The Critical Data Analytics Challenges in The Banking Industry

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2.5 quintillion bytes of data – That is the volume of data produced by the global population daily. 

This data holds the promise of endless opportunities that almost all forward-thinking businesses are keen to leverage. Even industries that have been considered ‘traditional’ such as banking and financial services, automotive, manufacturing, etc. have been moved by the big data proposition.

Owing to digitalization, digital transformation, the rise of digital banking, etc., the banking industry today has enough data in hand to rethink and re-evaluate the way they operate and become more customer-centric. 

The worldwide revenue for big data analytics is expected to reach $260 billion by 2022, with the banking sector making some of the highest investments in this technology. 

However, while the industry is gravitating towards becoming big data-driven, there are some critical challenges that it needs to navigate.

Legacy systems are a reality

Legacy systems in the banking domain are an inalienable reality that we cannot ignore. When it comes to big data, these legacy systems become roadblocks owing to their limited capability to process huge volumes of transactions efficiently. They are unable to cope with growing workloads. Collecting, storing, and analyzing the required data volume employing an outdated infrastructure also puts the stability of the system under risk. Holding large volumes of data is stored in legacy systems also becomes a challenge since these systems do not work easily with systems such as Hadoop.

A complete overhaul of the legacy architecture might lead to costs that can be prohibitively high. Business disruption is another important point of consideration. However, since we cannot immediately do away with legacy systems, we need to transition these systems capably so that they can work in the big data narrative. Working to grow the processing capacities or rebuilding the systems using the latest technologies such as cloud could be a way forward.

Unstructured data is important

While it is great to have a sea of data to work with, not all data is created equal in the big data analytics universe. Different kinds of data coming from several sources become a struggling point. While the share of useful data is increasing, there still remains irrelevant data that banks have to sort out.

Data quality also makes a big impact on the outcomes of big data analytics. The concept of ‘garbage in, garbage out’ cannot be ignored here. Separating structured data from unstructured data to determine its validity, accuracy, completeness, timeliness, etc. play a critical role in big data analytics outcomes.

It is also critical to develop the capacity to leverage unstructured data, the data that lies scattered across locations, data sources, and unstructured data pools. It is important to capably structure them to find correlations using smart analytic tools in combination with machine learning, NLP, and AI to bring tremendous business value.

Domain knowledge is hard to find

While big data promises to deliver tremendous business value, it can only do so when there is business understanding. Having a strong domain knowledge of the challenges in the banking sector, the impediments and opportunities, the regulatory landscape, and essentially all the moving parts, determine the capability of a big data analytics solution. 

Unfortunately, while there are many big data analytics solutions, there are few solution providers who can combine technical dexterity with domain knowledge. Only with a solid understanding of the domain and technology one can determine which analytics benefit the business, the direction the technical architecture should take, how to map and reduce ETL (Extract, transform, load) jobs, map processes, and determine how new technologies such as Machine Learning and Artificial Intelligence can be implemented to make big data analytics smarter and more intelligent.

Security and Privacy

While big data comes with huge potential, it also raises some red flags regarding security and privacy. There is the challenge of securely collecting, storing, and analyzing data and creating secure solutions.

The ever-changing compliance and regulatory landscape become hard to navigate in the light of regulations such as GDPR, Dodd-Frank, Basel III, FATCA, etc. Complex rules govern the access to critical client data, and rightfully so. Where there is data, there is a risk, especially with the legacy problem that we have mentioned before.

Clearly, a robust big data analytics solution is incomplete without considering these factors.

Despite these challenges, the benefits that big data analytics provides the fuel that can propel a business to the pinnacle of success. Hoarding large data volumes is not enough…what we do with it is. Be it process optimization or automation, personalization, segmentation and targeting, improved cybersecurity and risk management, performance management, etc., it is becoming clear that big data analytics is becoming critical for business success. Navigating these challenges, thus, no longer remain optional.
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