Splunk’s revenue continues to grow, but so too do operating losses. All in all, though, the Big Data analytics vendor had a very solid quarter, adding more than 400 new customers and continuing its land-and-expand strategy.
Specifically, Splunk reported $66.9 million in revenue for its fiscal Q2 2013 (ended July 31, 2013), up 50% over the same quarter last year. License revenue increased 43% year-over-year, topping $43 million, and the company upped its full year guidance to between $275 and $281 million in total revenue. Operating losses, however, grew to nearly 20% of total revenue, hitting $13.3 million.
While investors obviously prefer hot companies in growth mode to narrow losses year-over-year, Splunk leadership correctly understands the extent of the Big Data market opportunity and continues to invest heavily in people and product development. Wikibon forecasts the Big Data market to reach nearly $50 billion by 2017. In order to compete with IBM and others to gain its share of the Big Data pie, Splunk needs to move quickly to lock-in new customers and expand existing deployments.
To do that, Splunk continues executing its strategy to be both a Big Data analytics platform and application company. On the platform front, in Q2 Splunk released three new software developer kits to GA, giving developers new tools to build analytics applications on top of Splunk’s machine data indexing platform. The three new SDKs – for C#, PHP and Rub – join three existing SDKs for Java, JavaScript, and Python.
On the application side, Splunk in Q2 released its application for operational monitoring of VMware virtual environments and announced a new product called HUNK for analyzing and visualizing data in Hadoop.
Action Item: As more business processes become automated, it is critical that administrators have as clear a view into operational performance as possible to ensure both optimal performance and to predict/respond to problems. As the NASDAQ flash-freeze illustrated, highly-automated business processes that rely on software rather than people can and will fail if not proactively monitored. Enterprise CIOs should consider applications that provide this visibility into machine-generated data to mitigate such risks.
Footnotes:
