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The Three M’s: Monash, The Magic Quadrant, and Machine Generated Data

By Don DeLoach • March 20, 2013

When I was in high school I found myself quite attracted to a girl mainly because I found her incredibly smart and thus, to me, also incredibly interesting. I had a friend who found her attractive, yet was turned off by the “brainy thing.” I guess beauty, in whatever form, has always been in the eye of the beholder. My view is to embrace what you believe, yet welcome all interpretations without apology for that belief. Such is the discussion which has unfolded about Infobright between Curt Monash and the Gartner Group. At the center of this discussion is our focus on machine-generated data. Curt sees this as enabling our rise as a company, and the Gartner views it as limiting. My view? Yes.

Yes to Curt. Our focus fuels a momentum that is now clearly increasing. Our challenge is to keep up with our momentum without compromising the support of our customers. But this is a good problem to have. We have great momentum in ad-tech. We have great moment in financial services. We have great momentum in log management. And we have really great momentum with the companies whose applications service the mobile network operators. This all machine-generated data, and we are really good at it. We are also really focused on it. And to Curt’s point, it is driving our success.

Yes to Gartner Group. It is limiting. Because we focus here, we are not going to be a general-purpose data warehouse vendor and as such, we will not be displacing Exadata, Netezza, Vertica, Sybase IQ or other general-purpose solutions. Unless, of course, those general-purpose warehousing solutions were purchased, and are being used, for storing and analyzing machine-generated data. If that’s the case, then, well, perhaps we will. In fact, we are. And the reason is clear: when it’s machine-generated data, we have—as Gartner Group points out—a great combination of excellent performance and superior compression. This also results in an extremely low total cost of ownership—especially in comparison to general-purpose solutions. We get there by making assumptions about the data structure that gives us an advantage. And when that is the case, our customers gain that advantage. We are just fine with that. So the “limitation” is very real, but we embrace this “limitation.”

And as it turns out, the same underlining assumptions about the structure also make us a really great companion technology to Hadoop and other NoSQL environments, and well as for use in EPM. Here, too, we have many, many success stories.

And one last thing. Different people and groups debate the importance of machine-generated data and the degree to which M2M and the Internet of Things will impact our world. We fall into the true believer camp. We think the Internet of Things is not only going to happen, but it will truly be the next really big thing. A stroll through any of the eight buildings and 1,700 vendors at Mobile World Congress in Barcelona would re-enforce this view to anyone who roamed through the conference. And the underlying fuel of this trend is most definitely machine-generated data.

So Yes to Curt, and Yes to Gartner Group. The “brainy girl” might be appealing to some, and less to others, but at the end of the day, she is who she is. As are we.


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