VIDEO: Why You Need a Hybrid Data Management Strategy

Video produced by Steve Nathans-Kelly

At Data Summit 2018, Chris Reuter, North America data warehousing sales leader, IBM, presented a keynote focused on the major trends in IT today and what organizations must do to advance their organizations.

“Why do you need a data management strategy?” asked Reuter.  How are you going to deal with exponential growth of data? How are you going to infuse AI or cognitive capabilities into all of your data and provide access to the citizen data scientists or users who don't necessarily have those skills to bring those capabilities to results today? And, also, how are you going to decide on the right place for your data? Is it the cloud, is in on premise—and if it's on premise, is it Hadoop or just simple distributed? Where should your data live?

The answer, said Reuter is that you need an open, not a closed, platform as your backbone. “Think about this as the hub and then you can use that open platform as your hub and you can use spokes out just to some closed technologies that you're interested in—maybe TensorFlow, for example; maybe Spark ML, for example.”

The strategy should not be about one thing or another, emphasized Reuter. “It's not about cloud or on premise. Likely, your strategy shouldn't be either. Again, when we talk about cloud or on premise, I'm really, really surprised when someone says to me, ‘The corporate edict is to be 100% on cloud.’ Well, have you thought through all of your data? Where's the right place for your data to live? Is it all on the cloud? It might not be, right? It's not about just traditional, relational, or open source.”

The key idea to consider is access, said Reuter:  providing access for all different types of data, all kinds of different types of analytics. It is critical to provide access to not just the big guys, not just the power users, but to democratize the access to the citizen data scientist and the citizen analyst as well, said Reuter. “These roles are growing at a much faster rate than traditional data scientists. It's about flexibility and scalability. That's key and that's why you should be thinking about the cloud. But it's not all about the cloud. You need to think about where the right home for your data is. Then, finally, it's about infusing what we call AI machine learning, cognitive computing, bring it to your data.”

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Data Summit 2019, presented by DBTA and Big Data Quarterly, is scheduled for May 21-22, 2019, at the Hyatt Regency Boston, with pre-conference workshops on May 20.