If all variables of a system was known, would stochastic modelling be redundant? Statistical parametric methods such as regression can be used to predict a particular dependent variable using a pre-defined set of covariates. For example, a linear regression model used to predict merchandise sales.
If all covariates that cause true value of the continuous dependent variable to change was known, more specifically if an event could be deterministically modelled, then would using parametric modelling techniques to represent such event be unnecessary since a deterministic model would always yield an accurate prediction for any observation?
Thus, in predictive modelling, should more time be devoted in collating and gathering as much relevant features to increase the accuracy of a model rather than trying to optimise a model based on a given pre-defined list of independent random variables?

Not necessarily.
I will even do you one better: If all variables in a system and all causal functions relating them, including error processes (if any), were fully known and parameterized predictive modeling might still be required.
Knowing direct effects does not in and of itself give you the integration and accumulation of indirect effect over time. Even an purely deterministic equations can provide challenges to prediction if there is any uncertainty about initial conditions or even minute error, such as precision or representation. This is a general insight known in many branches of science, from population ecology (May, 1973; Levins, 1974), to physics (Schultz, 1994; see also the three-body problem) and chaos theory (Sanderson, 2021).



References
Levins, R. (1974). The Qualitative Analysis of Partially Specified Systems. Annals of the New York Academy of Sciences, 231, 123–138.
May, R. M. (1973). Qualitative Stability in Model Ecosystems. Ecology, 54(3), 638–641.
Sanderson, G. (2021). Newton’s Fractal (which Newton knew nothing about) [YouTube video]. 3Blue1Brown. Run time: 26:05
Schulz, H. C., & Hilgenfeldt, S. (1994). Experimente zum Chaos [algorithmic translation to English]. Spektrum der Wissenschaft, 1, 72-81. [A nice interactive simulation of the systems described in this article]
