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.
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.
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.
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:
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.
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.
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.
Glassbeam is pleased to announce Puneet (CEO) will be sharing some great insights on Machine Data Analytics on Nov 9 at the IoT Summit, Chicago.
Puneet will share how Glassbeam is creating value for product manufacturers from machine logs and the importance of partnerships at summit. The presentation is scheduled to begin at 10:15 am. Other principle speakers include Barclay Knapp from M2M Spectrum Networks.