IBM Puts Data To Work for AI and in the Cloud
Artificial Intelligence, deep learning and cognitive solutions are a big part of the future of IBM, as clients accelerate their digital transformations fueled by data. The company’s new multicloud data platform, IBM Cloud Private (ICP) for Data, is yet another step in that direction.
But this isn’t just another offering aimed at anybody with the word “data” in their job title.
ICP for Data is a fully unified platform layer built within a native cloud architecture that helps companies gather vast amounts of data regardless of where it lives, filter and prepare data to create a trusted analytics platform and then scale insights on demand by applying that data to machine learning and other AI projects.
On this cloud native platform, companies can containerize their data and tap into the convenience of microservices to unlock the value of the data.
The approach IBM took to building the offering mirrors another game changing IBM product: WebSphere Application Server, which emerged in the late 1990s as a unified suite of application and integration middleware for early days of internet-savvy e-business adopters.
It’s no coincidence that Scott Hebner, who led marketing and offering management for WebSphere during its inception, joined IBM Analytics in January as vice president of marketing and teamed with General Manager Rob Thomas to lead an analytics reinvention to create a platform to help clients get their data ready for AI projects.
In the following Q&A, Hebner speaks with Big Data and Analytics Hub about the bets he’s placing on the offering to evolve into the company’s first AI platform and emulate WebSphere’s success.
Big Data and Analytics Hub: How did ICP for Data come about?
Scott Hebner: It’s what clients have been asking for. There are clear client pain points that drove development and design of ICP for Data. They need to be able to collect their data, make it simple and accessible, plus organize it and build that trusted foundation off which to do their analytics. They need to be able to apply data science and business analytics against it and scale on demand if they want to help drive business process from their data. The challenge is that over the years, the source of data and the heterogeneity of the data has made it very difficult to do just that. What cloud represents is the opportunity to simplify the ability to not only get more value from data and unlock data and do it at lower cost, through self-service, but also help create a platform to roll up into the world of AI.
The most time consuming part of AI is not just building the AI models but getting data ready for AI. The last thing you want to do is train your AI models on bad data, because that is going to help shape your business processes. That drove us from a customer perspective to move to a unified platform, which is collaborative in nature: supports multiple teams, is agile, fast, simple and efficient to use.
BDAH: There was also the need to modernize the analytics portfolio, as you said.
SH: Right. We created a leadership portfolio over decades for data management and for analytics. We were recognized as such for it, but there are times when you need to modernize the portfolio as technology advances. We’re complementing our existing products that you buy individually by creating a platform of cloud microservices where basically you use services that you need in a highly agile fashion, provisioned in minutes as pre-assembled experiences from one unified data platform. This allows us to modernize based on the cloud-native computing foundation that can sustain new capabilities for the coming years.
BDAH: We’re so focused on AI now in the analytics unit. Why didn’t we put AI in the name?
SH: Because that is not the only value it delivers. You can be doing zero or little ML/AI and find a ton of value in this platform. A good analogy is the iPhone. Whereas 9 or 10 years ago, the primary use case was for making phone calls, now it’s reported that it is one of the least-used functionalities. I believe that IBM Cloud Private for Data will evolve to become a foundational platform for AI, and its primary use case will become AI in nature. That is, AI, in essence, will be the next phase of value in the world of data and analytics.
BDAH: I imagine you’re getting traction with regulation- sensitive clients that want to keep data securely behind the firewall.
SH: Exactly. Some of the early opportunities with customers and some of the use cases are actually starting to climb the ladder to AI, but they want to do it behind the firewall where the data resides and is most protected and manageable. They don’t want to load up trucks of data and send it into the public cloud. Since some of the data will likely be out in the public cloud, you need a platform to support a multicloud environment, which is what this does. Private, public, IBM, non-IBM, it’s all becoming hybrid. As time goes by, focus will intensify to unlock more and more value from dynamically changing data and build AI into business processes in a highly automated manner, and the less you will want to worry about on what kind of cloud it’s on. That’s why it is a tremendous opportunity.
BDAH: How is the Cloud Private for Data platform planning to capitalize on anticipated industry spending on cognitive and AI systems? Will we have industry-specific solutions for financial services, healthcare and manufacturing in the near future?
SH: We have the foundational platform. This gives you the ability to collect, organize and analyze your data on a unified platform in a team governed, collaborative model. We’re bringing cloud capabilities to where your data is, not requiring you to load up all your data and ship it around. We’re doing that in a way that makes it simple. This especially applies to managing your data, your compliance with your data and privacy of your data. All those things I listed off vary greatly by industry, and our extended capabilities can enable us to provide industry frameworks. We are obsessively focused on delivering value that is unique to the business environment a client operates within, which means industry-specific.
BDAH: While we’re the first out of the gate, how will we maintain our position?
SH: There’s a recognition as to where the market is going. We’re working to lead in addressing that market because we have a full implementation on a cloud-native platform. We have a foothold in the world of AI, everything from Watson Studio, Watson Catalog, Watson Machine Learning to SPSS, Cognos to the Data Science Experience. I think we have the building blocks in the data and analytics world to leapfrog and get ahead , especially when building upon IBM leadership in multicloud native architectures, that is, containerization and microservices.
BDAH: What’s been the response from Business Partners and clients?
SH: This is a pivot point to the future. The ramp up has been faster than anything I’ve ever been involved in. We have hundreds of client opportunities in just the first few weeks since being launched. We have a thriving ecosystem who are coming on board quickly. Because of our extensible platform, we anticipate to see a lot of added value extensions come on line as well as industry vertical plays as partners build things specific to healthcare and other industries.
Take advantage of over 100 ICP for Data events through the third quarter of 2018 to get more involved in the growing ecosystem.
Statements regarding IBM’s future direction and intent are subject to change or withdrawal without notice, and represent goals and objectives only.