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Items Tagged: Glassbeam

Profiles/Reports

Glassbeam SCALAR: Making Sense of the Internet of Things

In this new era of big data, sensors can be included in almost everything made. This “Internet Of Things” generates mountains of new data with exciting potential to be turned into invaluable information. As a vendor, if you make a product or solution that when deployed by your customers produces data about its ongoing status, condition, activity, usage, location, or practically any other useful information, you can now potentially derive deep intelligence that can be used to improve your products and services, better satisfy your customers, improve your margins, and grow market share.

For example, such information about a given customer’s usage of your product and its current operating condition, combined with knowledge gleaned from all of your customers’ experiences, enables you to be predictive about possible issues and proactive about addressing them. Not only do you come to know more about a customer’s implementation of your solution than the customer himself, but you can now make decisions about new features and capabilities based on hard data.

The key to gaining value from this “Internet Of Things” is the ability to make sense out of the kind of big data that it generates. One set of current solutions addresses data about internal IT operations including “logfile” analysis tools like Splunk and VMware Log Insight. These are designed for a technical user focused on recent time series and event data to improve tactical problem “time-to-resolution”. However, the big data derived from customer implementations is generally multi-structured across streams of whole “bundles” of complexly related files that can easily grow to PB’s over time. Business user/analysts are not necessarily IT-skilled (e.g. marketing, support, sales…) and the resulting analysis to be useful must at the same time be more sophisticated and be capable of handling dynamic changes to incoming data formats.

Click "Available Now" to read the full analyst opinion.

Publish date: 10/21/13
news / Blog

Mining the Data Your Clients Produce: Glassbeam SCALAR Lights Up Value

This week Glassbeam announced a new solution for vendors whose products in the hands of clients produce mountains of potentially valuable "back-end" data. Mining that data which can include streams of ongoing customer site configurations, usage, status, location or really any relevant sensor results can help drive vastly better customer support and satisfaction, new and timely revenue opportunities, and better informed product management decisions. It might seem straightforward to extract value out of big data once you have it, but trying to "home grow" processing and coherent analysis of PB's of "multi-structured" files to feed all those business processes by using IT logfile utilities or low-level Hadoop coding could take a big effort and fall far short of spectacular.

  • Premiered: 10/22/13
  • Author: Mike Matchett
Topic(s): Glassbeam Big Data machine data Internet of Things
Resources

Proven ROI for Internet of Things (IoT) Data Center Infrastructure Market

Internet of Things (IoT) is a hot trend in today’s economy of  connected devices. Every high-tech device in the data center industry – storage, servers, switches, application software, or any appliance – generates copius amounts of machine data that can be analyzed to help the manufacturer gain operational and strategic insights. These insights assist in reducing costs to support customers by lowering mean time to resolution (MTTR) per case.  They also enable manufacturers to build and sell specific value add services to their customers with the objective of being proactive, predictive and prescriptive in front of their top enterprise accounts.  Finally, deep insights can be gleaned off this kind of machine data analytics, if done the right way, to funnel strategic information onto future product roadmaps.  All of these benefits are very quantifiable if there is a solution like Glassbeam in place in such markets.

This webinar will discuss the right elements of such a solution, lay out the foundation of ROI with specific benefits, and discuss a case study with a Fortune 100 account of Glassbeam.

  • Premiered: 06/09/14 at 11 am PT/2 pm ET
  • Location: OnDemand
  • Speaker(s): Mike Matchett, Senior Analyst at Taneja Group; Puneet Pandit, Co-founder & CEO, Glassbeam
  • Sponsor(s): Glassbeam, BrightTALK
Topic(s): TBA Topic(s): Glassbeam Topic(s): TBA Topic(s): Internet of Things Topic(s): TBA Topic(s): IoT Topic(s): TBA Topic(s): Data Center Topic(s): TBA Topic(s): Infrastructure Topic(s): TBA Topic(s): analytics
news

Internet of Things data will boost storage

IT departments can benefit from storage vendors eavesdropping on their arrays to help them curb the amount of Internet of Things data inundating their storage shops.

  • Premiered: 06/09/14
  • Author: Mike Matchett
  • Published: Tech Target: Search Storage
Topic(s): TBA Mike Matchett TBA IoT TBA Internet of Things TBA Big Data TBA data sensor TBA Storage TBA Splunk TBA VMWare TBA vCenter TBA Log Insight TBA Automation TBA Glassbeam
news / Blog

IoT Goes Real-Time, Gets Predictive - Glassbeam Launches Spark-based Machine Learning

In-Memory processing was all the rage at Strata 2014 NY last month, and the hottest word was Spark! Spark is big data scale-out cluster solution that provides a way to speedily analyze large data sets in-memory using a "resilient distributed data" design for fault-tolerance. It can deploy into its own optimized cluster, or ride on top of Hadoop 2.0 using YARN... I haven't done justice to Spark itself and perhaps its biggest onrushing use case - taming the real-time data from from the Internet of Things (IoT)...

