In my last post, I wrote about the progress Glassbeam has made this year, leveraging the increasing interest in IoT analytics in a wide range of industries. It is now clear that IoT analytics can dramatically improve business efficiency in everything from operations to product development and customer service. Here, I’d like to look forward at where Glassbeam sees IoT advancing in the coming year.
In July, I WROTE about the role IoT analytics can play in optimizing today’s complex storage networks. I wrote in that blog, “Storage system providers have turned to IoT analytics both to help ensure optimal performance and to gain a deeper understanding of how customers are using their solutions.”
It’s a well-known fact that better customer segmentation results in increased profitability for any product company. Better segmentation enables product manufacturers to create highly customized offerings and messaging for each customer (the era of 1-1 marketing); better yet, these offerings are well-aligned with the needs and propensities of that customer.
The question of ROI comes up quite regularly in our sales discussions with prospects. It is natural for our buyers to delve on that topic since many evaluate GLASSBEAM SPEND WITH INTERNAL EFFORTS. We are no doubt on an evangelistic mission to convert naysayers inside product companies who think they can "build" such a solution on their own.
Typically there are three parts of GLASSBEAM VALUE that constitute a very compelling ROI:
In PART ONE of the Glassbeam for Medical blog, we explored how Glassbeam helps customer support save turnaround time and avoid unnecessary replacement costs. Support is one use case that benefits from machine data analytics. But the benefits of such a solution can span across the enterprise from customer support to engineering to sales and marketing.
The ability to gather and harness data from machines and devices connected to the net is increasingly becoming a source of competitive advantage for those who have been thinking ahead. GE is widely cited, with their INDUSTRIAL INTERNET initiative. Cisco has been talking about INTERNET OF EVERYTHING for a while. At Glassbeam, we have been focusing on the infrastructure required to process and analyze machine data for over 3 years now.
Over the last few years of Glassbeam evolution, I have seen many of our customers and prospects grappling with the question of “build vs buy” when it comes to deploying a machine data analytics solution. It is no doubt intuitive for a product company to start thinking about building this as an in-house solution. Why? Because it is their products, log data, formats, and they know the best on what value they want to derive out of that. However, many of these home grown projects start with a big promise and deliver very little in the long term.
Working with our customers on analyzing their machine log data, we found various common usage patterns of the log data. First, the users in a support organization are troubleshooting and hence are most interested in searching, viewing logs and looking at specific sections or exploring various logs in a log bundle. This is a basic use case that requires correlating various logs in a bundle, whether they have a time stamp or not and allowing users to do full text and attribute based search on the logs( attributes are the output of parsed logs).