Why IBM Analytics adopted big-bet thinking: an interview with Rob
It’s no secret that IBM can help companies transform data analytics, build a better cloud and ladder up to AI. The IBM approach to data analytics stands incorporates three core principles: making data simple and accessible; building a trusted analytics foundation; and scaling insights on demand.
Rob Thomas, general manager of IBM Analytics, discusses these principles that guide the IBM Analytics business and have led to its latest major offering: IBM Cloud Private for Data, a platform for high-performance analytics that powers cloud-based applications so companies can be ready for AI.
What are your goals for IBM Analytics now that you’ve worked to align the Cloud and Watson business units? It feels like 2018 is the year IBM Analytics is having its coming out party.
We started talking to clients at start of the year around the AI ladder, which is about the steps clients should take to get to an AI future. This is the best way to sum up our strategy in analytics, which provides the building blocks clients need to be ready for AI, so they can take advantage of AI both now and at-scale in the future.
Watson has delivered some tremendous use cases that are domain-specific. But when you move beyond to general-purpose AI, it requires more of your data to be organized correctly and easy to analyze. That’s what we’re focused on the rest of 2018 and for the foreseeable future.
What are your long-term bets over the next five to 10 years?
If you think broader on that time scale, we’ll be in a world of pervasive AI. One thing we’ll plan to enable is putting AI and machine learning inside most products, applications and experiences.
We’re also thinking about SQL. In the future, the world will be a potential data source for SQL.You can capture data —whether it’s from IoT, a server, or any type of PC or smartphone. The idea of “SQL the world” is something I see that will take hold.
And “data as a utility” is a third. Our belief is that it should be easy to virtualize and provision all data instantly.
A fourth bet is the convergence between DevOps and data science whereby organizations can automate any task they want on demand.
What are the essential principles guiding IBM Analytics?
How do our essential principles set the stage for our long-term bets?
Our core mission is make data simple and accessible. The belief is that everybody in every company should have access to all the data they need on a moment’s notice to make more informed decisions. That’s ultimately why we exist and we give you the key steps to help you get there and AI. If you have pervasive AI, you realize data is simple and accessible.
Let’s talk about IBM Cloud Private for Data (ICP for Data). How do you see it helping our customers prepare for AI?
It goes back to our belief in the power of containers. Everything we did with ICP for Data was built on containers. Containers and microservices are easy to deploy and you can more easily achieve value. The assumption with ICP for Data was that clients will modernize their data architectures. We’re essentially bringing the public cloud to data.
We’ll bring the public cloud to your data, because that’s where you’re most comfortable having your data today. As you’re ready to move to the public cloud, we’ll make that easy because we’ll be on the same architecture and you haven’t sacrificed anything for the future.
ICP for Data has all the components and building blocks you need for AI. We’re not talking about 12-36-month consulting projects here. The containers are designed to install in hours. Clients can then get their first experience within a day.
