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Trusted Business Advisors, Expert Technology Analysts

Items Tagged: machine+data

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
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
Profiles/Reports

Qumulo Tackles the Machine Data Challenge: Six Customers Explain How

We are moving into a new era of data storage. The traditional storage infrastructure that we know (and do not necessarily love) was designed to process and store input from human beings. People input emails, word processing documents and spreadsheets. They created databases and recorded business transactions. Data was stored on tape, workstation hard drives, and over the LAN.

In the second stage of data storage development, humans still produced most content but there was more and more of it, and file sizes got larger and larger. Video and audio, digital imaging, websites streaming entertainment content to millions of users; and no end to data growth. Storage capacity grew to encompass large data volumes and flash became more common in hybrid and all-flash storage systems.

Today, the storage environment has undergone another major change. The major content producers are no longer people, but machines. Storing and processing machine data offers tremendous opportunities: Seismic and weather sensors that may lead to meaningful disaster warnings. Social network diagnostics that display hard evidence of terrorist activity. Connected cars that could slash automotive fatalities. Research breakthroughs around the human brain thanks to advances in microscopy.

However, building storage systems that can store raw machine data and process it is not for the faint of heart. The best solution today is massively scale-out, general purpose NAS. This type of storage system has a single namespace capable of storing billions of differently sized files, linearly scales performance and capacity, and offers data-awareness and real-time analytics using extended metadata.

There are a very few vendors in the world today who offer this solution. One of them is Qumulo. Qumulo’s mission is to provide high volume storage to business and scientific environments that produce massive volumes of machine data.

To gauge how well Qumulo works in the real world of big data, we spoke with six customers from life sciences, media and entertainment, telco/cable/satellite, higher education and the automotive industries. Each customer deals with massive machine-generated data and uses Qumulo to store, manage, and curate mission-critical data volumes 24x7. Customers cited five major benefits to Qumulo: massive scalability, high performance, data-awareness and analytics, extreme reliability, and top-flight customer support.

Read on to see how Qumulo supports large-scale data storage and processing in these mission-critical, intensive machine data environments.

Publish date: 10/26/16
news

Taneja Group Recognizes Qumulo Data-Aware Scale-Out NAS As Top Solution for Tackling Machine Data

Qumulo, the leader in data-aware scale-out NAS, today announced that Taneja Group has published a new report titled "How Qumulo Technology Tackles Machine Data Storage Challenges."

  • Premiered: 11/17/16
  • Author: Taneja Group
  • Published: Yahoo! Finance
Topic(s): TBA Qumulo TBA scale-out TBA data-aware TBA NAS TBA machine data TBA Storage TBA Big Data TBA scalability TBA High Performance TBA analytics TBA reliability
Resources

A Whole New World: Machine-Generated Data and Massive Scale-Out NAS

Computer users aren’t top data producers anymore. Machines are. Raw data from sensors, labs, forensics, and exploration are surging into data centers and overwhelming traditional storage. There is a solution: high performance, massively scale-out NAS with data-aware intelligence. Join us as Jeff Cobb, VP of Product Management at Qumulo and Taneja Group Senior Analyst Jeff Kato explain Qumulo’s data-aware scale-out NAS and its seismic shift in storing and processing machine data. We will review how customers are using Qumulo Core, and Nick Rathke of the University of Utah’s Scientific Computing and Imaging (SCI) Institute will join us to share how SCI uses Qumulo to cut raw image processing from months to days.

Presenters:
Jeff Kato, Senior Analyst & Consultant, Taneja Group
Jeff Cobb, VP of Product Management, Qumulo
Nick Rathke, Assistant Director for IT, The Scientific Computing and Imaging Institute (SCI)
 

  • Premiered: 11/30/16
  • Location: Live
  • Speaker(s): Jeff Kato, Taneja Group; Jeff Cobb, Qumulo; Nick Rathke, SCI
Topic(s): TBA Topic(s): Qumulo Topic(s): TBA Topic(s): Storage Topic(s): TBA Topic(s): High Performance Topic(s): TBA Topic(s): scale-out NAS Topic(s): TBA Topic(s): data-aware Topic(s): TBA Topic(s): machine data Topic(s): TBA Topic(s): Big Data