While "big data" can be a misunderstood buzzword in tech, there's no denying that the recent AI and machine learning push is dependent on the labeling and synthesis of huge amounts of training data. A new trend report by advisory firm Ovum predicts that the big data market--currently at $1.7 billion--will swell to $9.4 billion by 2020. So what do data insiders see happening in the coming year? TechRepublic spoke to several leaders in this field to find out. Here are five big data trends to watch in 2017, from the experts.

Getting ready to step into the self-driving Ford Fusion in Dearborn, MI. These technological advances, which are dependent on the machine learning branch of AI, rely on the data collected by car companies--data from real miles driven, such as with Tesla's Autopilot, data from simulations of autonomous driving, and data from test situations, such as Uber's driverless fleet in Pittsburgh. Big data, said Michael Cavaretta, director of analytics infrastructure at Ford Motor Company, means data that is "too big to easily handle within your computational resources." It's about looking at datasets with "high velocity, high volume and high variety," he said. And as computers have become more powerful and storage is cheaper, grappling with this data has become more difficult.