In large organizations, the sheer amount of workflows, applications, and data platforms can monopolize all of your development resources. Projects that are simple patch-stitch-release do not generate long term revenue. Building a data science pipeline on top of shaky consolidation efforts can wreak havoc on data governance. Latency, while moving data from one part of the pipeline to another, can impede even the best data science efforts.

Read more: https://towardsdatascience.com/how-to-build-a-culture-of-experimentation-for-effective-ai-ml-de843752dae9