Computational aspects are typically ignored by statisticians In the introductory chapter of "Process Mining: Data Science in Action" (2016 - Van der Aalst, pag 11) the author says that :


  Although data science can be seen as a continuation of statistics, the
  majority of statisticians did not contribute much to recent progress
  in data science. Most statisticians focused on theoretical results
  rather than real-world analysis problems. The computational aspects,
  which are critical for larger data sets, are typically ignored by
  statisticians. The focus is on generative modeling rather than
  prediction and dealing with practical challenges related to data
  quality and size.


The bold phrase is not clear to me. In fact, since a generative model is the model that generates the data, once we obtain it we can do predictions. So, to me generative modeling and prediction are not opposing concepts. What do you think ?
I think what the author is speaking about is the time/memory complexity of algorithms that statisticians may don't care about. Make a model which is mathematically well proven may be more important to statistician eyes than making approximation to render a model feasible in real life.

I encourage you to look at the complexity of frequent mathematical operations. 

Often operation like Singular Value Decomposition, Matrix Inversion, Matrix transpose are used and their cost is way higher than the "upper bound scalable time complexity limit" $O(n.log(n))$ which prevent any utilization on massive datasets.

As an example, you can easily imagine than we are rapidly limited with time complexity because we are not able to increase computational power -- or wait -- more than approximately linearly with the increase of problem size. Take a very common $O(n^2)$ complexity, you can't afford to wait $1000000$ more longer -- or multiply your computational power by $1000000$ to keep same app duration -- when you multiply your dataset size by $1000$ (except if your operations are really fast...)