Analytics and Data Summit 2020

What AI requires are disambiguated, highly relevant, and specific, integrated data, flowing to the point of need. What it often gets are single-batch sets, cleaned up to be just good enough, often done manually. Data Scientists are said to spend 80% of their time with clean-up tasks. Often, we have to work with too abstract, disconnected data models that can't keep pace with market dynamics. Semantically enriched data serves as a basis for better data quality and provides more options for feature extraction. IT infrastructure that moves data to the algorithms and applications is failing. Data centric designs do away with these bottlenecks. Semantic AI is the combination of methods derived from symbolic AI and statistical AI. It is not only focused on process automation, but also on intelligence augmentation. We'll outline the path towards self-optimizing machines by demonstrating the interoperability of Knowledge Graph and ML algorithms for better performance and scale.