It’s a common misconception in the financial services industry that a database and a data warehouse are the same thing. Put simply, they are two different aspects of data storage, and each has its own role to play. Both are critically important, however, to enable financial institutions to hold their own in an increasingly competitive environment, and having either system without the other is like having a car with only three wheels.
Understanding the Big Difference
The core database system in a financial institution is designed to handle transactions. It’s usually organized for storage, access to and retrieval of records. Most often, a database is in the form of an online transaction processing (OLTP) system, and are restricted to being used with a single application. This could be Excel, CSV, XML or text, all of which are great for storing data but limited in their ability to analyze and make sense out of it.
A data warehouse, however, is an online analytics processing (OLAP) system that incorporates data from multiple different sources. It’s capable of identifying a single source of truth for all your data and can perform much more complex queries than a transactional database can, and has vastly superior reporting abilities than an OLTP system.
The Importance of Analytics in Finance
In the financial environment, analytics are vital to enable banks and other institutions to withstand the enormous economic pressure they are under. According to McKinsey, 54% of the world’s top 500 institutions are priced below book value, and just 18% hold all the value in the industry. That leaves a large number of players pressed to make improvements, and with the advances in technology and the push to digitization being exploited by the top institutions, it’s essential the rest follow suit. Without the ability to produce and utilize advanced analytics, maintaining any sort of position in the industry is likely to be impossible.
Where Analytics is Going
So, just what can analytics do for the financial services industry (FSI), which could argue in favor of acquiring a data warehouse? We’ve identified three trends worth noting because of their implications:
- Automation is burgeoning, with the increase in robotic processing automation (RPA) and cognitive automation. Financial institutions are rapidly turning to automation for investment reporting, fraud detection, interpretation of regulatory requirements and non-human customer service. All these use complex repositories of business rules and logic gathered from data sources to deliver their services.
- In spite of huge investments by the FSI in technology, mobile apps, social mining and data lakes, the customer experience hasn’t changed significantly enough for the struggling players to retain their clients. The solution to this is enhanced personalization, which can only be done based on the thorough analysis of data collected from multiple sources.
- The FSI might be well-positioned to leverage large repositories of client data, if they can reach a balance between the loss of privacy and the value gained in its place. So far, the jury is still out on this one.
- Cyber-security is an ongoing problem, and as institutions move their data and analytics to the cloud they acquire a whole new security challenge. Frequently, a data warehouse hosted with a professional ELT provider (extract, transform and load) is not only more secure than an in-house OLTP system that’s subject to any number of social engineering opportunities, but it also has the benefit of regular backups and disaster recovery protocols.
The value of your analytics depends on the value of the data input as well as the platform you use for analysis. A database alone is not enough to generate the insights you need for survival. A data warehousing application will give you that.