How better data management impacts business innovation

Successful retailers are data-driven retailers. The task of predicting consumer behavior, with an eye toward understanding individual customers, involves working with dozens of variables and data that is often incomplete, ambiguous, and organized for transaction processing and not analytics. The promise of data science, artificial intelligence (AI) and machine learning is for these techniques to help spot trends and patterns with sufficient speed and accuracy for retailers to be able to adjust floor-sets, advertising, discounting, and product mix as quickly as possible. But just as data scientists are becoming more commonplace, antagonism is growing between expectations about how quickly algorithms and advanced mathematical techniques can transform retailers, and the reality that data science is, well, science. Just as with the physical sciences, there are many dead ends and failures in data science.