Streaming Data

The speed at which data is getting generated is not going to slow down. This is not news anymore. How do you reduce the distance in time between an event happening and an action being taken to respond to it? How complex are these decisions to enable meaningful and effective responses? How can I dynamically refine these decisions based on what I uncover from my machine learning investments? After all, what good is learning if you don’t use it?

Today’s Processing must be fault tolerant and continuously adapt to changing data flows. 

In use cases ranging from IoT to ecommerce, an ongoing challenge for stream-processing applications is to extract important insights from real-time systems as fast as possible and then generate effective feedback that optimizes operations or avoids costly failures.

There is a growing need to make big data more accessible and intuitive for enterprises. By applying artificial intelligence to power smart conversations about data, chatbots will transform the way companies rely on intelligent conversations to make decisions. Having a personal analytics assistant makes it as simple as having a conversation to get all the powerful answers you need for success.