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
How do financial services companies usually address the challenge of multi-tenant data lakes? Through added infrastructure. According to a ResearchandMarkets report, firms investing at least $50-million on big data and AI rose by 7% last year. Globally, about $180-billion is spent on big data analytics investments, every year.
In short, the pressure to effectively leverage big data analytics in banking drives banks to rapidly invest in extra on-prem and cloud infrastructure, just to keep up with the consistently increasing volume of data. This results in a hefty hardware bill with Dell or IBM, and/or a huge cloud bill with AWS.
Plus, operating the infrastructure itself costs money. Management and maintenance of the command infrastructure and the ever-expanding data lake need the services of professionals with specialized expertise. The costs quickly add up, and many banks are spending more than $100-million a year to keep accessing the benefits of big data analytics in the banking industry.
Managing data analytics in banking
Yet, with a proper analytics stack performance software like Pepperdata, financial services companies can trim this overspend.
If an enterprise can performance-tune their environment, they can reduce the burden on their big data stack. This trims out the need to bring extra infrastructure onboard to keep up. Using software to cut out the waste and optimize the resources in play, companies don’t need as much hardware to handle the flow of data. This empowers the use of big data analytics in banking at a much lower cost than normal.
Spend less on infrastructure, but keep leveraging your data lakes to the largest effect. For financial services companies, this is the promised land. With analytics, getting there is possible. Managing the role of big data & analytics in banking becomes less of a challenge when you’ve got the right tools.
Try Pepperdata for free, and see for yourself how it can help you handle big data analytics in the banking sector. Or, read about how a Fortune 100 financial services giant was able to gain:
- $1.5M savings in infrastructure spend
- 35% improvement in application performance
- 90% reduction in MTTR