What is big data?

Imagine, you are browsing through the Internet as usual for, perhaps, finding your next vacation stop, such as Langkawi, and as you log into your Facebook or Instagram the next day, you see numbers of advertisements pop up in your notification, to your surprise, offering cool traveling packages to Langkawi Island!

Amazing huh?

That’s only a part of the magnificent feats pulled out using Big data.

So what big data really is?

Literally, Big data are extremely large data sets, containing both structured and unstructured data. Unstructured data comes from what traditionally known as database: customers name, age, address, contacts, gender and even the record number of customer purchases. Nothing much we can derive from the unstructured data, but on the other hand, structured data is a variety of data that can be collected from the interactions between human and computer (such as web browsing log and social networks). What you buy online or via mobile payments, what you read or watch online every day, and even where you move using Uber or Grab, all these data will be automatically collected by the computer so that it processes the data and interpret your patterns of interest and preference, and hence your behavior. The process of examining those large sets of data is called Big Data Analytics. In contrary to what “big” data sounds like, its impact is not necessarily dependence on the mere size, rather, most of the time, it is about how effectively business is able to organize, sort, clean and mung data for its full capacity (of course, the more the merrier too!)

Why do we need to know about Big data?

Undeniably, Big data has assisted businesses and enterprises as it analyses the customers’ behavior more effectively and faster than conventional ways, which enhances the whole level of customer experience and efficiency in business administration. Businesses using big data has striven in their respective market, and are likely to continue to edge over those without the support of Big data. Thus, guess what, ignorance is not a bliss! (not to mention even if you are a mere consumer, you are likely to be benefited too with the knowledge) In this article, we will particularly focus our scope on the usage of Big data on banking industry, given that it is the industry that is likely to make the greatest impact using Big data towards both the consumers and the entrepreneurs as a money lender and investor.

Big data and banking industry

In the midst of the 4th Industrial Revolution, financial institutions and banks are nevertheless the ones pioneering in using Big data. Naturally, banks have tons of data, be it regarding their clients’ personal data or transaction history, it is a big jewel to the data analysis, which the banks uses such information for several purposes. By monitoring the client’s transaction and spending behavior from time to time, it allows them to have an in-depth understanding of the clients’ needs and hence it can promote the most fitting financial packages to each segmented groups of customers. Besides, by tracking customers’ credit card, loan limits, and etc, banks ensure that they resort appropriately at times so that the chance of customers overdrafting is low.

Investment through Big Data

One of the most prominent roles of big data in financial institutions is indeed on leveraging the investment decision in stock market.

Using the Big data from shares price and its dividends, the performance of the company could be speculated. According to Osman Ali (2016), Portfolio Manager of Goldman Sachs Asset Management, he stated that the company is dedicated in creating data-driven investment models that can objectively evaluate public companies globally through fundamentally-based and economically motivated investment themes.The models use large sets of analysed historical data of companies such as public financial statements and market data like prices, returns, volumes, etc. In addition to that, companies are also evaluated based on investment themes such as momentum, value, profitability and sentiment through applications of Big data analysis as shown below:

THE QUANTITATIVE INVESTMENT STRATEGIES APPROACH TO IDENTIFYING INVESTMENT OPPORTUNITIES

To know more details, please click here!

Future of big data for banks and financial institutions in Malaysia

The benefits brought by the effective investment through the application of Big data analytics are swiftly spanning across the banking industry, therefore, confidently, the future of big data in banking industry will be phenomenal.

According to the NewStraitsTimes (2017), Ambank Group Bhd is said to be in the midst of incorporating the usage of Big Data analytics into its operations, aiming to strengthen its customer satisfaction and conduct better risk management through a more accurate identification of potential non-performing SMEs (Small Medium Enterprises) that apply for loan.

In terms of customer satisfaction, at today’s standard, meeting customers’ demand is cumbersome and is extremely hard to distinguish. However, with the deep insights provided by Big Data into customer spending habits and patterns, it simplifies the whole tonnes of tasks in ascertaining the specialised needs and wants of individuals. Thus, all banks will soon be able to effectively segmentise customers and meet the satisfactions productively in the guide of Big data.

