  A pervasive problem in Data Science is that the knowledge generated by
possibly expensive analytics processes is subject to decay over time, as the
data used to compute it drifts, the algorithms used in the processes are
improved, and the external knowledge embodied by reference datasets used in the
computation evolves. Deciding when such knowledge outcomes should be refreshed,
following a sequence of data change events, requires problem-specific functions
to quantify their value and its decay over time, as well as models for
estimating the cost of their re-computation. What makes this problem
challenging is the ambition to develop a decision support system for informing
data analytics re-computation decisions over time, that is both generic and
customisable. With the help of a case study from genomics, in this vision paper
we offer an initial formalisation of this problem, highlight research
challenges, and outline a possible approach based on the collection and
analysis of metadata from a history of past computations.
