Turning data into actionable insights with machine learning

An actionable insight is a process that is derived from raw data analysis. For example, business leaders could track a customer’s behavior on a platform that can indicate their sentiment regarding a product. These data points are fed into an analytics platform, from which users can derive conclusions.

Business leaders might notice that customers aren’t as keen on a product feature that was popular in the past and that they’re asking for a new feature or enhancement. Social media listening strategies also help to gather raw data and turn them into actionable insights.

Every industry could use actionable insights. “Actionable insights arising from analytics and AI are no longer a luxury, but a necessity for achieving competitiveness,” said Eitan Sofer, head of developer platform at Sisense, an analytics provider.

Information These days, analytics platforms rely on AI and machine learning (ML) algorithms to drive actionable insights. For example, a financial company can use an ML algorithm to quickly assess a loan applicant’s credit-worthiness and provide a human agent with a recommendation to approve or disapprove the loan.

While the human agent still has discretion over the process, the algorithm makes their job easier and more efficient.