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Data management is one of the greatest areas of tension and strategic importance for a bank. Outdated structures, complex and expensive processing procedures, and a hesitant approach to renewal will continue to generate increasing costs and quality losses.
While most banks generate and process data directly in core systems or Core-Banking Solutions, this leads to problems related to data provision and availability, as data is often fragmented across multiple systems. This often makes it difficult to maintain a consistent view of customers and transactions. Especially concerning analyses, evaluations, and reporting, this places a significant burden on the productivity of core systems. In terms of data protection, outdated structures also present targets for hacker attacks, as they are more vulnerable to security breaches.
Therefore, many banks are attempting to master complex data management through centralized data storage. In the past 20 years, the path through Data Warehouses (DWH; RWH) has become established.
A DWH is a large, centralized database that consolidates data from various sources, controls it, and makes it available for later analysis. However, with the ongoing integration of new technologies, traditional DWH systems are increasingly showing their disadvantages:
Data Warehouses enable at least a (somewhat) uniform access to historical data and allow processing independent of production systems. However, DWH solutions usually only mirror or extract data from legally leading systems that do not need to be reprocessed. Providing real-time data can be problematic, as data must first be uploaded from surrounding or source systems in batches. However, companies relying on DWH solutions are already becoming outdated:
DWH systems are often difficult to adapt, as changes to the data structure require extensive and time-consuming revisions, which primarily require precise and manual skills. Data processing in a DWH can be slow, which becomes a problem, especially with large amounts of data, even when provided to external B2B partners. Especially when banks already share data externally (B2B area or B2C customers with connected software solutions), there is often the dilemma that the data is only available on a daily basis.DWH solutions are also expensive to maintain and operate. The performance of each house or IT department lies in efficiently and precisely processing the data stored in the systems. However, this also means maintaining personnel and system capacities and regular high maintenance of the data.
Data management is therefore becoming an inevitably more complex issue. Novel business models have already detached themselves from the classic processing approach. With exponential digitization, end and corporate customers (B2C) as well as financial partners (B2B) increasingly expect faster and more precise interaction with their financial service providers. This concerns the timeliness of data, quality, and precision, as well as the capturing and processing of business transactions. Many banks solve this by accepting customer data and inputs in real-time, but the subsequent processing has to be paused and stored for several hours. This is particularly due to the fact that the IT architecture and the communication between core and processing systems are not synchronized, but are stuck in sequential processes ("batch processing"), thereby creating an elaborate buffering of data and also subject to downtimes (especially at night and on weekends).
This also affects the data made available via internal and external interfaces, such as SFTP servers to CSV files. Interruptions in the sequence often mean that data is not made available on time. This often leads to outages on the part of financial partners and software suppliers in the B2B banking business, who rely on this data to continue working. An additional urgency arises from the steadily increasing amounts of data, which affects performance and hinders production systems. Data is made available on a broad basis.
Given these challenges, banks will have to look for alternative solutions in the coming years that offer more flexibility and efficiency. They need to leverage synergies, reduce costs and complexities, and ensure high quality in the data area to provide new real-time-based products and services to their customers.
Here are some of the most innovative approaches in modern data storage:
Data Lakes are a more flexible approach to data storage, where data is stored in its original form. This means banks can process and analyze data as needed, offering more flexibility than a DWH. Additionally, Data Lakes can accommodate both structured and unstructured data, making them suitable for a variety of use cases.
Cloud data storage solutions offer many advantages over traditional models. They are scalable, cost-effective, and allow fast access to data, no matter where users are located. Cloud providers also offer advanced data analysis tools, which enable banks to extract important insights from their data.
Increasingly exciting are business models that modern "Tech" banks offer, complete with white-label Banking as a Service solutions and provide state-of-the-art data management. Providers also underpin this with corresponding banking licenses. Data and backend services are provided through powerful electronic interfaces (APIs) and are thus easily and efficiently tradable. This has the particular advantage that banks do not have to invest in know-how and infrastructure and can obtain ready-made solutions within a few weeks. Ongoing maintenance and provision are also offered as a service. Traditional banks thus benefit from concentrated know-how and can focus on products and distribution.
Without a doubt, database and data structure changes mean an "open-heart surgery" for a bank and are traditionally shunned by managers. Therefore, it is likely that bold houses will decide to start with a hybrid approach in the future, combining the advantages of Banking as a Service, Data Lakes, and Cloud with DWH. In such a model, structured inventory data could be stored in a DWH and transaction-related and unstructured data could be stored via a Banking as a Service solution or a Data Lake. Also, transferring the DWH to Cloud Service would make sense to ensure flexibility, stability, and scalability.