Hindsight Will Give Foresight When It Comes To Big Data

We must look to the history of big data to determine its future


Today the person on the street is a developing source of an enormous quantity of data every day - simply by going about their daily routine.

Be it the way they move, what they buy, where they shop or where they travel to, a whole wealth of knowledge on their behavior is available across a multitude of platforms.

For better or for worse, there is no denying that big data is only going to get increasingly bigger. But to what effect?

Open source data is becoming more and more prominent, with companies taking a ‘share and share alike’ approach to the way data is stored. With more vacancies for data-scientists roles than actual candidates to fill them, it is vital for companies to work together, utilising fresh eyes, alternative talents and new techniques to extract meaning from data sources.

There is endless speculation around the future of big data, from the development of new technologies and innovations, increasingly mobile societies and even a total digital revolution.

Nobody can truly guarantee the way the future will pan out, and with exponential amounts of data being generated by the second, it is becoming decidedly trickier for analysts and industry experts to accurately predict exactly what is next for big data. With these infinite streams of information, it is time for businesses and analysts alike to look back at historical data to be able to smartly interpret today’s statistics and harness their findings to foresee trends and patterns yet to come.

We’re all familiar with the idea of big data, perhaps whether we know it or not. Online shopping giants such as eBay and Amazon might send you an email on your birthday, but it’s data around your shopping habits, online searches, movement and sociological traits that they’re also privy to and utilize, whether you know it or not. Google searches lead to uncannily specific adverts in Facebook newsfeeds – a prime example of how businesses can utilise historical data to predict future purchases and tailor their offers and products for particular audiences.

Global streaming service, Spotify, has also joined in with its recent ‘Discover’ feature, which tailors a weekly playlist for subscribers, using an algorithm to generate a playlist based on their previous listening habits. Be it a similar artist, a song from a band’s early days or even just a popular cover, by using historical data, Spotify is positioned as an intuitive business that understands its users, whilst also increasing subscriptions to the feature by 40 million following the implementation of this strategy.

It doesn’t stop there; more and more businesses such as Netflix and Apple are pushing specific suggestions based on the habits of their customers. Companies are realizing that by appearing as thoughtful, as opposed to a ‘one size fits all’ we tend to trust their endorsements as we would a friend recommending a television series.

Hindsight is hardwired into human nature; learning from our past teaches us the lessons to use in our future. Trust between two or more parties is the basis on which our society continues to grow, but in a world where one is increasingly separated from the community by screens, using metadata to predict the wants of the consumer is the closest that some vendors can get to the ability to develop a personal relationship with their customers.

Whilst techniques for productive data-handling are evolving almost as quickly as the information itself is being generated, it is clear that businesses will benefit from simply looking back and analysing historical data, in a bid to move forwards and prepare for a stronger, more profitable and intrinsically intelligent future. 


Ashley Murdoch, CEO, Corethree

Ashley Murdoch is the CEO of Corethree, a company that has been evolving the m-ticket and native mobile solution marketing for over five years. Over one million people use its applications across bus, train, tram and cycle hire, with over 17 million m-tickets issued. 

University lecture small

Read next:

How Are Higher Education Institutions Using Analytics?