23
Apr

CEO Blog: What Splunk’s IPO Tells Us

Everyone seems to love a good IPO story. I am sure right now nobody loves one more than the founders of Splunk. My sincere congratulations goes out to them. I suppose since they talk a lot about "Machine Data" and we have been focused on what we characterize as "Machine-Generated Data" for quite some time now (with good reason, by the way), we have been seeing interest from the press and others wanting to better understand what all the buzz is about. Not only is that an easy question, it's my favorite question. There is simply no doubt that as the world of Big Data continues to evolve, more and more people and organizations will understand that solving the problems of today is different than solving the problems of yesterday, and will be different still from those of tomorrow. As more and more unstructured data and machine-generated data increase as a percentage of the total data organizations need to collect, store, and analyze, the more they realize that purpose-built components service these needs better. The components in hardware reflects this as well. And just as these hardware systems consist of more and more specialized components working together synergistically, the software elements do the same. We see Infobright working alongside Hadoop, MongoDB, Citrusleaf, and certainly SQL Server, Sybase, Oracle, and MySQL, just to name a few. Our goal is to be fabulous at storing and analyzing machine-generated data and to easily and effectively co-exist with other "components". It works.

While I understand the casual observer asking us if we are like Splunk, we certainly understand the differences. To begin with, I have an extremely high regard for them. I also believe they, not unlike us, are benefitting from the huge proliferation of machine-generated data. But their model is entirely different from ours. They got where they are by having a tailored solution for IT Log Management, including the logic and user interfaces designed specifically for that use case. Infobright, on the other hand, is not a vertically-integrated, turnkey application. The organizations that buy us directly do so with the intention of layering on their own processing logic, not unlike someone buying Sybase IQ or HP Vertica, except in our case, the use case is generally one that requires storing and analyzing machine-generated data (and they generally also look harder at the Total Cost of Ownership!) And those organizations that buy us directly are only a part of our revenue. The characteristics of Infobright make us ideally suited as the embedded database for turnkey solutions (similar to Splunk) which store and analyze machine-generated data. These use cases range across a variety of markets ranging from Telco OSS systems like those provided by JDSU or Polystar to network security solutions from SonicWALL, soon to be a part of Dell. In fact, the range of use cases represented by Infobright customers across the spectrum of machine-generated data, from online and mobile analytics to telco to sensor data processing to financial services, online gaming, virtual reality, to what I believe may be the next new horizon, smart energy monitoring and delivery.

I have been asked in the past why we did not choose to copy the Splunk model over a year ago and abandon our approach in lieu of developing a turnkey solution for one specific market. That was a good question. It is hard to argue with Splunk's success. A casual read of their IPO filing documents suggest they see the imperative to branch into other verticals. But theirs is a truly different model. We don't really compete with them, although arguably some of our partners do, and some of our direct customers have developed their own IT log management solutions using Infobright in lieu of Splunk or other turnkey alternatives. But our path is our path. We are not trying to be Splunk, but we are clearly, beyond any shadow of a doubt, both benefitting from the vast increase of machine-generated data. And in that regard, we are very happy for them. They have a good product and a good team and we wish them the very best.

Regards,

Don

I happen to agree with you on many points. The thing that worries me is the big brother element that this could take on. It is one thing to position the information gathering and analysis as a benefit to the employee/company. However, does it become a new way to quantify and squeeze out every ounce from the existing workforce?

Good management teams have been able to get their teams to contribute above and beyond since time immemorial. I am not sure that data analysis is going to help transform bad management though. That is really where the difference will lie from my vantage point.

Kitchen Cabinets

Author: markbuffet
Date: 07/17/12

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