What is the reference architecture for creating an analytics shop in today?s environment? This presentation will review how businesses are mapping out their next generation analytics architecture based on various decision layers within the Hadoop ecosystem. The architecture also takes into consideration how data engineering, data science, decision science, and decision support play a role in this architecture. This framework recommends how modern analytical departments structure their technology thinking, as well as locate the gaps. This presentation will provide attendees with a better understanding of how to map out their next generation analytics architecture within the Hadoop ecosystem: -Review standard examples witnessed in the industry, compare industry standard vs. reference architecture. Highlight areas where companies are capable, as well as under-represented areas. – Explore why the decision science layer is one of the most omitted areas in production analytics departments. -Review the importance of scale. Predictive methods are required to scale analytics into the operation. – Explain impact of intelligent systems on the technology selection, physics for data in flight vs. data at rest . – Constructing model management systems that are implemented into production. Discuss possible solutions of this perplexing problems.