Four principles for machine learning

BOSTON: Many businesses are failing to extract actionable insights from the huge volumes of data they track due to the systemic failure of their approach to machine learning, a leading academic has argued. "If companies want to get value from their data, they need to focus on accelerating human understanding of data, scaling the number of modelling questions they can ask of that data in a short amount of time, and assessing their implications," according to Kalyan Veeramachaneni, Principal Research Scientist in the Laboratory for Information and Decision Systems at MIT. He made this assertion in the Harvard Business Review where he observed that machine-learning experts and their business counterparts were frequently speaking different languages and had different expectations. So, for example, when the former complain that "the data is a mess", this often turns out to refer less to its quality than its granularity. "Machine learning experts are used to working with data that's already been aggregated into useful variables," he noted, "such as the number of website visits by a user, rather than a table of every action the user has ever taken on the site."