Building a modern data and analytics architecture

Each time a promising new technology emerges, we seem to go through a period where it is proposed to be the solution to everything—until we reconcile how that technology fits into the bigger picture.  Such is the case with artificial intelligence (AI). Clearly the advancements in deep learning will create new classes of solutions but rather than being a standalone solution, we are just now beginning to see how it fits into our IT landscape. AI emerges at a time when several other shifts in analytics technology are occurring. Taken together, they paint a new picture of what a modern data and analytics architecture looks like. Over the next few years, we see the following trends aligning.

The end-to-end data pipeline

Early AI deployments were often point solutions meant to resolve a specific problem. Cameras might capture images from a manufacturing line which are fed through a deep neural net to identify potential quality issues. 

Now we are seeing more organizations who are integrating AI as one of many machine learning techniques in their analytics infrastructure. In fact, we see that IoT, Big Data, and AI are truly just different vantage points of an end-to-end data pipeline.  CONTINUE READING


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