ABSTRACT
This chapter discusses the technology infrastructure that has emerged to support augmented intelligence. It provides an overview of the type of machine learning techniques that are being applied to this well-managed data. The chapter aims to understand the underlying hardware systems and platforms needed to put the data to use for augmented intelligence. It seeks to divide the foundations of big data into three types: structured, unstructured, and semi-structured data. Structured data refers to information that has a defined length and format—for example, numbers, dates, and names. A majority of traditional business applications are designed to manage highly structured data. Machine learning models are well suited to the structure and tagging of structured data. Turning unstructured data into meaningful information can be much more complex than dealing with the highly structured databases. The imperative for finding meaning in unstructured data is to understand the hidden patterns within that data.
