In an enterprise market balancing multiple cloud environments and numerous edge points, artificial intelligence is the cornerstone to any competitive data strategy.

Jay Limburn (pictured, left), distinguished engineer and director of product offering management at IBM, and Julie Lockner (pictured, right), director of IBM data and AI portfolio operations and offering management, are working to simplify AI integrations for a legacy market still adjusting to cloud complexities.

Limburn and Lockner spoke with John Furrier (@furrier) and Stu Miniman (@stu), co-hosts of theCUBE, SiliconANGLE Media’s mobile livestreaming studio, during the IBM Think event in San Francisco. They discussed IBM’s revamped data analytics services and how its offerings enable AI accessibility at every level. (* Disclosure below.)

[Editor’s note: The following answers have been condensed for clarity.]

Furrier: Give us the update. What’s the focus?

Lockner: Before, we were called IBM Analytics, and that really is only a part of what we do. Now that we’re data plus AI, not only are we responsible for delivering data assets and technology that supports those data assets to our customers, but infusing AI in the technologies that we have [and] helping them build applications so they can fuse AI into their business processes.

Furrier: How do you present it to the marketplace? How are clients engaged with it?

Limburn: The ladder to AI is IBM’s view of how we explain our client’s journey. It starts at the bottom rung of the ladder, where we’ve got the collection of information. Once you’ve collected your data, you move up to the next rung … organize … where all the governance stuff comes in. [It’s] how we provide a view across that data, understand that data, provide trust to that data, and then serve that up to the consumers of that information so they can actually use that in AI. That’s where all the data science capabilities come in, allowing people to actually be able to consume that information.

Lockner: Our whole divisions are organized around these ladders to AI: collect, organize, analyze, infuse. On the organize side, it’s all about inventorying the data assets, then providing data quality rules, governance, compliance-type offerings, that allow organizations to not just know your data, trust your data, but then make it available. Being able to see that whole end-to-end lineage is a key point, critical point of the ladder to AI. Especially when you start to think about things like bias detection, which is a big part of the analyze layer.

Furrier: How are people getting there? What are some use cases?

Lockner: One of the big use cases is around taking information that might be real time, or batch data, and providing the ability to analyze that data very quickly to the point where you can predict when someone might potentially have a cardiac arrest. The ability to take data from a wearable device, combine it with data that’s sitting in an Amazon, MySQL database, be able to predict who is the most at-risk of having a potential cardiac arrest, and then present that to a call center of cardiologists. This company that we work with, iCure, took that entire stack — collect, organize, analyze, infuse — and built an application in a matter of six weeks.

Furrier: Now you have a new kind of data source going into the cloud … so, the ladder needs a secure piece. Talk about that.

Limburn: That falls into that organize piece of that ladder. If you’re going to make data available for self-service, you’ve got to make sure that data’s protected and that you’re not going to break any regulatory law around that data. We actually can use technology now to understand what that data is, whether it contains sensitive information, and expose that information out to those consumers, yet still mask the key elements that should be protected.

Furrier: How are you guys going to go after this new market?

Lockner: Our key strategy is embedding [IBM Cloud] Catalog everywhere and anywhere we can. We believe that having that open metadata exchange allows us to open up access to metadata across these applications. First, making sure that we can catalog and inventory data assets that might not be in the IBM Cloud.

The second step is taking all of our capabilities, making them microservices-enabled, delivering them through Docker containers, and making sure that they can port across whatever cloud deployment model our customers want to be able to execute on.

Limburn: It’s really about lowering the entry point to that technology. How can you allow individuals with lower levels of skills to actually get in and be productive and create something valuable? It shouldn’t be just a practice that’s held away for the hardcore developer anymore. One of the things we have in Watson Studio, our data science platform, is about providing wizards and walk-throughs to allow people to develop productive use models very easily, without needing hardcore coding skills.

Watch the complete video interview below, and be sure to check out more of SiliconANGLE’s and theCUBE’s coverage of the IBM Think event. (* Disclosure: IBM sponsored this segment of theCUBE. Neither IBM nor other sponsors have editorial control over content on theCUBE or SiliconANGLE.)

Photo: SiliconANGLE