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Machine Learning and Data Science are Top Trends at Strata Data


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Data professionals and vendors converged at Strata Data in New York to trade tips and tricks for handling big data. Top of mind for most was the impact of machine learning and how it’s continuing to evolve as the “next big thing.”

Zaloni’s Scott Gidley, vice president of product marketing, said that the continuing trend at Strata from 2016 to 2017 is machine learning and artificial intelligence and how to automate and optimize them.

“What can be done without human intervention is a big theme,” Gidley said.

Kelly Schupp, vice president of marketing, agreed with that sentiment and added her own two cents on how machine learning is getting attention.

“What’s interesting about machine learning is, when using it as keyword in press releases I get a lot of attention. That indicates to me that it’s on a lot of people’s minds right now,” Schupp said.

MemSQL’s Gary Orenstein, chief marketing officer, said it’s all about how to make machine learning operational.

“It’s one thing if you can do machine learning on yesterday’s data, it’s another thing if you can enable your live applications to be able to do machine learning where the feedback loop is continuous and current,” Orenstein said.

Data Science was the second hot topic at the conference with Kelly Stirman, vice president of strategy at Dremio, saying that companies are concerned about making copies of data and protecting those copies.

“Most companies have five to 15 copies of data and in some industries you are required to destroy data,” Stirman said. “When you have sensitive data about customers there are additional regulatory requirements around the management and securing of that info.”

It seems like this year, unlike previous years, people are coming in saying they are buying a data science platform, explained Peter Wang, cofounder and CTO SVP at Anaconda.

“[Customers] don’t ask what we are, they’re ready to buy,” Wang said. “Enterprises understand that they need something to bridge between the exploratory and agile world of data science. This is the first few rain drops before more data science stuff moves mainstream into an enterprise context.”

Collaborative data science platforms are certainly hot, Kenneth Sanford, analytics architect at Dataiku, said.

“We are, product wise, mature in this space. We’ve got a strong story and value proposition around what the value of collaboration is because we let very different personas touch the software we think we’re unique in that space,” Sanford said.

Other trends at the conference include the growing popularity of data cataloguing, in memory databases, cloud, security, containers, and GPU.


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