Since 2009, big data has progressed from being an awkwardly named though highly touted super-category to being an awkwardly named and over-hyped bandwagon category. During this time Splunk has quietly gone about building a leadership position in a few gritty but mission-critical sub-categories such as log search, security information and event management (SIEM), and operational analytics. Splunk’s machine-based indexing, search, and analytics engine also qualifies it to play in another emerging super-category – the internet of things (IOT). With around $420m in revenues and growing at 50% or so every year, Splunk has got everyone’s attention despite being much smaller than many much larger big-data pretenders such as IBM. How has the company achieved this, what new challenges does it face going forward, and what every other player in big data learn from Splunk’s progress thus far?
Philip Lay spent two decades as an account executive, general manager and entrepreneur, before becoming a strategy advisor and managing director with The Chasm Group in 1995.
Today Philip is visiting professor at IESE business school in Barcelona and serves on a public-company board alongside his advisory activities with boards, CEOs and management teams.
Major clients include Autodesk, Compuware, HP, NetApp, Rackspace, SAP, and Salesforce.com.