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The growing volume and variety of data presents both opportunities and challenges for visual analytics. Addressing these challenges is needed for big data to provide valuable insights and novel solutions for business, security, social media, and healthcare. In the case of temporal event sequence analytics it is the number of events in the data and variety of temporal sequence patterns that challenges users of visual analytic tools. This paper describes 14 strategies for sharpening analytic focus that analysts can use to reduce the data volume and pattern variety. Four groups of strategies are proposed: (1) extraction strategies, (2) temporal folding, (3) pattern simplification strategies, and (4) iterative strategies. For each strategy we provide examples of use and of the impact of this strategy on volume and/or variety. Examples are selected from 18 case studies gathered from either our own work, the literature, or based on email interviews with application developers and analysts. Finally, we discuss how these strategies might be combined and opportunities for new technologies and user interfaces.