This document discusses data architecture and management for data analytics. It begins by defining data architecture and explaining that it is composed of models, policies, and standards that govern how data is collected, stored, integrated, and used. Various factors influence data architecture design, including enterprise requirements, technology drivers, economics, business policies, and data processing needs. The document then outlines three levels of data architecture specification - the logical level, physical level, and implementation level. It also discusses primary and secondary sources of data, with primary sources including observation, surveys, and experiments, and secondary sources including internal sources like sales reports and accounting data as well as external sources.