MACHINE DATA ANALYTICS

ROI and Business Impact of Glassbeam Clinsights™ for Healthcare Providers

Puneet Pandit
Sep 18, 2019

Having met several C-suite executives in recent customer meetings, it has become increasingly clear to me the immense value and ROI we bring to the table for healthcare providers using our AI/ML analytics platform. 

Taking a few cues from our recently published e-Book for Healthcare Provider market, here is an example of how this successful discussion goes:

Why Visiting Our Booth at AHRA 2019 is an Unmissable Opportunity to Recover Thousands of Dollars in Lost Revenues

Puneet Pandit
Jul 19, 2019

Are you attending the AHRA 2019 Event? If you are, you must plan on visiting our booth #205.  

This event is designed to keep up with the latest developments in the world of imaging, with a keen focus on using AI-driven analytics in the clinical engineering and radiology world.

Who Owns the Data – Part 2

Puneet Pandit
May 16, 2019

In Part 1 of this blog series, I set the stage to understand who owns the machine data generated by medical devices such as CT, MRI, and so on. We also discussed how the restrictions on device data evolved over time and the implications on healthcare providers’ maintenance programs.

Expanding Customer Base and Thought Leadership Conversations That Make Us Proud of Q1 FY2019

Puneet Pandit
Apr 16, 2019

Welcome to the first newsletter of 2019! As always, we present some of the key milestones we have achieved last quarter. This quarterly recap highlights the ways we are bringing all our business functions to make a positive impact on our customers and our partner ecosystem.

Growth Momentum in Our Customer Base Continues

Machine Data Analytics from Glassbeam: The Antidote to a Challenging Environment

Puneet Pandit
Jul 24, 2018

It is well known that margins in the medical device industry are eroding, partly due to the fact that nearly all health care providers are involved in some type of group purchasing organization (GPO) and that GPOs account for nearly three-quarters of provider purchases. 

Redefining Art of Analyzing Medical Machine Logs: Glassbeam Q2 Milestones and Momentum

Puneet Pandit
Jul 17, 2018

Most would consider analytics a science. The Glassbeam team considers analytics an art of combining impermeable truth from machine logs with deep healthcare domain expertise.  As we expand our penetration of the healthcare market after spending years in the data center world, where the gold standard for machine uptime was 99.999 percent, we have recognized a huge opportunity since the acceptable machine uptime for medical equipment ranged from 90 to 97 percent.

Data Doesn’t Lie: 5 Ways Hospitals Can Use Machine Log Data

Pawan Jheeta
Jul 04, 2018

Maximizing uptime of diagnostic equipment is vital to both patients and healthcare organizations. As medical imaging equipment becomes more sophisticated and the need for healthcare organizations to improve their availability becomes more acute, so does the value of machine log data and advanced analytics. Here, we’ve listed several important ways that hospitals can use machine log data and predictive/prescriptive analytics to optimize operational efficiency and revenues.

Semiotic Parsing Language (SPL) - Breakthrough DSL for IIoT Analytics - Part Two

Ashok Agarwal
Jun 13, 2018

In this section, we will investigate how Glassbeam’s DSL called SPL (Semiotic Parsing Language) helps in parsing multi-structured machine logs.

SPL Terminology:

Namespace:

SPL allows a log file to be treated as a hierarchical document consisting of multiple segments (or sections). Each hierarchical segment is called a namespace. This allows for zeroing in on the exact section to parse specific elements from, thus localizing the scope of extracts.

7 Must Have Items on Your Checklist Before You Consider Machine Data Analysis

Vijay Vasudevan
Jun 05, 2016

These are the 7 key factors that highlight whether performing machine data analysis realizes credible value for you.

1. Prepare the raw data —

Most of machine data are in the form of logs. Industrial machines are constantly producing valuable operational data (call-home data) on configuration, performance, usage, and other important parameters that define the very life of the device in the field.

Geting the most out of akka clusters

SURAJ ATREYA
Aug 14, 2015

Anyone serious about distributed systems or building one, commonly encounters issues such as replication, consistency, availability and partition tolerance (CAP) [1]. In a real life scenario, partition tolerance is inevitable. So the system must be able to handle partition tolerance when there are network outages. Therefore, ‘P’ in the CAP is a must for any distributed system. This has been backed by Peter Deutsch in his (EIGHT FALLACIES OF DISTRIBUTED COMPUTING).

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