Big data has become the oft bandied-about buzz word in the business world.
With the advent of digitalisation, companies have access to data like never before.
It is estimated that Walmart collects over 2.5 petabytes of data every hour from its customer transactions, while Netflix collects about 1.3 petabytes of data per day to help build the Netflix recommendation system.
To put things into context, a petabyte equates to about 20 million filing cabinets worth of text.
These examples illustrate the vast amount of data or trace that each of us leave behind in the digital world, allowing companies to learn more about us.
In 2006, only one technology company made it to the global top four largest market capitalisation list, with an estimated market capitalisation of US$0.3 trillion.
Fast forward to 2017 and technology companies – Apple, Alphabet, Microsoft and Amazon – occupy the top four spots with an estimated total market capitalisation of more than US$2 trillion.
The unparalleled rise of technology companies is not by accident; many of them have successfully harnessed big data to push the right product to the right customers, at the right place and time.
The success of the technology giants has prompted many companies to also jump onto the big data bandwagon and invest a significant amount of time and dedicated precious resources to it.
The hype around big data
Big data, as the name suggests, is fundamentally about the collection and analysis of large volumes of data to derive trends and patterns.
This data can reside within a company's own database or be drawn from the external universe of data sources including social networks.
A large part of the attraction of big data stems from the potential for its application in predictive analytics and modelling, as well as in artificial intelligence and machine learning.
Facebook, for example, successfully leveraged big data to match companies with users who are likely to be interested in their products and services.
This translated into a 49% year-on-year increase in its advertising revenues in 3Q17.
It is no wonder that many companies have turned to big data in the hope of making more informed decisions that will result in greater efficiency, superior products and services or deeper customer engagement.
But is big data really the silver bullet it is said to be?
And how many companies have fully grappled with the suitability and feasibility of applying big data in the conduct of their business?
Or in the words of Professor Dan Ariely Hebrew of Psychology and Behavioural Economics at Duke University, "Big data is like teenage sex: everyone talks about it, nobody really knows how to do it, everyone thinks everyone else is doing it, so everyone claims they are doing it".
Small data matters too
Size matters, for sure.
The use of big data will continue to gain impetus alongside increasing digitalisation and data proliferation.
But small data, which has taken a backseat, can and should play an equally important role in the decisions that businesses make.
Small data, as conventionally understood, refers literally to smaller volumes of data.
Because it is less vast and complex than big data (and indeed can be distilled or extracted from big data), it allows for better human comprehension. In other words, machines make sense of big data, people make sense of small data.
An added dimension to the meaning of small data was expressed by best-selling author Martin Lindstrom in his book "Small data: The tiny clues that uncover huge trends".
Small data, as he sees it, relates to the collection of bits of information from real world observations gleaned from human interaction – these are clues that lead to actionable and transformative insights.
Either way, the nub of the matter remains the same: It is about putting the human element front and centre.
This, I would argue, is most critical to the effective use (and interpretation) of data.
After all, any meaningful innovation that has come to past – think penicillin, the microwave, post-it notes or even potato chips – was the result of a person or persons who observed (a human need or behaviour), made use of data or information at hand, and applied intuition to connect the dots.
In today’s world, the adoption of small data can be strangely counter-intuitive.
Many may find comfort in “numbers” – large volumes of data in this case – to back up a business case.
The quantitative approach gives them a sense of security but should not be relied upon as the sole determining factor in decision making.
It is important to recognise that big data, made possible by technological advancement, is essentially a means rather than an end.
Applying big and small data in banking
In banking, likewise, there is a place for big and small data.
And in the latter, humans will feature prominently as both the source and the interpreter of such information.
Customers are increasingly performing banking transactions through online platforms and banks have been deploying big data, or data-driven analytics, to better engage customers through these channels.
At the same time, customers will continue to yearn for the human touch for relatively more significant, complex transactions.
It is these human interactions that will provide the observable small data that banks can also turn to for a deeper understanding of their customers.
And it is humans that will make meaningful sense of these insights.
Further, customers are looking for more personalised product offerings and tailored advisory services.
One benefit of customer segmentation using big data is that it allows banks to better target their clients with relatable marketing campaigns.
But banks need to look at small data too and use them to design and deliver personalised products to their customers and meet their needs uniquely.
For example, at OCBC Bank, a goal-based advisory approach was introduced, with the aim of providing personalised advisory centred around our customers’ goals, as we recognise that there is no one size that fits all.
While the bank continues to leverage data analytics and technology to identify market trends and the corresponding products that may be of interest to customers, the engagement with customers is one that is targeted and centred around the client’s financial goals, such as retirement and education planning, making it more relevant and meaningful for the customers.
This is achieved through conversations between the customer and the bank, allowing the bank to understand customers in ways beyond what the data says about them.
With an evolving environment, the focus should not be just about how big data can help banks make better decisions.
Perhaps, the focus should be on how technology, processes, skills and, most importantly, people can all be harnessed to connect the dots of big and small data to develop meaningful insights and ultimately, create value for stakeholders.
It is important to take a step back from time to time to reflect on the observations and see what is meaningful to the organisation, and to customers.
So, let's leverage data to deliver the stories, but let’s not let data be the only story.Disclaimer applicable to recommendation
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