The organizers at the recent Hadoop Summit had Geoffery Moore author of “Crossing the Chasm” as a keynote speaker presumably to explain away the fact that there were relatively few enterprises speaking about their Hadoop projects. The enterprise adoption of Hadoop is not as visible as hoped, compared to the lightening rod adoption of Hadoop by high trafficked Web sites. Moore did a fine job and of course talked about the chasm effect with the early majority of the market patiently sitting by and watching what the early adopters are doing. Everyone that we speak with who is in a position of working with enterprise Hadoop users says that there is a tremendous amount of enterprise interest and activity even though there are only a handful of enterprises that are talking about their Hadoop plans in public. But one does have to wonder how relevant Chasmism is in today’s technology world. Twenty years ago technology adoption by consumers and businesses took time — lots of time. In 1994/1995, it took 18 months for the first million DirecTV units to be sold. But today it takes only hours to pass the million mark — the iPhone 4S reached a million in less than 24 hours after its release in October. It’s hard to believe that only five years have passed since the introduction of the iPhone which has been a tremendous catalyst for changing the way we use technology. Twenty years ago the chasm was created by people resisting change. Today everyone pays attention to new technology developments and enthusiastically experiments with new devices and services. Everyone is an early adopter racing to be the first on their block to sport a shiny new device. The chasm has been replaced by the land grab — how many users can you sign up in the shortest period of time. For consumer related products — how fast can you get to a million users? For businesses focused products the land grab is — how many and how fast can you sign up the Fortune 500? How does this relate to Hadoop? Those groups that are waiting for Hadoop to cross the chasm may be wasting valuable time and instead should examine Splunk’s rapid rise signing 4,000 Big Data customers without waiting for chasms to be crossed. It may be that the initial use cases for enterprise Big Data are happening faster for things like what Splunk does (Google type search query sitting in front of a massive amount of machine generated data), than for other types of data.