Banks Need Non-Relational Data Solutions to Transform and Compete
Not surprisingly, 85% of banks cited digital transformation as a key business priority in 2018, according to an EY report.
Why is the banking industry hyperfocused on digital transformation? Over the last decade, global banks have been essentially “treading water,” with the average global bank unable to improve financial performance as indicated by the cost-to-income ratio. The cost-to-income ratio, which is calculated by dividing the operating expenses by operating income, is an important measure for bank profitability. The ratio is a clear indicator of how efficiently a bank is being run—the lower the ratio, the more profitable the bank.
The Average Cost-to-Income Ratio Has Been Flat over the Past Decade
The average cost-to-income ratio of global banks barely moved in the last decade due to higher compliance costs, maintenance of legacy systems and elevated litigation charges, as reported by EY. With costs expected to continue to rise and increased competition from new technology-driven financial services (FinTech) providers, banks will need to not only improve operational efficiencies but also ramp up product innovation and customer engagement to improve overall performance.
To improve business and financial performance in a complex and highly competitive environment, banks have been making investments to address legacy concerns related to poor data and disparate risk and control processes. Unfortunately, those investments have yet to pay off. Although aggregate costs have been moderating since 2013, global banks have struggled to increase income levels. At the world’s top investment banks, 2017 revenues fell to the lowest levels since the financial crises. Although revenues have bounced back in Q1 2018, senior leadership believes that over the next five years, their companies may be at risk of losing an average of 28% of their revenues because of digital disruption.
Banking executives must secure the right digital capabilities for sustainable gains, shifting from a regulatory-focused organization to one focused on data-driven innovation.
First Address Legacy Concerns Related to Poor Data and Disparate Risk and Control Processes
Building an intelligent and innovative enterprise requires a solid technical foundation with a modern data architecture. The first step to becoming digitally mature is addressing any legacy concerns related to poor data and disparate risk and control processes. Based on PwC research, banks continue to struggle with key transformation efforts such as developing a customer-centric business model and proactive management of risks and regulation, as evidenced by the significant gap between relative preparedness and importance of these key initiatives.
According to McKinsey, banks have invested billions of dollars over the last five years to digitize operations, but nearly 50% around the world reveal that their latest digital investments are failing to generate returns greater than the cost of capital.
There are a number of reasons we believe that banks are failing to close the gap in these priorities and generate sufficient returns on investment. Some are internal reasons, like legacy IT systems, data stuck in silos and poor data governance practices leading to “dirty data.” Others are external, like the constantly evolving regulatory landscape and the emergence of competing FinTech providers.
In an attempt to quickly close these gaps, banks have typically added more modern applications and interfaces for data analysis and client engagement on top of existing legacy architectures and disconnected databases. Unfortunately, bank management teams are waking up to a new reality—this technology layering approach is not closing the gap as they had hoped. IT must look at its database layer to improve data integrity and accessibility and begin to close these gaps.
Increasing Investments in Non-Relational Data Stores
Chief information and data officers in the banking industry are quickly realizing that relational databases, file management and application layer technologies will only take them so far in executing data-driven transformation initiatives. As such, investments in non-relational data store solutions, which include NoSQL analytical and operational data stores, are increasing dramatically. According to IDC data, worldwide spending by banks on non-relational data store solutions is expected to grow faster than any other technology spend category with a projected compounded annual growth rate of 38.6% through 2020.
What to Look for in a NoSQL Solution
In evaluating NoSQL database solutions, we believe banks should consider security features, data integration and access performance, and advanced data curation capabilities for use in the development of machine learning and artificial intelligence.
While there are many popular NoSQL databases on the market today, most major banks shy away from using these databases in production environments for handling mission-critical data. Developers love the speed and flexibility of NoSQL, but most NoSQL databases fall short on basic requirements for data security and data integrity. For a NoSQL database to meet banking requirements for operational and transactional data, it should meet the highest level of security certification and provide full ACID transactions.
In addition to security, an important criterion for selecting a NoSQL database should be proof that the solution delivered project results faster by enabling the bank’s IT function to integrate and access data more quickly. While many NoSQL databases offer a single data model (e.g., document only, key value only, graph only), multi-model approaches allow for faster and more powerful data integration and access.
Beyond core capabilities for integrating and accessing data, we recommend that banks also consider advanced functionality to enhance data curation and quality. To improve data analytical capabilities, a “smart” NoSQL solution should include a comprehensive set of capabilities for harmonizing, mastering and governing bank data all within the database. These features are foundational for producing better intelligence applications.
Why Banks Choose to Work with MarkLogic
Many of the largest global banks evaluated NoSQL database providers and ultimately chose MarkLogic. Why? Because MarkLogic checks all the boxes when it comes to bank requirements for the highest levels of enterprise security, core and advanced NoSQL database functionality, and a proven record of enabling the largest global banks to improve business performance.
MarkLogic is an operational and transactional Enterprise NoSQL database that enables many of the largest global banks to integrate data better, faster and with significantly less cost. Visit MarkLogic.com/Financial Services to find out more about MarkLogic’s Enterprise NoSQL solution for financial services, and see how MarkLogic has already enabled data-driven transformation at other banks.