It’s well known that gaming poses the most demanding requirements on consumer computing. Graphic Processing Units have been on the forefront of innovation, driving more distributed computing and higher processor-memory bandwidth, all for drawing more polygons with finer textures at higher frame rates.
A similar role is played in the enterprise IT stack – servers, networking, storage, virtualization – by analytics. Instead of shooting aliens ever more realistically, companies want to get more accurate predictions for trouble in their supply chains, better know their customers, crunch through more realistic credit risk models, and more quickly optimize their next best offers.
All of this requires data; lots of data. Correspondingly, it requires architectures that can crunch through this data, and generate results and insights – NOW.
It’s not hard to be convinced that increasingly more companies want to process increasingly more data to drive better business decisions. But how you do it is the hard part. So at Battery, when we find companies that come up with relevant, unique and truly innovative approaches, we are happy to back them.
Generally speaking, we think about the evolving analytics market in generations.
- Gen 1: The first generation is dominated by big software stacks. Buying such a solution and implementing it is typically a multi-month process that involves costly in-house and external resources, and has a non-negligible failure rate. I’m sure many of us used such systems, usually sold by members of the MISO* soup gang, with their frustrating user interface and the long IT cycles required to adapt them to changing business needs.
- Gen 2: The second generation is more in-line with the consumerization trend of the enterprise. Unconsciously, the modern business user is trying to replicate the experience of browsing the app store, downloading his choice and getting up and running in a few minutes. The Departmental BI solutions of the likes of QlikView and Tableau are going a long way towards that goal, and are much better at one of the key metrics we view at Battery as a predictor of success: Mean Time To Pretty Chart. Still, once Data gets Big, these solutions hit a fairly abrupt limit, forcing companies to invest time and effort to reduce their data volumes and make sub-optimal business decisions.
- Gen 3: Which leads us to the third generation: analytics suites that are up and running in minutes and are not easily defeated by large data volumes. As is quoted of the late Steve Jobs, “You have to work hard to get your thinking clean to make it simple.” SiSense, a recent Battery investment, is this in action: a huge amount of innovation went into the ability to mash together different schemas into a coherent data source, requiring almost no user intervention or, God forbid, IT involvement. Moreover, the ability to seamlessly churn through 10s of Terabytes of data in close to real-time looks easy and natural to the end user. But under the hood, SiSense shows a deep understanding of the architecture of modern operating systems and the hardware chains from disk to RAM to CPU.
The modern enterprise is driven by analytics of Terabytes of data. The business user wants the solution here, today, tailored to her needs without resorting to that dreaded word: “Project.” The third generation of BI tools is the first to satisfy these seemingly contradictory requirements in a neat, simple, package.
At Battery, we’re happy to be on the train to transforming the modern IT stack. Have ideas in this space? We’d love to hear from you!
*The Big 4 analytics vendors, © of Bruno Aziza, VP Marketing of SiSense