This document summarizes a seminar on secondary analysis, big data, and open data. It defines big data as large, complex datasets difficult to process traditionally due to high volume, variety, and velocity of data. While big data enables understanding global issues and increasing transparency, it also raises privacy and surveillance concerns requiring sociological perspective. Open data is data freely available to use and share, as defined by openness, reuse, and universal participation without discrimination. Examples of open data mapping projects and sentiment analysis are provided.