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.
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.
Industrial internet network is a complex mix up of OT, IT, and business operations setup. At the heart of the large computing that’s going to take place is the infrastructure. It’s not just high speed data transmission networks that are going to define the reliability of the infrastructure, it’s the many sub layers that need to meet at the vortex to make IIoT networks work.