Today’s forward-thinking financial services providers are turning to next-generation technologies such as Big Data Analytics, AI, and cloud-native applications. This shift in direction has been sparked by a need to match the performance and flexibility of new market entrants and to meet growing market expectations for digital services.

With the dependency on data more urgent than ever, many businesses are exploring modern data architecture - both in the public cloud and on-premise - to develop real-time analytics capabilities which unlock insights buried within large datasets. This in turn will support the rapid creation of new value propositions.

However, there are challenges in migrating to a modern data architecture. Many businesses are stuck with cumbersome legacy technologies, siloed data, and slow applications. Such unstructued data workloads are fragile, costly to maintain, and hinder efforts to innovate. The result of this is that long-standing financial services providers could fall behind more agile market entrants.

What is Data Modernisation?

Data modernisation is the process of migrating data from bloated, inefficient legacy systems to more relvant, value focussed, systems that meet current and future requirements. If your business is facing a problem that needs to be solved or is trying to mitigate risk to achieve a business goal – for that, you need to modernise.

The problem with legacy systems is they lack the ability to solve modern data problems, consisting of too many steps and too many integrations. Invaraibly, this creates unecessary operational complexities and time constraints, both of which lead to higher costs.

A well implemeted data modernisation strategy allows businesses to access cleaner, more comprehensive datasets. From here they can capitalise on actionable insights, API-driven application integrations, and respond in real-time to the demands of the modern bussiness landscape. In short, it means making businesses more responsive, more agile, and able to make better decisions. 

How financial services providers use data architecture to tackle data silos and drive efficiencies

Legacy data architectures are at a tipping point, struggling with rapidly evolvingand growing data volumes and the sheer variety of data they need to process and store. Modern data architecture addresses these problems by enabling organizations to quickly locate and unify their datasets across hybrid storage technologies, deftly handling the volume and variety of big data.

Financial services providers are leveraging the power of modern data architecture in all kinds of ways. For example, robust risk and compliance strategies require access to different types of information. 

Content sitting outside of structured databases, such as chat logs, various documentation, and emails, all contain crucial information for supporting these strategies. Such text-based content often contains information that can help with fraud detection and regulatory compliance. By supporting both structured and unstructured data, a modern data archictecture can drastically reduce the man-hours otherwise needed to achieve this. 

The hurdles to deployment of AI-driven technologies within FS 

There are two obvious hurdles; businesses not having the problems they want to solve clearly defined, and not having the right data in place or structure.

Effectively, if businesses can overcome both these hurdles, they’re ready for deployment. 

It’s also important that all non-technological stakeholders are clear on any approach towards AI as well. Such endeavours don’t always work first time, but if everyone is focused on the same goal, there is a much greater chance of success. 

How data management and advanced analytics provide value to the customer 

Advanced analyitcs are important when we’re discussing how to expand on customer lifetime value. Ultimately, customers want a service that’s relevant to them and quick.

However, it’s important that we don’t devalue basic analytics. The information they give us on the likes of customer behaviour, segmentation, devices, and frequency of visits can all still drive significant insight. 

Integration challenge: modern cloud architecture Vs legacy technology systems 

Most financial service providers are doing it with cost benefit in mind. Some applications simply will not move a business forward and it’s vital that this is recognised and processes are kept simple. As business leaders, it is our responsibility to ensure this happens. This involves looking at non-functional requirements, what these are around issues such as scalability and speed of scale, and looking at our stacks not just vertically and horizontally, but through the middle too. 

Some businesses are also looking at multi-cloud structures, but they must consider the ease with which applications can be moved between different cloud services providers. If a business is going to be cloud-agnostic, this must be at the top of features and considerations before anything gets built. Tooling is also key here. By keeping processes simple, businesses will avoid shooting themselves in the foot by getting locked into agreements.

As we look to the next two to three years, we’re going to see more businesses embracing the likes of AI and predicitve analytics. This will likely to happen mostly with start-ups who will begin offering services that right now might not even seem feasible.