Transforming Data: Data-as-a-Service in Finance

August 26, 2020

Data-as-a-Service (DaaS) offers financial institutions the opportunity for data consolidation, essential when digitally transforming functions and processes. Learn how modernising data the right way can unlock valuable insights for institutions.

At Your Service: Data-As-A-Service In The World Of Finance

Data-as-a-service has given institutions the ability to manage their data in an ad hoc yet fully accessible and effective way.

Data-as-a-service (DaaS), the practice of sourcing and analysing data on demand rather than completing full formal data transformation, has reinvented the way financial institutions handle their data management capabilities. It has given institutions the ability to manage their data in an ad hoc yet fully accessible and effective way, enabling them to drill down to the most relevant data needed for a specific task. 

But what hurdles must financial institutions overcome to ensure that data-as-a-service functions as an efficiency mechanism within institutions’ wider digital transformation efforts? And what new methods are emerging when it comes to managing and using data in financial services? 

Implementing DaaS 

One of the main issues faced by many institutions is that their data is unorganised and/or inaccessible. Related datasets are not linked, meaning few useful insights can be drawn from them. What’s more, the sheer volume of the data stored by financial institutions could mean that processing it is too time-expensive when weighed against any derivable insights. 

DaaS can provide some solutions to these problems. Chiefly, it enables the simplification of data outputs, hence generating coherent datasets in which any present trends or patterns can be identified. It can also cut down the time taken to process data which, for many incumbent financial institutions with vast swathes of data behind them, could otherwise take years. 

DaaS can also be useful in bringing together various datasets in an easily understandable way, working in tandem with data visualisation tools. This not only enables easy comparisons between datasets, but also ensures the compatibility of data between systems. Through use of unique identifiers attached to data from different sources, enabling cross-system compatibility as well as reducing incidences of data duplications.

Targeting the Right Data

In a sector where the level of data processed and used on a daily basis is so vast, it is pivotal that institutions are able to derive the most useful insights from the data they produce. By this year (2020), the amount of data handled by financial institutions is expected to grow by up to 200%. DaaS provides a platform to easily access and leverage this data, but is it enough? 

Many DaaS platforms promise streamlined data access and management, reducing the need for thoroughly planned data extraction and analysis projects. Yet this mindset may encourage financial institutions to cut corners when dealing with data, instead handing responsibility for data management over to their DaaS partner (i.e. a third-party who builds and sometimes runs their DaaS platform). With this in mind, it is key that financial institutions do not see DaaS as a means to absolve themselves of the responsibility of continuously improving their internal data stores. Instead, it is a potentially powerful tool through which strong insights can be achieved based on the data as it is, without further alterations. The onus is also on DaaS partners to ensure that institutions are given suitable exposure and training to the platform so that they can use it autonomously beyond the timescale of the project itself.  

Scaling On Up…  

DaaS as we’ve outlined so far offers a dynamic solution to dealing with high volumes of complex data. It allows institutions to scale up and down their access to data when necessary, producing cost and wider resourcing benefits through reducing the need for the internal maintenance of data.

The other key benefit of the scalability of DaaS solutions is the very nature of financial institutions and how they are developing to meet the challenges of modern finance. The emergence of FinTechs has meant that scalable solutions are integral to ensuring that the needs of an institution can be suitably serviced at any given time. 

Yet the same goes for incumbent institutions. Institutions at the beginning of their digital transformation processes may only require limited data management at first, yet as these operations grow and new avenues of opportunity are explored, so too will their data management needs. While the rigidity of internal data warehouses would constrain these institutions’ ability to expand, DaaS gives them the required flexibility to always be on top of increasing data demands. 

Future Struggles

Digital is the here and now when it comes to the management of data in financial services, yet that doesn’t mean such areas have become static. In 2017, a Gartner Hype Cycle of new technologies in the financial services industry predicted that adoption of DaaS was still at a relatively early stage, with peak adoption occurring in or around 2027. Yet how institutions will put this approach to work is another matter entirely. 

As financial institutions become more digitally-savvy, the opportunities opened up by DaaS as well as other ‘as-a-services’ will be more keenly felt. Rather than incorporating cumbersome data management processes into the business, financial institutions will likely prefer to approach data on an ad hoc basis. Some FinTechs are already doing this, including UK-based Revolut which manages its over 800 dashboards and 100,000 SQL queries using a flexible and dynamic analytics platform based on a hybrid cloud environment.  

Data-as-a-service offers institutions the potential to access the right data services at the right time, reducing costs and the time spent internally managing data. However, institutions are responsible for ensuring that the most benefit can be taken from this service through taking advantage of the scalability of DaaS platforms as well as ensuring that data harmonisation takes place, enabling easy identification and comparison between different datasets. Used correctly, DaaS could be – and, in some cases, is – a powerful tool in financial institutions’ digital transformation efforts. 

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