Navigating the challenges in creating complex data systems: a development philosophy

In this perspective, we argue that despite the democratization of powerful tools for data science and machine learning over the last decade, developing the code for a trustworthy and effective data science system (DSS) is getting harder. Perverse incentives and a lack of widespread software engineering (SE) skills are among many root causes we identify that naturally give rise to the current systemic crisis in reproducibility of DSSs. We analyze why SE and building large complex systems is, in general, hard. Based on these insights, we identify how SE addresses those difficulties and how we can apply and generalize SE methods to construct DSSs that are fit for purpose. We advocate two key development philosophies, namely that one should incrementally grow - not biphasically plan and build - DSSs, and one should always employ two types of feedback loops during development: one which tests the code's correctness and another that evaluates the code's efficacy. Machine learning is in a reproducibility crisis [Hai+20; the code produces identical results - for replicability and Pin+21; Bak16]. We argue that a primary driver is poor code general reproducibility using independent implementations, quality, having two root causes: poor incentives to produce correctness is crucial.