  Mobile phone data -- with file sizes scaling into terabytes -- easily
overwhelm the computational capacity available to some researchers. Moreover,
for ethical reasons, data access is often granted only to particular subsets,
restricting analyses to cover single days, weeks, or geographical areas.
Consequently, it is frequently impossible to set a particular analysis or event
in its context and know how typical it is, compared to other days, weeks or
months. This is important for academic referees questioning research on mobile
phone data and for the analysts in deciding how to sample, how much data to
process, and which events are anomalous. All these issues require an
understanding of variability in Big Data to answer the question of how average
is average? This paper provides a method, using a large mobile phone dataset,
to answer these basic but necessary questions. We show that file size is a
robust proxy for the activity level of phone users by profiling the temporal
variability of the data at an hourly, daily and monthly level. We then apply
time-series analysis to isolate temporal periodicity. Finally, we discuss
confidence limits to anomalous events in the data. We recommend an analytical
approach to mobile phone data selection which suggests that ideally data should
be sampled across days, across working weeks, and across the year, to obtain a
representative average. However, where this is impossible, the temporal
variability is such that specific weekdays' data can provide a fair picture of
other days in their general structure.
