The unreasonable importance of data preparation

Edit note: We know data preparation requires a ton of work and thought. In this provocative article, Hugo Bowne-Anderson provides a formal rationale for why that work matters, why data preparation is particularly important for reanalyzing data, and why you should stay focused on the question you hope to answer. Along the way, Hugo introduces how tools and automation can help augment analysts and better enable real-time models. In a world focused on buzzword-driven models and algorithms, you'd be forgiven for forgetting about the unreasonable importance of data preparation and quality: your models are only as good as the data you feed them. This is the garbage in, garbage out principle: flawed data going in leads to flawed results, algorithms, and business decisions. If a self-driving car's decision-making algorithm is trained on data of traffic collected during the day, you wouldn't put it on the roads at night.