Data is a strategic asset that has made an indelible impact on the way we do business, so much so that cost-per-click, average revenue per user, and terabytes of data have all become routine turns of phrase in even the smallest organisations.

These days, customer acquisition and retention largely depends on businesses anticipating their customers’ needs so well that they can win their hearts and minds with ease – and data analytics allows this to take place at the deepest level. The whole process is so ubiquitous, that you probably don’t even notice it any more. We no longer need to browse millions of movies to get the kind of entertainment we like; our Netflix preferences of the past can suggest the movies we should watch in the future. When we book holidays online, the booking engine helpfully leverages our destination and purchase options to predict our tastes in future, and market to them accordingly.

Data, AI and machine learning: a powerful combination

The International Data Corporation (IDC) reports that data is compounding by 60 per cent each year, and predicts it will grow from 33 zetabytes in 2018 to a staggering 175 zetabytes in 2025. But, data on its own has limited use. For data to live up to its reputation as a powerful asset and efficiency generator, we need to apply Artificial Intelligence (AI) and Machine Learning (ML) techniques to expose the patterns, connections and insights that lie beneath the surface.

At CoreLogic, AI & ML are intrinsic to our operational excellence. Every day, we value over nine million residential properties – a process which consumes 100 billion decision points encompassing the number of bedrooms and bathrooms, land area, neighbour identity, geographic density, proximity factors as well as recent sales transactions. All of this can be completed in two and a half hours. Similarly, our best of breed automated valuation model (AVM) uses ML to estimate the value of every residential property across Australia in 4 hours. These valuation solutions serve over 10,000 customers including government, banking and finance and research as well as informing millions of consumers each year about the value of their largest asset. Without the aid of ML and AI, these tasks would take an army of humans years to complete.

An efficiency exchange: for better or for worse?

But it’s not just big business that is harnessing the super powers of data harvesting. Smart speakers, such as Google Home and Amazon Alexa are making their way into the modern household. These devices, powered by some of the biggest leaders in machine learning, are coupled with home automation hubs that can enable almost every electrical appliance to be controlled through voice. Amazon’s Alexa now has a user base of over 31 million users alone with a combined total of over 133 million users worldwide. These devices start to bridge the divide between our virtual worlds and our physical footprint. But it comes at a cost…

For example, China’s “sharp eyes” program collects surveillance data to inform its budding social credit system. By 2020 the system will render a score for each of its 1.4 billion citizens, based on their observed behaviour, and this will be used to grant or deny entry to education institutions, access to financial institutions, service in restaurants and so forth.

Data can be seductive because it delivers ease: we bounce around sites and apps having our first order needs met seamlessly. But, at the same time, we are knowingly exchanging our privacy for efficiencies. So how do we strike the balance?

The answer lies in demanding high standards in terms of privacy, security, use, and longevity of the data we provide. Whether we like it or not, data is here to stay. Having stringent regulation will build much needed stakeholder trust and simultaneously allow organisations that can successfully capture, analyse and commercialise data to flourish.