The financial services industry is currently in the midst of what KPMG describes as the “third wave of data value”. In one of the most data-intensive sectors, financial services institutions generate and leverage vast volumes of data, all of which can provide insights of value to the business. However, with the scale and complexity of modern data, the only way to truly harness the value of disparate data sources lies in automating the process of data discovery, preparation and analysis.
According to KPMG, the value of data initially lies in its ability to make routine processes more efficient. The second wave saw data integrated across entire enterprises to reimagine processes and improve how services were delivered. Today, data is recognised as a source of insights and information, representing significant value not only to financial institutions but also to their customers, partners, and third parties.
The customer experience, for instance, is critical to growth in the financial services industry, where competition between providers is heated. Understanding customer behaviour, needs, and preferences and anticipating their future actions will enable organisations to tailor their products and services and take advantage of opportunities to improve the experience they offer. And data, of course, is essential to painting this accurate picture of a customer.
Today’s enterprises are pulling data from multiple input sources, from legacy databases and applications to modern cloud data warehouses and platforms, all in various formats and structures. The challenge is facilitating the extraction of value from data at the speed and scale needed so financial services firms can develop personalised offerings, sharpen analytics strategies, and navigate their journey toward modernising their data management. Without the right analytic automation tools in their tech stack, it can be highly challenging for a CFO pursuing a reduction in the time to close the quarter or a Head of Supply Chain wanting to optimise complex logistics to analyse all this disparate data and maximise its value for real-time insights.
The right data analytics stack
The technologies available to CIOs to modernise the data management journey across the entire stack have never been better. Evidence of this can be seen in the rise of cloud-based data warehouses and lakehouses and in the confidence shown by financial service IT teams over the past year to quickly build their own LLMs based on internal training data.
Despite this clear trajectory to data maturity, many organisations can’t unlock the full potential of their data to ensure it drives business value, even as it grows in volume and variety. According to the European Commission, 80% of the data collected in the industry remains unused. Many are still overspending on the cloud, or they’re saddled with tool sprawl and buying software licences that sit unused. Many organisations’ data analytics stacks aren’t set up to take advantage of the available technologies.
A data analytics stack is needed to more effectively address the various roles and use cases in today’s financial services industry. It should have the flexibility required to support multiple deployment scenarios, such as managing data pipelines, whether on-premise, private cloud, public cloud, multi-cloud, or hybrid. Similarly, it should allow operatives to transform their data in any data warehouse environment they choose and enable them to build a data workflow in one place and execute it in another.
Perhaps most importantly, the right data analytics stack should empower a company’s workforce to get the most from its technologies. It should be accessible:
- Low-code or even no-code
- Making it easy for anyone to compile and analyse data
- Not just data scientists or engineers
As mentioned earlier, data offers value across an organisation, enabling outcomes for all business areas. Whether developing personalised offerings for customers, reducing churn, or ensuring regulatory compliance, each line of business should be able to creatively solve its own analytic problems and apply its domain knowledge to relevant and impactful use cases.
Automating to support regulatory compliance
To illustrate just one of the benefits of a tech stack capable of automating data analytics, consider how they can be used to ensure regulatory compliance.
Regulations such as MiFIR and MiFID II require financial institutions to report applicable transactions. But, since millions of transactions can happen every minute – especially at larger institutions – manually preparing and cleansing the data for quality assurance (QA) can be extremely slow. As a result, it can take up to two months for the appropriate regulators to be notified, leaving financial institutions vulnerable to regulatory fines should any part of the system fail and require correcting.
An automated data analytics stack, combining data engineering capabilities with a mixture of visual analytics tools, can create a streamlined workflow that alerts institutions in real time if they must make corrections to ensure compliance. This transforms the QA process from reactive to proactive, allowing teams to address issues as they occur rather than waiting months for results, thereby mitigating the risk of potentially costly mistakes.
This is just one example. Automated data analytics can be applied to almost any business use case, enabling financial services institutions to deploy an analytics strategy to identify new opportunities, comply with regulations more easily, meet ESG criteria, and more.
In this “third wave of data value“, a modern automated data analytics stack with a simple and accessible interface will allow everyone within a financial services firm to deliver that value to stakeholders within and outside their organisation.
