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.
There’s no question that the AI boom depends on data labeling and analysis. “Machine learning has really come along,” said Carla Gentry, a data scientist in Louisville, KY. “2017 will be the year we see more expertise, but still it will struggle, with understanding, proper usage and talent.”
“IoT on the other hand, will surge with toys, car accessories, home and security uses but it will also set up nasty hackers with lots more access to our private lives,” Gentry said.
Monte Zweben, co-founder and CEO of Splice Machine, has a background in AI. “AI applications powered by machine learning depend on data to develop more predictive models,” said Zweben. “The more data, and, even more importantly, more data that represents the concepts you need to learn, makes AI applications better.
For example, said Zweben, “the more electronic medical records a system sees that reflect dangerous sepsis events in hospitals, the better a system can predict them before they happen.”
Big data, according to Tony Baer, principal analyst for information management at Ovum, “has emerged from its infancy to transition from buzzword to urgency for enterprises across all major sectors.”
“The growing pains are being abetted by machine learning, which will lower barriers to adoption of big data-enabled analytics and solutions,” said Baer, “and the growing dominance of the cloud, which will ease deployment hurdles.”
With advances in data processing and cloud applications, there is a plethora of free data platforms online that make organizing and synthesizing data easy—even for beginners.
“Every platform is becoming cloud-available,” said Zweben. “Even big data platforms like Splice Machine are available now as a self-service platform.