The iPhone redefined how we interacted with phones. Uber reimagined the way we use taxis. Airbnb transformed how we booked our hotels. Each one of them has one thing in common: they gave us a new way to experience the world around us.
With the launch of Watson Data Platform, IBM is giving every enterprise the ability to fast-track their journey of redefining the experience for their customers.
Let’s take a look at the modern day enterprise. They are at a cross-road. They want to give their own customers a new experience: a more personal experience or shall we say: a more human experience. As they explore this path to redefine the way their customers interact with their world, enterprises often come to the conclusion that they must gain better, and more timely insights from the data that they are collecting. By putting the data to work, they can give their applications cognitive capabilities and reimagine the way their customers experience their world.
While the technology behind these cognitive applications had existed for a long-time, it has never seen as much success as in recent days. The cognitive capabilities in these applications need a stack that requires horizontal scalability in both storage and compute, little to no workload affinity to compute resources, distributed processing capabilities, and often require very large compute and storage resources. Such stacks were simply not available in data centers across various enterprises or required too large of an investment with no guarantees on returns.
This reality has led IBM (and other Cloud providers) to invest heavily in providing scalable cognitive services (Machine Learning as a Service: services like Tone Analyzer, Visual Recognition, Natural Language Classifier, and, soon to be launched, Watson Machine Learning). By utilizing these services in the cloud, application developers (and large enterprises) can move extremely fast and bring out new, differentiated and more human experiences for their customers at lightning speed without necessarily investing heavily in building out the data centers required to run the machine learning algorithms at acceptable speed. Enterprises can now simply focus on delivering the differentiated experience that their customers are waiting for without worrying about the underlying infrastructure and/or resource and the dreaded capacity planning that comes along with that.
This brings us to our point. Cloud and cognitive were meant to be together: the rise of cognitive apps continues to derive growth of cloud workloads, and similarly cloud-based services continue to prove themselves to be a key-enabler of cognitive apps. The challenges presented in a cloud environment (such as security) can be addressed and dealt with (in fact, have already been addressed by most public cloud providers). Additioally, the cloud experience can also be brought to the data center. The stack that was once not feasible is now readily available and can be recreated in a private data center if needed (using hybrid deployments)
As enterprises launch their cognitive journey, it is critical that the decision makers evaluate the cloud providers for services that provide machine learning capabilities at the PaaS layer and how well are those services supported in a hybrid deployment (Private + Public). This will be a key differentiator between cloud providers as we move into the cognitive era. Choosing the right provider will be the key to enabling continuous innovation that will enable the modern day enterprise to redefine their customer experiences.
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