Charles Southwood, Regional VP – Northern Europe and MEA at Denodo
The financial industry is no stranger to disruption and change, but there’s no doubt that right now, it is more disrupted than ever. Indeed, the Capgemini Research Institute’s recent World Retail Banking Report sums up the current banking landscape as volatile, uncertain, complex and ambiguous. Underlying this disruption is the desire and need to digitally transform.
In recent years we’ve seen a boom in the sheer amount of data and personal information that financial services (FS) firms have been collecting and accessing. The data insights available have also become more complete. For example, with COVID19 driving the market to cashless payments for almost everything, the data on our payment patterns is now far more comprehensive. This data has the potential to unlock a raft of new growth opportunities, from increased revenue to enhanced customer services. But it also presents some challenges, which need to be addressed before financial companies can reap the rewards.
Overcoming data hurdles with data virtualisation
The era of open banking is well underway, making digital capabilities the foundation of success and elevating the importance of quality data management architecture. With FinTech start-ups and challengers muscling into the market with digitally native services and disrupting incumbent institutions in the process, there is now a pressing need for financial firms to revolutionise the delivery, integration and utilisation of their own data, harnessing it to drive better business outcomes and customer experiences. It’s never been more important for FS firms to capitalise on their data. But the disparate nature and large fast-changing volumes are making this ever more difficult to achieve. That’s where data virtualisation comes in.
Many of the challenges associated with digital transformation in the FS sector are to do with either establishing or improving the ability to effectively manage data and allowing agents to both access and understand data in order to stay competitive, whilst still protecting their customers from data privacy breaches and complying with shifting industry regulations.
Data virtualisation is a modern approach to data integration. It provides a single, logical view of all data no matter where it originates or resides, which reduces the need to replicate data, and grants financial institutions early, high and accurate data visibility, helping to bring the digital transformation requirement to fruition. Unlike traditional extract, transform and load (ETL) solutions, data virtualisation does not move and copy the data, instead it leaves the data in the source systems. This brings major advantages in agility and timeliness. Rather than replicating, it simply exposes an integrated view of all the data to the data consumers. As business users access and navigate reports, data virtualisation fetches the data in real time from these underlying source systems – delivering speed, agility and accuracy through the seamless connection of data.
The future of finance is data-driven
The amount of data generated from every transaction and interaction is now simply staggering. It’ also not something that is set to change any time soon. In fact, IDC predicts that global data levels will increase to 175 zettabytes by 2025, a rise of 61%. As such, effective data management holds the key for FS firms looking to unlock new, agile ways of working and ultimately to achieving ongoing success.
Against this backdrop, data virtualisation is empowering FS firms to improve the overall performance and efficiency of their operations in a strategic manner, thus reducing costs and shrinking the cycle time for new projects, alongside the ongoing enhancement of business decisions through the provision of granular real-time insights.
Many organisations around the world are already realising these benefits. For example, the Johannesburg Stock Exchange (JSE) has a data landscape made up of over 180 disparate data sources and requires the harmonious operation of over 120 different applications to function successfully and ensure accurate settlement of transactions. Many of these are small in data volumes but of high complexity, making data integration a top priority for the Exchange.
Previously, this data had to be sourced from a diverse array of data systems before being integrated to serve the needs of various business functions. The JSE used traditional batch oriented ETL processes, but with the amount of data flowing through their systems, this was becoming a cumbersome and inefficient way to manage it. In high-speed trading environments, as we know, anything less than 100% accuracy, 100% of the time is not acceptable.
In order to rectify this, the JSE implemented the Denodo platform, with the aim of consolidating its intricate data landscape and aggregating data in real time to build a logical data layer. This platform is a data integration and management solution built on the principles of data virtualisation. All relevant information from the JSE’s various systems is compiled into base views using it. The Exchange has built over 1,700 base views on top of these data sources, which are processed by transformation rules to produce derived and interface views. Now, the JSE’s data integration layer processes about 2 billion rows a month.
As the digital transformation of the financial industry continues to gather speed, banks and other financial institutions must make sure to harness data management architectures such as data virtualisation to enable the insights that help to instil pace and agility of operations to stay ahead of fierce competition. The ability of data virtualisation to generate significant ROI is demonstrable and not to be ignored in an industry context where the margins between success and failure have never been thinner. The message for financial leaders is clear: data virtualisation is key to fulfilling the digital transformation in banking.
