Data analysis is the most challenging step in mixed methods research, especially if it is done integratively. Data integration refers to the merging of different data elements to produce a result that is bigger than the sum of its parts. This chapter outlines challenges in analyzing data integratively. I focus on data linkage, transformation, and consolidation as integrative strategies, and I illustrate them with an empirical example. Using this foundation, I problematize the qualitative-quantitative dichotomy and call for more creative and flexible - yet reflected - thinking around integration that goes beyond terminological boundaries to advance methodological as well as substantive research.