Digital Transformation in Financial Services – Addressing 5 Key Trends with Data Virtualisation
Six months ago, with the potential impact from Brexit, COVID-19, and the US Presidency changes, you would have been brave predicting the future of any market. However, with growing certainty around the outcomes on these and other factors, the start of 2021 is a good time to look more closely at the future and consider the major digital transformation initiatives taking place in financial services.
Here we look at 5 key trends and the role that data virtualization plays in accelerating business value. Data virtualisation rapidly integrates data from different sources without having to move or copy it; it provides a single virtual layer for accessing all required data.
1. Cloud Computing
The benefits of cloud computing are widely acknowledged, but the journey to realising those benefits can be challenging. It can be complex to maintain hybrid cloud infrastructures, as this requires organisations to combine many data sources of different formats and protocols from several different cloud services, as well as perhaps from legacy on-premises sources and Software-as-a-Service (SaaS) applications.
To address these needs, data virtualisation is placed in the architecture over the top of all these disparate data sources, providing a unified layer that protects users from the complexity below while integrating and exposing all data through a secure unified data fabric. No data is moved or repeatedly stored, so IT can get on with cloud migrations and application changes under the virtual layer without disruption or change for the business users. As one user put it, “with data virtualisation we’re able to change the wheels of the car while driving at 70mph.” The major cloud service providers all have their own portfolio of data integration tools for their own platforms, but with many financial institutions now using multiple cloud services, data virtualisation is playing a key role in integrating data across these different providers’ services.
2. Open Banking
With open banking, the question arises as to how much data to expose, and how easily this can be accomplished. Second, there is a data-integration challenge, as open banking depends on the consolidation of data from many different, often siloed data sources, based on different technologies and formats.
The unified layer provided by a data virtualisation platform enables any data sources or applications to be combined in the virtual layer and then exposed to any number of data consumers. In addition to accelerating data integration in an open banking scenario, the virtual layer enables centralised security and governance as well as easy exposure through APIs. Unlike traditional data integration methods that rely on the batch movement and copying of large volumes of data, typically overnight, using extract, transform, and load (ETL) tools, data virtualisation provides access to the data in real time, on an as-needed basis.
3. Mobile and Online Banking
Bank closures during the pandemic lockdowns and concerns over contagion have encouraged the adoption of online and mobile banking. In the Statista Research report of November 2020, the adoption of online banking rose from 55% to 76% over the previous 5 years, and this trend shows no signs of abating. The exposure of online banking services and apps on mobile devices requires back-end data integration from many disparate systems, many of which were developed without consideration for these new data integration needs.
Using data virtualisation as a common layer for exposing all sources enables a data model to be created once, in the virtual layer, then exposed to multiple channel consumers; say to the mobile app, through HTML to a web page, and then perhaps to management dashboards and reports. All these different consuming technologies can be fed from the same data model with just the publication layer adjusted to serve the format required by each data consumer. This accelerates development and the addition of new products, services, and functions.
4. Digital Payments
The growth in the use of contactless cards has been rising rapidly with talk of a cashless society finally becoming a real possibility. By April of last year, Visa in the US reported 60% of its face-to-face payments being contactless, and Mastercard reported 51%. In August 2020, the point-of-sale and payments specialist Square reported that less than 25% of payments were being made with cash. Square found that 31% of businesses made the move to being cashless by mid-July from just 8% at the start of 2020, which equates to an increase of 288%.
This gives new insights into payment patterns, which helps to reduce fraud as well as to aid in the recognition of new upsell opportunities. Agile development is needed to take advantage of these shifting behaviors, and data integration is at the heart of such efforts. Data virtualisation is being used to accelerate analytics outcomes, with users reporting savings of up to 80-90% of typical development and deployment times, which leads directly to competitive advantage.
5. Improving the Customer Experience
COVID-19 has undoubtedly accelerated the appetite for digital transformation in the financial sector. Extra pressure comes from customers themselves, who are demanding more from their financial institutions. Virtual assistants and chatbots are now more widely used, and analytics and AI is playing a key role in providing additional insights, enabling more personalised banking, improved customer experience, and more relevant product offerings. From the bank’s perspective, the extra data means enhanced opportunities for propensity modelling, for upselling current products, for a better understanding of needs around new offerings, as well as, once again, better real-time systems for reducing fraud.
For analytics and digitisation to be most effective in improving the customer experience, data integration is key, and many financial institutions have leveraged data virtualisation to accelerate these capabilities. As data volumes grow and the data becomes more disparate, the old models of repeatedly moving and copying data across the enterprise are too costly, too complex, and too slow. For such institutions, data virtualization provides a single point of access to all the data, regardless of source and location, and without copying or moving the data.
Data Virtualisation for Financial Services
For these 5 trends and other initiatives across financial services, data virtualisation offers proven value. For IT stakeholders, data virtualisation enables easier integration of disparate data sources and applications, reduced processing and data movement, reduced storage costs, much faster development/deployment times, and more effective data governance. It enables business users to access data securely through a single access point for all data sources, for agile, self-service BI. For advanced analytics users, data scientists, and “quants,” data virtualisation enables seamless exploration for easier access to new insights and a better understanding of the data lineage, the impact of any changes, and what data might be available for analytics and artificial intelligence (AI)/machine learning (ML) models. In short, data virtualisation provides financial institutions with faster time-to-market and greater agility in meeting changing needs.