If you caught the IBM announcement on January 9 on the formation of the IBM Watson Group you may have seen mention of IBM Watson Explorer. If you are familiar with IBM InfoSphere Data Explorer (the product that IBM added to its portfolio with the acquisition of Vivisimo, Inc., in May 2012) this description of Watson Explorer from the January 9 announcement probably sounds familiar.:
IBM Watson Explorer is designed to help users across the enterprise uncover and share data-driven insights more easily, while helping organizations launch big data initiatives more quickly. Watson Explorer provides users with a unified view displaying all of their data-driven information, as well as a framework for developing information-rich applications that deliver a comprehensive, contextually-relevant view of any topic for business users, data scientists and a variety of targeted business functions.
To net this out more directly: the InfoSphere Data Explorer product has been moved from the IBM Software Group to the newly-formed Watson Group, and is being re-named IBM Watson Explorer. The product retains all of the features and capabilities that have made it an important part of IBM’s big data portfolio—most notably, connecting users with data from many different sources, or silos, and helping them to make sense of it.
For example, since joining the IBM software portfolio in June 2012, InfoSphere Data Explorer has been a key element in the five key use cases that IBM has identified to help organizations understand what problems big data can address. In fact, InfoSphere Data Explorer has been foundational in two them: big data exploration and enhanced 360-degree view of the customer. It’s important to recognize that the role of Watson Explorer in these important use cases and the overall IBM big data portfolio will not change. For the big data exploration use case, Watson Explorer continues to provide the connectivity, search and exploration capabilities that organizations need to understand their data, make decisions about how to leverage it in big data initiatives, and leverage it in ongoing activities. And for the enhanced 360-degree view of the customer, Watson Explorer continues to provide not only the underlying access and connectivity layer to bring together customer data from disparate sources, but also the user interface framework that delivers data and analytics on the glass in the 360-degree view for front-line staff.
Over the next six weeks you’ll see a transition on our website and other media from InfoSphere Data Explorer to IBM Watson Explorer. Keep in mind that all the capabilities that have delivered business value to organizations in virtually every industry continue to be delivered under the new name.
So what’s next?
What’s exciting about the move to the Watson Group is that it puts Watson Explorer squarely in the field of cognitive computing, and part of an overall trend identified by Tom Eid of Gartner in a recent report. As Eid writes, “Smart computing capabilities, analytic technologies and a plethora of new information and content are disrupting the status quo and driving expansion into multiple innovative methods of adoption.”
Cognitive computing systems are designed to learn and interact naturally with people to extend what either humans or machines could do on their own. In essence they imitate some human capabilities such as interpreting language, creating hypotheses and learning. As currently conceived, they are designed to do a lot of the heavy lifting that humans would otherwise undertake to leverage the vast amount of data and knowledge we generate.
Watson Explorer represents just one element in a bigger picture represented by Watson Foundations, which was announced in the roll-out of the IBM Watson Group on January 9, 2014. At its core, the definition of Watson Foundations, incorporating a broad portfolio of information management and analytics capabilities, makes clear the need to manage, secure and fully leverage all data as part of a journey toward cognitive computing.
In a future blog post I’ll delve more deeply into the role of discovery and exploration in supporting cognitive computing. But it should be easy to see the parallel between the big data exploration use case, which addresses the need to explore all of an organization’s data as a precursor to launching a big data project, and similar need to establish broad access and exploration capability as a step in the journey to cognitive computing.
What do you think will be the role of search and discovery in the context of cognitive computing? Does the notion that big data is all data apply to cognitive computing as well?