Pacing ahead to conquer the next-gen technology & drive business growth
Table of Contents
Data Lake enables modern enterprises to capture and store massive volumes of data at scale. Its ability to accept data from any source and in any format distinguishes it from conventional storage systems. The global data lake market size was valued at USD 7.6 billion in 2019 and is expected to grow at a compound annual growth rate (CAGR) of 20.6% from 2020 to 2027.
Data is all that matters
In the current business scenario, data-driven decisions hold the key to business success. It has become essential for enterprises to manage and analyze data at scale. However, a majority of data generated today is from unconventional sources and comes in various formats.
Looking beyond the conventional data storages
A traditional repository, which accepts data only from specific sources and in explicit structure, may not be sufficient after all. What you need is a progressive storage system that can accept and store data from all types of unconventional sources and contemporary formats such as:
Photos, Screenshots, or Image files
Webpages and Social media sites
Emails & short messages
Contact center transcripts
Open-ended survey responses
What makes Data Lakes pragmatic?
In addition to this innovative approach, the radical concept of Data Lake has the following features:
Scale : Data Lakes accept data from all types of sources such as websites, mobile apps, IoT devices, social media, and various other modern applications. Also, the marked departure from highly structured storage within traditional relational databases allows unlimited scaling.
Moderation : The arriving data is accepted and retained without any changes in structure or format. As such, the data remains in its original state as long it is accessed and modified for various purposes such as Predictive Analytics, Machine Learning, Data Discovery, and Profiling.
Schema : The Data Lake approach follows the “Schema on Read” as compared to the “Schema on Write” used in the Data Warehouse. It means the schema comes in at the time of analysis and not before the implementation.
Utility : Data Lakes support all types of users as compared to Data Warehouses, which are purpose-built for operational users with specific queries. Data Lakes also cater to advanced users like Data scientists who need extensive data to perform complex procedures like statistical analysis and predictive modeling.
Why BFSIs are focusing on Data Lakes?
The big conundrum
Banks, Financial institutions, and Insurance companies across the globe have been accumulating huge amounts of transactional and operational data at an astonishing pace - almost every day, for years. They are facing the common challenge of finding an ideal storage system that can address their specific needs. If not, they risk losing critical data on which they have been spending millions. They urgently need centralized data storage with express processing speeds to ensure a barrier-free data paradigm.
A credible solution
With Data Lakes, BFSIs have discovered the opportunity to create a centralized storage system that is inexpensive and infinitely scalable. It gives them the unparalleled ability to store raw, structured, or semi-structured data that can be imported in real-time.
By moving the incoming data into the Data Lake, they can now scale up to any size, without having to pre-define schema, data structures, and transformations. It would not only save them a huge amount of time and effort but also give them a competitive edge - achieved by delivering an enhanced customer experience.
In India, leading the way in the private sector are some of the key players. IndusInd Bank’s vision towards being a digital bank is underpinned by a data & analytics strategy. The bank uses data for customer analytics, fraud analytics, and risk analytics. Federal Bank’s COO also has similar views, as she believes her bank has gained deeper insights into several facets of its business with the data lakes approach, which has helped it make better business decisions faster and enhance customer service.”
In the public sector, country’s largest lender - State Bank of India (SBI) has already started the process of implementing the big data lake, which it calls a one-point data processing and storing system. As per its chief information officer, the bank is looking at processing unstructured data along with the information generated within the bank to get insights into user experiences and upgrade its risk management capabilities through the big data lake system.
While economic customer acquisition and persistency are some of the major challenges that can be resolved with Data Lake initiatives, what banks in India need is a reliable innovation journey to secure dynamic and advanced data storage capabilities. With key expertise in implementing next-gen data systems around the evidence-based business models, IT experts like NSEIT and others can enable financial enterprises translate their Data Lake initiatives into an enhanced customer experience and operational efficiency platform. By leveraging these capabilities to develop sustainable data sources, the BFSI sector in India has plenty of reasons to bet big on Data Lakes and make their data more actionable than ever.
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