The article “What Happens When Big Data Blunders” by Logan Kugler concentrates on the failure of Google researches using search trends to predict flu outbreaks, identifying these failures to be a result of both the inability of Google researchers to isolate what should be meaningful indicators of illness (searches about flu symptoms and remedies) from other trendy searches and the difficulty of adapting dynamic nature of Google’s search algorithms to assumptions about the search habits of the susceptible population. This article characterizes a common problem within social science research: statistical methods which once struggled to collect enough data are applicable now that digital resources faithfully aggregate copious amounts of information, but these methods often require stable sampling techniques which don’t align with the goals of the application or the consumer’s behavior. In a few words, messy data is as bad as no data. As Kugler notes, Google’s profit-driven, business goals don’t align with those of social science researchers and the data being collected is often skewed by the desire of the application (like Google search) to improve customer experience, rather than provide consistency. Finding clever ways to work with data with has been comprised in such a way, allowing social scientists to piggy-back their experimental data collection on modern applications, would provide ways for businesses to profit from selling consumer data and ways for social scientists to utilize the computational resources which have revolutionized so many other fields already.