This offers the potential to provide business insights and to hand some capability back to your business users. Consider implementing a data warehouse for structured data (e.g., reg reporting, inter-system files, end-user reports) and a data lake for unstructured data and underpinning advanced data pipelines. Imagine giving the business stakeholders the ability to quickly turn this around themselves in real time - the data is there, somewhere, but how can we unlock it?ĭata warehouses and data lakes are key elements of a data-centric strategy. The usual process of engaging technology, prioritizing, defining the requirement, building and testing can see any potential business advantage become a distant memory by the time the information is available. Let’s say you want to see the timing of cross-asset activity of a subset of clients or frequency of unusual funding requirements over time. You can use your ESB or iPaaS as an end point for the legacy and your streaming platforms for market and enterprise data. However, it may not be compatible with all your applications if you’re supporting a legacy stack, so consider combining the two. If real-time streaming is a need for you, you might look at an open-source data integration and distributed event streaming platform like Apache Kafka. A more modern approach to integration will enable much better management of transformations, filtering and routing than old-school point-to-point. The solution might be an enterprise service bus (ESB) or an Integration Platform as-a-Service (iPaaS) if you are working with on-prem and cloud-hosted apps. One of the most significant improvements is to implement some form of middleware the exact size and shape varies depending on organizational size and complexity. This can make system upgrades a nightmare. Often, you’ll need to make changes at both ends. Another consequence here - testing gets complicated. When you try to change, your systems are so closely coupled that a shift of application B directly affects application C. Point-to-point integration does the job, but locks you in. Keep your core trading and risk management systems lean so they can do what they are supposed to do.įirstly, decouple integration. This could even be impacting the potential of core trading and risk systems as they manage messaging, mapping, data mastering and archiving in the absence of a cohesive set of solutions.Ī no-nonsense remedial approach is to use systems and applications for the purpose they were designed. Workarounds are often apparent in that more flexible, modern applications have been configured to do the heavy lifting for inflexible legacy technology. In some cases, there will be lots of manual work in evidence of past choices that don’t support the digital aspiration. However, it doesn’t take long to pinpoint the more apparent problems many companies share. However, the difficulty of developing a successful digitalization strategy that integrates to a firm’s existing data strategy and architecture varies dramatically from enterprise to enterprise. Delivering a smoother multichannel customer experience, taking advantage of API-based integration to broader market services and leveraging the insights available through big data are all common themes across financial markets participants today.
0 Comments
Leave a Reply. |