David Menninger's Analyst Perspectives

I recently spent time at Strata+Hadoop World 2016 in New York. I attended this event and its predecessor, Hadoop World, off and on for the past six years. This one in New York had a different feel from previous events including the most recent event in San Jose at the end of March. Perhaps because of its location in one of the financial and commercial hubs of the world, the event had much more of a business orientation. But it’s not just location. Past events have been held in New York also, and I see the business focus as a sign of the Hadoop market maturing.

Our research shows that big data can have significant business benefits. In our Big Data and Analytics benchmark research, more than three-quarters (78%) of participants indicated that predictive analytics is
 the most important area of big data  analytics for their organization. In our Predictive Analytics research almost three out of five (57%) organizations said they have achieved a competitive advantage through their application of advanced analytics. Thus we are moving beyond the early adopter phase of the technology adoption life cycle into the early majority. More and more organizations recognize that big data and advanced analytics can provide a competitive advantage. As a result, we see more focus on the business value of it, not just the technology required to pursue this advantage.

At the Strata+Hadoop World keynote presentations many vendors chose to bring their customers on stage or share stories about how their customers are positively impacting their organizations with big data technology. There were also plenty of technical training sessions, including two full days of training prior to the keynotes and expo, but the main stage of the event was focused on what you can do with big data rather than how to do it. The attendees also seemed to bring a business focus to the event. I spoke with multiple vendors in the expo hall who had attended both the Strata+Hadoop event in San Jose earlier this year and the New York event. They all described customer interactions that had more of a business focus than at previous events. People came looking for ways to apply big data technology to real business needs.

This is not say there wasn’t plenty of technology at the event including in particular data science, streaming data and data preparation and governance. Tutorials were offered on a variety of data science topics including how to implement machine learning in programming languages such as Python and Spark. Our research shows that Python is one of the most popular languages for data science analyses, in use by more than one-third (36%) of organizations. As I have written previously, Spark is growing in popularity as a way of providing big data, machine learning and real-time capabilities. At least half a dozen vendors ranging from large to small participated in the expo, touting their data science capabilities, and many other vendors’ marketing materials described how they support data science, for instance with data preparation tools that enable the data science process.

Processing streaming data in real time was also a frequent theme. Part of what makes big data big is that it is being generated constantly. It follows that you can probably get value out of analyzing that data in real time as it is being generated. In our research real-time analytics is the second-most frequently cited (by 54%) area of big data analytics, after predictive analytics. In its original incarnation, Hadoop was designed as a batch-oriented system, but as it has grown in popularity, much attention has been given to adding real-time capabilities to the Hadoop ecosystem, which I have described.

The themes of data preparation and governance come as no surprise. Our Big Data Integration benchmark research shows that reviewing data for quality and consistency issues (52%) and preparing data (46%) are cited as the two most time-consuming aspects of the big data integration process. Similarly our big data analytics research shows that data quality and information management is the second-most common barrier to big data analytics, cited by 39 percent of organizations. Vendors and the big data community are on the right track in addressing these issues.

The big data community continues to evolve, and the Strata+Hadoop World events are helping to foster dialog, education and growth. I’d say that this most recent event is evidence that the big data community is “growing up,” meaning that the focus has shifted to delivering business value. Strata+Hadoop World is a place where you can learn not only about the technology of big data but also how to solve business problems.

Regards,

David Menninger

SVP & Research Director

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