You know the scenario: You get to work in the morning and quickly check your personal email. Over on the side, you notice that your spam folder has a couple of items in it, so you look inside. You’re amazed — although some of them look like genuine emails, they’re not; these cleverly disguised ads are all correctly labeled as spam. What you’re seeing is natural language processing (NLP) in action. In this instance, the email service provider is using what’s known as predictive analytics to assess language data and determine which combinations of words are likely spam, filtering your email accordingly.
With the volume of data being created, collected, and stored increasing by the hour, the days of making decisions based solely on intuition are numbered. Companies collect data on their customers, nonprofits collect data on their donors, apps collect data on their users, all with the goal of finding opportunities to improve their products and services. More and more, decision-making is becoming data driven. People use information to understand what’s happening in the world around them and try to predict what will happen in the future. For this, we turn to predictive analytics.
Predictive analytics is the concept of using current information to forecast what will happen next time. This area of study covers a broad range of concepts and skills — oftentimes involving modeling techniques — that help turn data into insights and insights into action. These ideas are already in practice in industries like eCommerce, direct marketing, cybersecurity, financial services, and more. It’s likely that you’ve come across implementations of predictive analytics and modeling in your daily life and not even realized it.