If in 2015 banking and financial markets firms were infants in utilizing Big Data to effectively transform their processes and organizations. They have turned out to be toddlers in 2016 as they inch forward from various stages of their activity with Big Data. It is encouraging to see banks continuing to make progress on drafting Big Data strategies, on-boarding providers and executing against initial and subsequent use cases. The BFSI segment (Banks, Financial Institutions, and Insurance companies) principally use Big Data to improve customer intelligence, reduce risk and meet regulatory objectives. As a result, the market for data software and services providers is moving closer to a break-even point where banks adopt, on larger scales and with greater confidence, solutions to manage internal operations and client-facing activities.
Moreover, the BFSI sector deals with a lot of vulnerable data, making data security a challenge for this industry. Customers’ personal data, financial data, location, and identity are among the various kinds of data banks and other financial institutions have to delve into and preserve from security threats. Secure database and stringent data governance provide complete control over who gets access to which data. Various components of Big Data are aligned with maintaining data security in storage and maintaining and upgrading with various compliances. Similarly, another important part of this is risk management. Data lakes can serve as converged regulatory and risk (RDARR) hubs. Thanks to predictive data analytics, it is easier to sort through customer history and other information to filter out risks and fraudulent activity before investing.
Similarly, as data governance, lineage, and other compliance aspects have become more deeply integrated with Big Data platforms a huge number of banks have either started to either develop or purchase point solutions to find a more complete and comprehensive data solution to manage compliance mandates. This has resulted in an increasing number of improved data governance, lineage and quality solutions for Hadoop. These new platforms can reach beyond Hadoop and into traditional/legacy data stores to complete the picture for regulation, and they are doing so with the volume, speed, and detail needed to achieve compliance. On a similar note, the adoption of Hadoop for RBase storage and access is proliferating within financial services. Data offload is now a “classic” use of Hadoop (relatively speaking), while the cool kids move on to larger Big Data playgrounds, and the masses will climb on board for this application of Big Data.
The Data Lake Dilemma
The data lake (a single store for all enterprise data characterized by the ability to collect vast amounts of data in its native, untransformed format at a very low cost) offers much promise but it also has limitations. Cataloging data sources, harmonizing disparate data and adding meaning to the data continue to be challenging for many organizations. Although there are a lot many vendors who are attempting to solve pieces of this problem, only a few succeed in working with semantic technologies to provide a holistic end-to-end solution.
Similarly, Smart data lake tools leverage the power of semantic technologies on top of Big Data tools like Hadoop HDFS and Apache Spark. By delivering a massively parallel, in-memory, graph database that supports semantics standards, companies can overcome one of the long-standing challenges with semantic technology – performance at scale. It is now possible to run interactive graph queries across enterprise data sets (tens of billions of triples).
These technologies also allow organizations to leverage industry-standard models like the Financial Industry Business Ontology (FIBO) from the EDM Council. When combined with enterprise-scale semantic tools to operationalize the model, smart data lakes provide a powerful path forward for the industry to benefit from its data assets.
The smart data lake tools also solve another challenge with data lakes: end-user access. Most data lake solutions require manual coding for transforming and preparing data for consumption by BI tools. Data described by semantic models does not presuppose the queries and analytics it needs to support. The semantic descriptions enable end users to find the data they need and to query it in business terms, without any coding.
This democratization of data access will open up access to enterprise data from a few data scientists to many business analysts. However, on the other side of the graph, there still exist a minority group of financial intuitions that use their existing legacy platforms that are not able to deal with the data surge.
The IoT Hype
The next wave of hype to grab Big Data’s attention is IoT. The Internet of Things (IoT) is, without a doubt, one of the biggest technological transformations on the horizon, with many already claiming that we are entering the second major digital revolution. Analysts at Gartner predict there will be 25 billion smartphones, smartwatches, wearables, connected cars and other connected devices by 2020. An amazing forecast, that strongly indicates the influence that machine-to-machine (M2M) connectivity is going to have on our society, culture, and business. In a very short space of time, we are all going to be surrounded by intuitively connected devices, from our smartphones and wearable tech, through to millions of sensors in our homes, on our roads, and in our workplaces. For business, there are multiple opportunities to benefit from IoT, with $2 trillion of economic benefit predicted on a global level by Gartner.
Financial institutions, especially retail banks, have invested increasing amounts of resources into developing both their internal infrastructure and consumer-facing technology capabilities. IDC Financial Insights predicts that retail banks will spend over $16 billion on digital information technology initiatives, and this spending will continue to increase. In fact, according to PWC’s 6th annual digital IQ survey, financial service is one of the top 10 industries that has been investing in sensors for potential IoT innovations. Financial institutions have been busy holding board meeting discussing how they transform the ATM experience by augmenting it with the smartphone or smartwatch and skipping the debit card experience. Recent research shows that consumers who receive personalized messages are nearly 20 times more likely to buy thus proving that by connecting the retail banking environment, banks can increase the adoption rate of extra lines of services dramatically with personalized, contextual messages.
The banking industry is now starting to see the various potential ways in which IoT can help to take it to the next level.
The time has come where banks have realized that IoT extends beyond ATM and Mobile banking. For instance, real-time, multi-channel activities can use IoT data to offer the right offer and advice to retail banking customers at the right time.
So How Can the Data Generated from IoT Be Used to Add Value for Banks and Customers
Machine-to-machine connectivity that enables the mass collection and exchange of information from sensors and objects also opens up multiple opportunities for banks, who will be able to better track and analyze the behaviors, wants and demands of their customers. This, in turn, allows banks to provide customers with a far more personalized experience, with targeted advice, context-aware offers, and insight. The bank is able to achieve a new level of understanding of the needs of both consumer and business clients, attaining a new level of customer intimacy.
Banks is increasingly using IoT technologies to create more engaging and context-aware customer rewards and to generate more intelligent and personalized customer cross-sell opportunities. Business clients and consumers are now able to access a much more holistic view of their finances wherever and whenever they like. And banks are offering far more tailored products and solutions to help customers make the best financial decisions at all times.
The increased amount of real-time data available to banks, from information on residential and commercial properties through to personal data from social media, spending habits and credit behavior, is allowing banks to make better commercial decisions, based on far more accurate financial risk data.
It will be those banks that best use these new types of IoT-generated data streams to make vital decisions on business lending that stay ahead of the curve. In terms of consumer-facing retail banking branches, IoT could also be used to assist customers with new and improved video tellers and kiosks that will be equipped with sensing technology that will be able to biometrically recognize the customer from the moment they enter the branch.
Having seen the awesome possibilities that Big Data is offering for the BFSi segment, it is time for a tiny change of perspective. Let us look at some numbers. Below is the average salary of a Data analyst and a data scientist as per PayScale India.