Dream. Dare. Do – that is Suyati’s work principle in a nutshell.
Big Data entered the virtual world as a collective memory for technology. Between the year 1986 and 2007, the digital storage amplified by 23% annually. Every footprint in the matrix of machines which are fuelled by and give out data is gathered and recorded. Once such recording occurs, the data can be analysed to gain insights into the working of different sectors. Thus primarily, this gives self-knowledge to each enterprise and with such knowledge comes the power of prediction. Industries and service providers can catch scent of the pattern of customer experience and conversion rates tracking the trends which would garner appeal and revenue later.
The conversion of undifferentiated data into sector-specific (more importantly, purpose-specific) information and insight is the domain of data analytics. Thus, while the question of whether a particular sector should dip its feet into the waters of Big Data is still being debated, Big Data is the elephant in the room- it cannot be ignored. When Professor Gary King of Weatherhead University remarked “There is a big data revolution”, he essentially meant that the body of technology which evolved its brain of the internet has now formulated its limbic system (part of the brain responsible for various functions of memory) through Big Data.
The most recent camp to join the debate on Big Data is the fate of payment sector. How important is Big Data for the payment sector? With the world wide web becoming home to transactions, the payment sector is undergoing its most critical transition- the revolution of being driven by data.
Apart from fulfilling the basic function of paying online, the payment sector has undergone a series of growth spurts – the emergence of eWallets, mobile payments, eCash exchange and others. Currently, Big Data is sporadically utilized in the financial sector. It is an integral part of the security dimension in the payment pipeline. However, the delivery of data is so huge that its processing (after which it can be used for other purposes as well) requires way more data scientists than are currently available. The top-level financial organizations, keeping in mind the unpredictability in the sector, have to create a system where the copious data can be digested to give out meaningful financial insights.
Once such an intelligible system of handling Big Data within the financial sector is set up, it will be possible to drive payment anytime, anywhere. Time gaps in transactions would disappear: fund transfer between banks, confirmation from companies on clients’ online requests, sending of eCash across platforms would become an instantaneous event. At such a moment, the financial sector would have overcome the two primary confinements of space and time it has previously faced. Individuals and communities (companies, intra-company teams, work groups) can communicate financially with one another from across the world and actualize their agendas instantaneously.
However, with such a leap would rise the need for stronger security walls that can combat a multitude of payment risks and fraud. Only those enterprises which can effectively deal with such security challenges would be able to survive in the long run, thus becoming the evolved species of the financial sector.
The rapidly changing face of financial sectors as technology produces transcendental versions of Big Data tools leads to natural selection of financial firms. Such selection is based on how the firms adapt and answer to rising financial trends. This implies rigorous competition between the pre-existent enterprises and most significantly, the start-ups.
Apart from having to select efficiently between a range of payment methods, they also have to figure out a way of soaking value into the process of payment. To extract value from the spectrum of data, firms would need highly developed machines. Such machines would be able to maintain a database of all kinds and quantities of information encompassing the tiniest of transaction detail. The strongest prediction in the financial sector is regarding to role of Big Data for credit unions. As mobile payments become the primary way of interacting with companies for services and products, credit unions would need out-of-box Data Analytics solutions.
The overarching question mark on importance of Big Data for financial sector is in the scenario where the market is consolidated coherently, from top to bottom. In the case that firms do not come together through acquisitions and mergers, the market will confront more service options. Today, there are thousands of offers on the market. It is almost impossible to differentiate whether new or well-established firms offer it. How could such a challenge be resolved? Either the personal financial landscape may undergo improvisation or there may be effective aggregation of rewards which would stimulate the purchasing pipeline for users.