Chapter 2 extends some of the arguments made in Chapter 1 in relation to data and the datalogical in order to argue that data are longstanding gender issues that intersect with class, geography and other social and economic markers. Data, algorithms and infrastructure are deeply and inherently gendered. This creates a number of algorithmic vulnerabilities, which can be clearly seen when we consider decision-making systems such as health, education or social services, which have positioned and understood humans as data for a long time. Drawing on empirical material with teenagers not in education, employment or training (NEET), this chapter argues that understanding how these teenagers are inherently positioned as data and as data bundles reveals the extent of the gendered algorithms at work here. The contemporary crisis over data-ownership is a latter intervention in what is actually a much longer and deeper-gendered datalogical system.