This paper presents seven tactics for managing the variability evident in some physical activity data. High levels of variability in daily step‐count data from pedometers or accelerometers can make typical visual inspection difficult. Therefore, the purpose of the current paper is to discuss several strategies that might facilitate the visual interpretation of highly variable data. The seven strategies discussed in this paper are phase mean and median lines, daily average per week, weekly cumulative, proportion of baseline, 7‐day moving average, change point detection, and confidence intervals. We apply each strategy to a data set and discuss the advantages and disadvantages.