The Strata Data Conference is changing and it’s changing in a good way. At the recent Strata Data Conference in New York, Mike Olson, chief strategy officer at Cloudera, which co-sponsored the event, commented that at prior events we used to talk about the “Hadoop zoo animals,” meaning the various components of the Hadoop ecosystem of which I have written previously. Following last fall’s Strata event, I observed that the conference was evolving to focus on the use of data. Advancing that evolution, this year’s event focused on a particular type of usage: artificial intelligence (AI) and machine learning. The evolution from a focus on zoo animals to a focus on business value using advanced analytics shows further maturation of the big data market.
The alignment of big data with advanced analytics makes sense. There is no other way to uncover and act on all the information contained in big data sources. Visualization, reports and dashboards simply cannot indicate all relationships contained in a data set. There’s too much information. Our Big Data Analytics benchmark research confirms that organizations increasingly recognize this. Participating organizations reported that advanced and predictive analytics are the most important type of analytics in their big data analytics efforts. The Strata Data Conference organizers appear to recognize the importance of advanced analytics as well.
Nearly every keynote was about AI or machine learning. That’s not to say other aspects of big data were completely absent. The session catalog included topics on streaming data, big data in the cloud, big data security, data engineering, data lakes and other technical or architectural topics. Exhibitors were not solely focused on AI either, running the gamut across these same market segments. There was also a Business Summit track designed for executives, business leaders, and strategists. But overall, the buzz throughout the conference was AI and machine learning. AI and machine learning are the new black.
Machine learning and AI require very large amounts of data and therefore big data storage and management technologies. Our research shows big data is being stored in relational databases less often. Hadoop and other big data storage technologies do not appear to be going away despite the fact that they are vanishing from conference titles and vendors’ marketing materials. In our Dynamics Insights: Data Lakes research we are currently exploring, among other things, the relationship between data lakes and data warehouses, with the latter often based on relational technology. If you complete this survey you will be provided an assessment of how your data lake efforts compare to those of others. We are also currently conducting Dynamic Insights research into the state of machine learning deployments in organizations’ current analytic processes.
The trend evident from the recent Strata Data Conferences is clear. Organizations have largely overcome the technical hurdles of collecting and managing large amounts of data. That doesn’t mean collecting and managing data is trivial, but it’s certainly achievable. Now organizations are turning their sights toward AI and machine learning to derive from big data value and insights that were not accessible before. There will certainly be challenges, as we have highlighted in our predictive analytics research, but the research also shows that those organizations that embrace AI and machine learning will benefit from it. I encourage your organization to consider how it can apply AI and machine learning to your business processes so you can achieve these benefits too.
Regards,
David Menninger
SVP & Research Director
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