Financial institutions have a natural desire to predict the volume, volatility, value or other parameters of financial instruments or their derivatives, to manage positions and mitigate risk more effectively. They also have a rich set of business problems (and correspondingly large datasets) to which it’s practical to apply machine learning techniques.

As enterprises amass terabytes of complex data, they need tools to house and make better sense of their information. This is why we’ve built BigQuery, to help data analysts deal with large datasets. But not all of us are data wizards. Many of us use spreadsheets to perform ad-hoc analysis.

Since announcing our new interface back in July, our goal has been to make it easier for BigQuery users and their teams to uncover insights and share them with teammates and colleagues. Whether you’re a veteran or brand new to BigQuery, we wanted to highlight some of the major improvements we’ve made to the interface in the past five months. Some of this functionality was previously available in the classic UI, while other elements are totally new. Let’s take a closer look.