can be parsed and analyzed, and will come into play for Analysis. Our team has deep pedigree in this space and many person-years of effort has gone into building this highly differentiated, infinitely scalable, and extremely powerful Analytics platform.

  Here are the basic building blocks on which Studio is built:

  

  We’ve built Studio with the philosophy of creating a holistic, IDE-like, platform that can be used to seamlessly transform unstructured log. Some of my favorite features include the versatile SPL editor, and the ability to transpose rows and columns to effectively model the data. The output of Studio is prepared data, and this prepared data has numerous ‘upstream’ applications - for example, setting up sophisticated Complex Event Processing (CEP) (same as ‘Rules and Alerts’) models that can capture and act upon anomalous conditions.  This transformed data can also be fed into leading visualization applications like Tableau and ThingWorx composer.

  Close on the heels of the Studio announcement, Glassbeam is unveiling another powerful capability – Glassbeam Edge – that offers state-of-the-art Analytics close to the actual IoT device. So, industries with assets in far-flung geographical areas like Oil/Gas, Mining, etc. don’t need to fret anymore about the ‘data center roundtrip’ that generated data often has to take. Timely, information-driven decisions can be taken in close physical proximity to the device; and voluminous data can be sent back to the cloud for deeper inspection using advanced machine learning algorithms (Glassbeam integrates well with ThingWorx Machine Learning).  Using sophisticated peer-to-core and peer-to-peer protocols, and the ability to periodically push critical findings to the edge, we have created a very compelling and economically-viable architecture for distributing the data analytics workload between the edge and the cloud.

  Here are key elements of Glassbeam’s edge capabilities:

  

  Please visit www.glassbeam.com to learn more. I am sure you will be as thrilled as I am.