Moreover, the future of Big data in banks also lies on the ability and possibility to conduct a more comprehensive and yet exponentially faster loaning process to individuals and businesses. The conventional way of lending, as we observed in Malaysia, is still primarily on hasty paperworks which tend to consume enormous amounts of time and effort from both the businesses or individuals and banks. Not to mention, this process also incurs the extra charges for face-to-face discussions with loan officers and the extra miles and times spent for in-and-out visits to the bank. But, with Big data, the future of such mundane processes are less appalling. Besides assessing the credit score of borrowers to determine how risky lending money to this person is, now using Big data, together with machine learning, enables the bank to statistically derive multifaceted analysis of such likelihood through channels as trivial as it may be seen in infancy, such as the time of the day the borrowers ask for a loan, the amount of email he/she sends out in a day, the numbers of Facebook friends and etc (Delgado, 2016). Consolidating all such trivial matters together, it becomes meaningful in speculating a pattern used to be undetectable by humans, and renders a valuable decision factor in the context of loan acquiring legitimacy- and such can only be derived through big data!

Big data also plays an important role in Bank Negara Malaysia (central bank) and in a greater extent, the whole nation at a macroeconomic level. In an interview with Ben Wicks, the Head of Research Innovation in Schroders- a British multinational asset management company, he stated that the company has achieved a remarkable achievement in the application of Big data analytics where their groups of specialists managed to analyse a big pool of data and conclude that, the assumption of the deterioration of consumers’ confidence and expenditures in certain parts of UK as a result of the Brexit referendum is (surprisingly) invalid. This is achieved by analysing the expenditure patterns from hundreds of thousands of recorded consumer data. Thus, it is not far-fetched to say that there is a possible future when Bank Negara will be able to analyse and “predict” current and upcoming economic cycles at near perfect precision, be it amidst the influence of certain economic events or not. Let’s hypothetically say,  Malaysia rages an economic tug of war against Singapore (for whatever reasons). Bank Negara can analyse the degree of influence by the event towards the general optimism of Malaysians towards the economic certainty and from there it can initiate appropriate policies to do check and balances to the economy to minimise any form of deterioration due to such event. Of course, such hypothesis can only be assumed given that there is the certainty of Malaysia adopting data analytics at modern standards in the future, which ironically, many challenges are still faced by organisations at policy level (which means Malaysians are still not yet readied for Big data and its applications!)

(Read more on how a country’s central bank adopts policies to keep the economy running in FLY’s article on Central Bank)

Potential problems and threats

Internally, for banks to implement big data analytics, there will be setbacks too. First of all, lack of expertise would require them to construct a new department to handle and run the big data analytics or else they would need to seek solutions through partnership with third parties specializing in big data. Both options would cost the bank a huge amount of opportunity cost to implement it. Furthermore, most banks are not equipped with the infrastructure that is capable of handling constant influx and traffic of data. Therefore, banks will need to upgrade their infrastructures with computer hardwares that are likely to take away significant amount of their property space due to its rather bulky size. Plus, acquiring such supercomputer to organize and store tons of information that the banks hold is going to cost them a lot. Last but not least, despite all the enormous benefits of big data analytics we mentioned, there is also a huge resistance, coming from users and executives, in implementing such system, mainly because of the two main concerns regarding the big data, which are threat to privacy and employment. Even though there is existing law to protect the consumers’ privacy, but the huge wave of data collected by banks and financial institutions has made them more vulnerable than ever in the aspect of privacy, especially such information is often being shared among other subsidiary organisations. Quoting from Surya Suharman, Corporate Communications of Sedania Innovator Bhd, who argues that no breach of customer privacy in using big data analytics as it only uses data that is provided with the knowledge and approval of the customer. However, we all know, most of the time, the customers (we) are not aware of the level of being tracked by these organisations which includes things as extensive as our activities, preferences, interests and behavior. To make the situation worse, there is huge grey area in law worldwide to regulate the usage of customer information through big data analytics. While we are exciting on embarking the journey of big data, it is widely accepted that technology revolution like it will disrupt the labour market by eliminating low qualified jobs. We can see in previous years how banks in Malaysia like Maybank closed some of the branches to cut the operation cost and to fully utilize online banking, it will somehow affect the economy.

