Glassbeam for Glassbeam (Part 3) – Making our Support Team Super Heroes!

In the second post of this series, I have listed the high level use cases of Glassbeam for Glassbeam across our internal teams: Technical Support, Sales, Engineering, and Product Management. In the next 4 posts, I will dive deep into the use cases for each of the above teams and talk about the value Glassbeam for Glassbeam as a data-driven decision making solution, brings to each team.

PTC and Glassbeam to Showcase Industry Leadership in Internet of Things Analytics

We are working with our partner PTC to produce a series of thought-leadership materials aimed at helping developers, business managers, Internet of Things enthusiasts get started on the ThingWorx Analytics Internet of Things (IoT) platform.

Here’s the announcement from PTC on the upcoming thought leadership meetups:

On to liveworx 2016

We’re just about wrapping up a wonderful week at TSW in San Diego where we met with tons of Support practitioners and discussed with them numerous ways to help them transform their Support organizations into Profit Centers. Now, we’re getting prepped for LIVEWORX – traditionally the biggest Conference for us every year

Actionable feedback right through edge computing

Continuing our discussion on Edge Computing and Analytics ….. Remember WE SAID that a key benefit of Edge was Local Decision Making. Typically, that will preclude access to the install base data. However, there is a wealth of information which can be gleaned from the install base data (such as machine learning output). It seems a shame to not be able to utilize that on the edge.

Glassbeam edge computing – a primer

As the Internet of Things inevitable starts coming into it’s own, the origin of data has evolved from people to machines to “things”. Technologies emerged from leaders like Google and Facebook to enable analyzing tons of data in massive data farms deployed in the cloud. All that is well and good, but the approach itself needed moving this “ton” of data to a central location, partition it across large number of nodes so that analysis could be parallelized. Imagine, Netflix has over 1,000 nodes in their cluster. Hmmmm, doable, but at some point the laws of physics start to interfere.

Glassbeam studio architecture

GLASSBEAM STUDIO is a one of a kind software which helps in data transformation and preparation, visualizing data, deployment and much more. The Glassbeam Studio technology is modeled on a client-server architecture with functionalities balanced neatly between the client and the server. This software is architected to offer an infinitely scalable, seamlessly, and functionally compelling way to transform even the most complex machine data into valuable business insights.

Metadata extraction

Semiotic parsing language – the language for machine data analytics

The Internet of Things (IoT), an ever-growing number of connected devices, generates vast amounts of data, called “Machine Data”, complex in variety, volume and velocity. Machine data is information about the device, like its configuration, status, performance, usage and more. Manufacturers across data-intensive industries – such as storage, wireless, networking and medical devices – are struggling to make sense of all this data. Analyzing machine data can help organizations with reactive diagnostic activity, predictive problem identification, and business intelligence.

A feather in our (academic) cap

We spent the better part of the day in the verdant campus of UC, Santa Cruz. And for good reason – Glassbeam is FUNDING a new Center of Excellence at the University to help create stronger competencies in the area of Data Science research.

Remote maintenance of escalators and elevators using log analytics

Every machine produces logs. Even those escalators and elevators at your favorite mall or office space. Here’s what will happen next. Those escalators will talk to a forecaster sitting at a remote location,and transmit data that will be tremendously valuable for their own maintenance.

But what messages are they transmitting? What’s the objective?

Typical messages that are getting collected include information on wear and tear of mechanical parts, peak load times, usages of interactive controls, activation of alarms and safety thresholds, and so on.