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We deliver analytical insights, leverage machine complex data, and empower end-users with business intelligence.

Four Things Medical Device Manufacturers Should Consider Before Investing in an Analytics Solution

The market for Big Data is expected to grow to $118.52 billion by 2022 as more and more companies begin to realize the business benefits of Big Data. For medical device manufacturers, Big Data and data analytics can enable them to deliver better customer service by giving them greater insights on asset performance. However, investing in a data analytics solution requires careful study and assessment.

How much does medical equipment downtime cost hospitals?

The increasing demand for better diagnostics is putting pressure on hospitals to invest in high-end medical imaging equipment such as ultrasound and X-Ray devices, computerized tomography (CT) scanners, magnetic resonance imaging (MRI) scanners, and positron emission tomography (PET) scanners. These machines can range from several hundred thousand to a few million dollars each.

Glassbeam for Glassbeam (Part 6) – In SaaS, ‘Sale’ is not the end of Sales cycle

In my concluding post, let’s look at the use cases that help our Sales/Account teams to manage our customer relationships efficiently, discover upsell and cross sell opportunities and analyse application use across our install base to help improve the adoption of our product. 

Glassbeam Analytics - Sales Use Cases

Glassbeam for Glassbeam (Part 5) – Data Driven Product Management

Glassbeam for Glassbeam - Product Management
 
Product Management is not an easy thing to do. Bulk of the company’s resources and direction is driven by the decisions made on the roadmap of the product. Bad decisions lead to wasted effort internally and in the best case lead to unhappy customers and in the worst case, lead to losing customers. 
 

Glassbeam for Glassbeam (Part 4) - Helping Smart Engineering Teams be Smarter

In this post, let me explain how Glassbeam’s engineering team uses the Glassbeam Analytics solution to be an effective and responsive team to every bug and the not-so-nice user experiences that our customers could potentially face.

Here are some guidelines that our Engineering team uses to seek answers from log data-driven Glassbeam Analytics:

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.

Glassbeam for Glassbeam (Part 2) – Why, Who and for What!

In my first blog in this series (here), I talked a little about the importance of log analytics in general and specifically I touched upon the types of logs and the frequency of our log data collection.  In this post, let’s go over the use cases of our teams in Glassbeam. The use cases our teams have are very similar to the use cases that we solve for our customers. 

Glassbeam for Glassbeam –Dogfooding and Loving it!

At Glassbeam, we have always believed in eating our own dog food and why not! We have the same use cases that our customers use for our platform. But, before I go deep into the internal use cases that we use Glassbeam for, let me explain how we collect our infrastructure logs and the types of logs we collect.

Medical Device Usability is a Dire Need, Not a Nice-to-Have Option

The connected medical equipment is here and the possibilities of a fresher, richer future are staggering. Imagine CAT scanners talking back to technicians, initiating reports on its profitability, or does a self-diagnosis and tells product management they ought to replace it. These possibilities are not too far into the future but we’re almost there.

What’s On the Chief Data Officers’ Table to Wrestle Data Preparation

The need for proper tool to organize and understand the underlying patterns in machine data exists. From what we’ve been hearing, that’s gripping the Chief Data Officer’s mind. Why?

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