Think about the past and the future of data analysis
Everyone is a data analyst, whether they know it or not and whether they like it or not.
Training for data analysis is essential, but there is limited bandwith.
We can automate or teach. None of them is perfect.
Tukey JW, The Future of Data Analysis: If data analysis is to be well done, much of it must be a matter of judgement and theory, whether statistical or non-statistical, will have to guide and not command.
Theory comes to rescue. It provides a scalable understanding of what's good and what's not. Like in music.
Today, there is still a lot of discussion about the "instruments" rather than the "music" that is being produced.
We have a good idea a posteriori but not a priori.
Basically, you need to be provided with everything – both software and data – to be able to reproduce the results. You also need to be quite knowledgable in the field to understand what's going on.
Examples of aesthetics we could apply to data analysis:
Reproducible: analytic code, software packages, VCS, data formatting, metadata, documentation, distribution