BFSI sector can redefine services with big data
The banking and financial organizations are struggling to manage the huge volume of datasets which often becomes a challenge in their business. In such a scenario, organizations must find smarter data management approaches that enable them to effectively manage and optimize their data. In an exclusive interaction with CXOtoday, Ramakrishna V S Chinta, Sr. Vice President, Global Head - Data & Analytics, Polaris Financial Technology Limited explains how banks can leverage data to generate insights and derive value from it. Excerpts.
What are the key challenges of CIOs in the BFSI sector with regard to managing the huge data growth?
In the BFSI sector, enterprises often face multiple challenges in managing the massive data that is generated daily. The primary challenge is to handle, store and manage the data growth within the existing infrastructure. For example, some banks use mainframes with custom code to capture data, which may not have the capability to handle the huge volume of data generated today. Secondly, the current data management practice including data quality, storage and its access, replication etc., often turns out to be a challenge for banks. Thirdly, a number of banks find it challenging to meet the business need for the data and its attributes. For example, banks must consider real-time data which again the existing systems may not be able to support.The fourth challenge lies in the utilization of new technologies and its integration with the existing system, investing in new tools and acceptance by the user base. Finally, in many cases banks often do not understand the real potential of the data generated. Today banks run many projects that address specific needs of a few user groups, with each project running as a silo without the benefit of the larger context. Therefore, the dilemma is where to start and how to start managing data growth, storage and analysis.
So, what is it that financial companies need to do to keep pace with this massive data growth?
The existing data collection systems in banks are old and need upgrades, including replacement with new systems where necessary, to be able to handle new types of data. These new types of data are both structured and unstructured data, what we often refer to as big data that not only includes textual data but social media posts, digital pictures and videos, purchase transaction records, and cell phone GPS signals to name a few - that the traditional transaction systems cannot handle. For example, banks used to view data volumes of a few 10s of GB as huge volumes until recently. Today, with growth, expansion and mergers, data that is already available and its speed of growth is pushing this limit to 100s and 1000s of GBs, or PBs (Peta Bytes). This suggests that, not only should banks strive to keep pace with data capture, but also develop systems that can process and provide data for insights or for other requirements like compliance, on the same day. In such situations, Big Data is the answer. This however does not mean one can simply replace existing data warehousing systems with Big Data.
Can you explain in which areas banks can leverage data to generate insights and derive value from it?
Banks can use data for monetization: Today a few banks are trying to create monetization benefits from their data. Banks have every piece of information about their customer as they capture a lot of it as part of the Know Your Customer (KYC) process. For many transactions to be processed, submission of a lot of data is required as mandatory for either regulatory purposes, or for dependence of other services being used. This is another important source of data.
Business Insights: Collation and analysis of this data for insights leading to business benefits from either cross-selling or up-selling enable the bank to increase its business with existing customers cost effectively. Such insights are also necessary for not just for increasing the services foot print for additional revenue but also for regulatory and risk management purposes.
Risk Management: While regulatory requirements are mandatory like Basel III, risk management teams also need better insights to address the risk of the bank from different sources like market risk, credit risk, customer risk etc. Today, newer tools and techniques help achieve this more effectively.
Data Mining: Earlier data analysis was done on a few samples of data with the likelihood of errors due to either the sample size and distribution or because of the method used. With Big data, the sampling method is irrelevant. We can run algorithms on the full set of data reducing the risk of wrong computation and enabling better insights. Sometimes better visualization of data itself can present ways of for deriving value from it through dashboards that capture and present the data visually in an easily understandable manner. Big Data provides many more opportunities in analyzing data to find more insights like customer behavior by doing segmentation analysis, likes and dislikes, age, income, demographic analysis etc.
What are the recent big data trends you notice in the BFSI sector?
Big Data in itself is an emerging trend in the industry worldwide. Many banks are experimenting with it, and some have taken this seriously, making this as an alternative to traditional data solutions.Within Big data, we see that open source has a higher acceptance with solutions like Hadoop very popular and by far at a very low cost, and others like noSQL data bases like Oracle’s NoSQL, DynamoDB, Cassandra, HBASE, etc.The second trend is high-end monolithic servers being replaced by commodity cluster based infrastructure for quick adoption. Also today, many big players like Oracle, SAP, IBM and EMC are providing appliance based solutions for adopting big data.The third is the belief, by some early adopters, that Big Data can replace traditional data warehousing solutions. However, many banks have already invested billions of dollars in implementing data warehouses either at the departmental level or at an organizational level, and it will not be realistic and an easy sell to replace this. We believe that, over the next few years, we will see consolidation of Big Data applications in different use cases to arrive at a better understanding of its adoption and graduation.
In the future how do you see the uptake of analytics and big data solution in the BFSI segment?
Financial services have always been heavily dependent on the data they generate and use in operations. This sector has had many early adopters for the tools and technologies in the data management space. Data management has indeed been the major investment item in many banks. Now, with the increasing maturity of the modules in Big Data solutions, their adoption will be seen as a major initiative with increase in investments in implementing Big Data solutions.Managing data has matured in the last decade, from a set of reports delivered in a week after manual analysis, to real time information delivery for both diversified and specific needs across the organization. This has enabled them to find the insights they want on the same data sets across the organization, as well as built dependability and trust.Big data is a huge opportunity for the BFSI sector to redefine their services and provide their customers with better service experience. In fact, analytics Big Data will be in the top of many CXOs list of priorities in the coming years to derive competitive differentiation through insights gained from the large volumes of data generated daily by their organizations.
- Weekly Rewind: Top 10 Stories On CXO Today - (Jan 16-21)
- Data Integrity Breaches: What CISOs Should Know
- How Genpact Is Closing The Analytics Skills Gap
- The Key To Protect Customers' Personal Data
- 2017 Will Be A Busy Year For Digital Payment
- Top 6 CXO Trends Companies Should Know In 2017
- Weekly Rewind: Top 10 Stories On CXO Today [Jan 9-14]
- India's Digital Sector To Cross Rs 20k-Cr Mark By 2020: EY
- Are Robots A Growing Threat To BFSI Employees?
- What's In Store For Data Management In 2017