The contemporary information age is defined by data. The consistent adoption of digital tools in diverse types of businesses has made data the genesis of informed decision making. Modern organizations spanning all sectors collect and monetize data. Data lakes basically provide direction to this overflow of data in an organization.
It is a place to put all the data that a business would want to gather, store, analyze, and turn into insights and actions. Data lakes play a crucial role in eliminating the data silos, improving scalability, and drive valuable insights, as well as augmenting the standard of the existing business infrastructure.
Banking and data
Data is the genesis of informed enterprise decisions. Today, organizations collect and monetize data. Data Lake gives direction to the overflow of data. Raising the standard of the existing business infrastructure, accelerating process implementation, eliminating the data silos, and maximizing the value of obtained data are the business goals of many organizations by default.
Even though data warehouses have been used by the banking sector for a long time to store and manage their structured, semi-structured, and unstructured data, the intelligence required by such organizations was lacking in this system. Additionally, the typical solutions took way too much time to provide insightful insights on the basis of the collected data. With the volume of data increasing with every passing day in the banking sector, the challenges associated with the traditional system also grew.
In today’s world, having centralized data storage with unparalleled processing speed has become the utmost imperative for the banking sector to ensure a barrier-free data paradigm. Hence, banking institutions are gradually moving towards making business intelligence and analytics-driven moves to effectively cope up with their discerning data related problems. They are aiming to identify robust, efficient, and productive solutions that allow them to derive the most value from data.
The Transition from Data Warehouse to Data Lakes
The implementation of the Data Lake brings more agility to the data exploration and analytical practices of a banking institution. Instead of forcing banking data stifling data silos, data lakes lay emphasis on providing banks with easy access to an expansive berth of information that does not discriminate based on perceived data importance.
Banking organizations tend to get an influx of data just about each and every second. To ensure long lasting positive impact for the institution, it is vital to put this data to use in a smart and systematic manner. Hadoop-based Data Lake Solution ideally tends to get aligned with institutional data and manages to draw insightful reports from them, with the integrated implementation of Business Intelligence and Analytics Solutions.
Intelligent access to data is made possible through Data Lakes, which ultimately yields customer-focused and business-driven outcomes. This smart solution tends to predict and prescribe the most competent and optimized manner to effectively solve challenges with data in the banking sector.
Ways Banks can Leverage Data Lakes
The manner in which a banking organization opts to monetize its data depends on several factors, including regulatory constraints, customer requirements, strategic priorities, and more. Here are some of the ways through which banks can leverage data lakes to augment their operational efficiency and provide superior customer experiences:
- Aid customers in making financial decisions: Banks can aid customers in making better credit risk decisions by choosing to incorporate structured and unstructured customer information into their risk modeling processes, as well as monitor the portfolios of their discerning patrons for early identification of any potential problem. Banks may even use these insights to predict operational losses and detect financial crime.
- Automate compliance and regulatory reporting: Manual tasks undertaken at banks can be reduced considerably by streamlining data extraction from source systems, as well as standardizing data aggregation and reporting for the purpose of driving enhanced productivity and efficiency.
- Improve loan book profitability: Providing the needed access to data, a data lake would help in the preparation of the analysis of credit profile spread against margins and revenue for the purpose of identifying the most profitable customers, as well as reducing the risk of delinquency.
- Improve collection efficiency: The debt collection effectiveness of banks can improve considerably by analyzing debt recovery and collection performance, in addition to the stratification of customers with the aim of identifying and prioritizing which customers to contact at the earliest.
Data lakes play a vital role in executing a successful data monetization strategy, enabling banks to reap optimal value from their data and make it accessible to teams across their organization. This ultimately allows banks to be in a better position to improve operational efficiency and acquire an edge over the competitors.