Moving from Model-centric to Data-centric approach

Google researchers found that "data cascades -- compounding events causing negative, downstream effects from data issues -- triggered by conventional AI/ML practices that undervalue data quality… are pervasive (92% prevalence), invisible, delayed, but often avoidable." Lets discuss on the trend being followed widely for most or all of the AI use cases across the organizations. Just to make myself clear the term AI over here in being referred as an umbrella encompassing our DataScience/Machine Learning and Deep Learning Use cases. The Two basic components of all AI systems are Data and Model, both go hand in hand in producing desired results. We do realize that the AI community has been biased towards putting more effort in the model building One plausible reason is that AI industry closely follows academic research in AI.