Big data and other industries

Once we consider the importance of informative data in driving rational business decisions, as well as the very nature of big data itself in terms of its complexity, it is of no surprise that Big data analytics can bring significant advantage to any business that adopts it better than any of its competitors in the same industry. On a logical standpoint, any company regardless of sectors and nature will not be exempt from the benefits of big data as long as the company requires information from data sets to initiate and power its business decisions. The universality of BDA usage in industries is of notable prevalence, perpetuated by the seemingly wide-range of companies involved in acquiring data to gain a unparalleled competitive edge. These companies include Amazon, American Express, General Electric, Netflix, Starbucks, and etc, where the motive of such technological acquisition is to explore the advertising algorithms, customer relations and behaviour, sensor data from machineries, consumption patterns and many more . In Malaysia, manufacturing companies, such as Top Glove tap into the functions of Big data analysis by executing a pilot programme which utilises sensors to monitor the mixture in the production of rubber, thus automating what was previously a manually-driven process (Mahmood, 2017). In the ride hailing industry, Grab, together with Malaysia Digital Economy Corporation Sdn.Bhd (MDEC) and World Bank Group launched the OpenTraffic initiative which provides traffic data from Grab’s GPS data streams to address traffic congestion and improve road safety in major Malaysian cities (Grab, 2017). This initiative allows access to an open dataset among Malaysia’s traffic management agencies and city planners to better manage traffic flow and make investment decisions on local transport infrastructure. This can only be accomplished through the analysis of traffic congestion peak patterns and travel times, which are valuable data. Though there are many more applications of Big data analysis yet exposed and explored by the public, we are still assured that big data will be the “next big thing” encircling the world!

 

References

Cameron, N. (2016). How iflix used consumer intent data to gain 1 million subscribers in six months. [online] CMO FROM IDG. Available at: https://www.cmo.com.au/article/611097/how-iflix-used-consumer-intent-data-gain-1-million-subscribers-six-months/

 

Chong, J. (2017). MDEC to spearhead bigger push for big data adoption in Malaysia. [online] Digital News Asia: Your Eye on the Tech Ecosystem. Available at: https://www.digitalnewsasia.com/digital-economy/mdec-spearhead-bigger-push-big-data-adoption-malaysia

 

Delgado, R. (2016). How Big Data Will Transform the Lending Industry. [online] Data Informed : Big Data and Analytics in the Enterprise. Available at: http://data-informed.com/how-big-data-will-transform-the-lending-industry/

 

Grab. (2018). Grab and MDEC together with the World Bank Group Launch OpenTraffic Platform in Malaysia to Combat Local Traffic Woes. [online] Available at: https://www.grab.com/my/press/business/grab-mdec-together-world-bank-group-launch-opentraffic-platform-malaysia-combat-local-traffic-woes/

 

O’Neill, E. (2016). 10 companies that are using big data. [online] ICAS The professional body of CAs. Available at: https://www.icas.com/ca-today-news/10-companies-using-big-data

 

Osman, A., Suwabe, T. and Walsh, D. (2016). The Role of Big Data in Investing.

 

Rosli, L. (2017). Banking on big data analytics. [online] New Straits Times. Available at: https://www.nst.com.my/business/2017/10/294117/banking-big-data-analytics

 

Schroders (2017). How ‘big data’ can improve investment decisions – three examples.Available at: http://www.schroders.com/en/hk/wealth-management/insights/strategy-and-economics/how-big-data-can-improve-investment-decisions–three-examples/

 

Arthur, L. (2013). What Is Big Data?. [online] Forbes- CMO Network. Available at: https://www.forbes.com/sites/lisaarthur/2013/08/15/what-is-big-data/#4bfe0dae5c85

 

Big Data Made Simple. (2016). The role of big data in the banking industry. [online] Available at: http://bigdata-madesimple.com/role-big-data-banking-industry/

 

Buttler, P. (2017). 10 CHALLENGES TO BIG DATA SECURITY AND PRIVACY. [online] Dataconomy. Available at: http://dataconomy.com/2017/07/10-challenges-big-data-security-privacy/