Why So Many Data Science Projects Fail to Deliver

Organizations can gain more business value from advanced analytics by recognizing and overcoming five common obstacles. Through in-depth research, the authors identify these mistakes and suggest corresponding solutions. Mistake #1: The Hammer in Search of a Nail. Companies are infatuated with data science; however, analytical solutions work best when they are developed and applied in a way that is sensitive to the business context, not regardless of context. Mistake #2: Unrecognized Sources of Bias. You can avoid unrecognized bias by creating project teams composed of data scientists and business professionals, whereby they identify potential predictor variables and their data sources, then scrutinize each for potential biases. This article includes three more nuggets of advice and concludes that what is needed is a higher degree of coordination between data scientists and those responsible for problem diagnostics, process administration, and solution implementation. In other words, a dose of reality and context make for great data science.