Effectively leveraging big data analytics in banking is now a competitive requirement. The role of big data in banking is multifaceted. The most common use case is accessing various types of first-party data to better understand customers and tailor offerings and solutions to their needs. Other big data banking examples and use cases include utilizing imperative data to assist banks with Customer 360 and conducting risk management through advanced finance data analytics. Across the board, automation through big data analytics in the banking sector has also minimized the impact of human emotion and bias on financial transactions.
However, many banks are spending more than $100-million a year to keep accessing the benefits of big data analytics in the banking industry. Some of this is unnecessary overspend. Companies need to recruit new technical tools to help them optimize performance and cut the overspend.
The challenge of multi-tenant data lakes
All the major banks are storing and accessing massive amounts of data at all times. This data sits in data lakes, which expand every year. Often, they more than double in size.
Once upon a time, big data banking examples & use cases were partitioned. There would be one data lake for commercial banking, one for retail, one for investment, one for home insurance, and so on. But today, exponentially more data is coming in, and there is one enormous data lake. The various parts of the bank act as tenants, accessing the same data lake. This multi-tenant data lake arrangement puts massive pressure on systems and servers. Frequently, the ability to effectively use big data analytics in banking grinds to a halt.
Multi-tenant data lakes also bring up another concern: data privacy. There is a lot of nervous discussion about how banks and financial institutions handle large collections of diverse, unstructured, private data. And with good reason; when it comes to data analytics in banking, personal information is gathered from consumer behavior analysis, and their decision-making is made using tons of disorganized information. Of course, security protocols are in place to protect such a colossal data collection. But when everyone is dipping into the same lake, the risk of data breaches increases.
How banks end up overspending to keep up