Any company wrangling large volumes of diverse data will realize quickly they need machine learning models. But turning data into valuable, reliable intelligence is a challenge that has confronted many industries over the last decade as the world fell head over heels for big data. During the last few years, we have learned that big data isn't anywhere near as valuable as intelligent data. This is because companies need data they can rely on to use instantly in decision making. Intelligent data combines both deep data science capacity and expertise.

It took our data science team led by Dr Xuxu Wang almost a year to develop the machine learning and deep learning models required," Mr Brown told …

Big data has emerged as a lucrative by-product of the digital revolution, enabled by vast banks of information collated from the likes of smartphones, sensor networks, and cloud-based apps and databases. Deriving meaningful insights from this data can help cities figure out the real-time flow of people and traffic, for example, or enable life insurance providers to establish more accurate mortality rates. Extracting meaning from big data through analytics is estimated to be a $200 billion industry today, according to recent IDC numbers. It's against this backdrop that PredictHQ has come to fruition, offering a purpose-built data-aggregation platform that takes information from myriad sources related to events (public holidays, concerts, festivals, etc), meshes it with more "hard to find" data, adds a little machine learning to the mix, and sells it to third-party companies via an application programming interface (API). PredictHQ emerged from stealth last year with $10 million in funding and a host of big-name clients.