  • Premiered: 11/21/14
  • Author: Mike Matchett
Topic(s): Glassbeam Big Data Spark In Memory GridGain Machine Learning
Profiles/Reports

Converged IT Infrastructure's Place in the Internet of Things

All of the trends leading towards the world-wide Internet of Things (IoT) – ubiquitous, embedded computing, mobile, organically distributed nodes, and far-flung networks tying them together - are also coming in full force into the IT data center. These solutions are taking the form of converged and hyperconverged modules of IT infrastructure. Organizations adopting such solutions gain from a simpler building-block way to architect and deploy IT, and forward-thinking vendors now have a unique opportunity to profit from subscription services that while delivering superior customer insight and support, also help build a trusted advisor relationship that promises an ongoing “win-win” scenario for both the client and the vendor.

There are many direct (e.g. revenue impacting) and indirect (e.g. customer satisfaction) benefits we mention in this report, but the key enabler to this opportunity is in establishing an IoT scale data analysis capability. Specifically, by approaching converged and hyperconverged solutions as an IoT “appliance”, and harvesting low-level component data on utilization, health, configuration, performance, availability, faults, and other end point metrics across the full worldwide customer base deployment of appliances, an IoT vendor can then analyze the resulting stream of data with great profit for both the vendor and each individual client. Top-notch analytics can feed support, drive product management, assure sales/account control, inform marketing, and even provide a revenue opportunity directly (e.g. offering a gold level of service to the end customer). 

An IoT data stream from a large pool of appliances is almost literally the definition of “big data” – non-stop machine data at large scale with tremendous variety (even within a single converged solution stack) – and operating and maintaining such a big data solution requires a significant amount of data wrangling, data science and ongoing maintenance to stay current. Unfortunately this means IT vendors looking to position IoT oriented solutions may have to invest a large amount of cash, staff and resources into building out and supporting such analytics. For many vendors, especially those with a varied or complex convergence solution portfolio or established as a channel partner building them from third-party reference architectures, these big data costs can be prohibitive. However, failing to provide these services may result in large friction selling and supporting converged solutions to clients now expecting to manage IT infrastructure as appliances.

In this report, we’ll look at the convergence and hyperconvergence appliance trend, and the increasing customer expectations for such solutions. In particular we’ll see how IT appliances in the market need to be treated as complete, commoditized products as ubiquitous and with the same end user expectations as emerging household IoT solutions. In this context, we’ll look at Glassbeam’s unique B2B SaaS SCALAR that converged and hyperconverged IT appliance vendors can immediately adopt to provide an IoT machine data analytic solution. We’ll see how Glassbeam can help differentiate amongst competing solutions, build a trusted client relationship, better manage and support clients, and even provide additional direct revenue opportunities.

Publish date: 08/18/15
news

Glassbeam Enters Converged and Hyper Converged Infrastructure Market

Company's Ground Breaking Machine Data Analytics Solution Already in Production With Two Leading Product Vendors

  • Premiered: 08/31/15
  • Author: Taneja Group
  • Published: MarketWatch
Topic(s): TBA Glassbeam TBA converged TBA Converged Infrastructure TBA hyperconverged TBA hyperconvergence TBA hyper convergence TBA machine data TBA analytics TBA Big Data TBA Internet of Things TBA IoT TBA data analytics TBA Saas TBA SCALAR TBA Cloud
news

When data storage infrastructure really has a brain

Big data analysis and the internet of things are helping produce more intelligent storage infrastructure.

  • Premiered: 09/06/16
  • Author: Mike Matchett
  • Published: TechTarget: Search Storage
Topic(s): TBA Big Data TBA big data analytics TBA Internet of Things TBA IoT TBA storage infrastructure TBA Storage TBA Intelligent Storage TBA CPU TBA software-defined TBA software-defined storage TBA SDS TBA HPE TBA StoreVirtual TBA hyper-converged TBA hyper-converged architectures TBA HyperGrid TBA Nutanix TBA Pivot3 TBA SimpliVity TBA Optimization TBA Datrium TBA Provisioning TBA Artificial Intelligence TBA Cloud TBA elastic cloud TBA data processing TBA Python TBA Spark TBA API TBA REST API