Is your data ready to drive next-gen banking?

Establish a firm foundation for new strategies .Generative artificial intelligence (GenAI) and advanced analytics have the potential to transform financial services for customers and banks. In just a few years, real-time issue resolution could be a reality for most customers. The same is true for GenAI-based financial planning, with each of us using natural language to make better decisions helped by a coach that has insight into our needs and habits. Business users will be able to create and launch personalized products with little or no help from IT. Automated reporting will streamline compliance..Some banks are already well on their way, but others will struggle. For many, the root cause of success or failure will have little to do with choosing the right AI or advanced analytics solutions to fuel innovation. Yes, that matters, but data is the true foundation for the next generation of financial experiences. A single source of truth is the essential precondition for business strategies that leverage data. .Ready your data for the future .With all the buzz around machine learning and GenAI, you might think it's already transforming banking. In fact, it's transforming our expectations of the future — and it's transforming customer expectations. Soon, your customers will expect much more than chatbots that feel like an interactive FAQ page. They'll expect innovations like family-first banking. With family-first banking, customers receive role-based banking and real-time financial coaching that's tailored to their household. Only data derived from a single source of truth can deliver the insight and consistency required to deliver on the promise of GenAI. .There are a number of signs that your data may not be ready for AI, advanced analytics, and next-gen customer experiences. Time wasted reconciling reports for regulators is a key indicator. At many banks, it's all too common that different teams generate different answers to the same question. For instance, customer service and compliance produce different fraud reports because they pull from different underlying data sources. Even the largest banks have organizational pockets merging data in spreadsheets, writing queries, and pasting the results into regulatory reporting. .Diving into advanced analytics and GenAI without building a single source of truth won't propel you ahead of the competition. It will simply exacerbate data quality issues. The good news is that creating a single source may not be as arduous as you think when you have the correct foundational elements, including automation. Let's look at the essential steps. .Step one: Understand your data landscape .To get started, you need to know what data you have and where it currently resides. Analyze your data sources and map them. Many banks have complex data landscapes, with data stored on premise and in the cloud. Some may live in transactional and analytical databases — and the same data may flow into multiple data warehouses and data lakes. At the end of the day, much of the data likely resides in antiquated mainframes that rely on outdated data platforms and applications. A comprehensive data map will give you insight into your current state, and as you undertake this process, you may see some quick wins from identifying and eliminating duplicate data sets. .Step two: Determine your north star architecture .Now that you have an end-to-end view of your current state, it's time to decide on a data strategy that matches your business needs. How do you want to organize your data? Many banks opt for a strategy that maximizes the use of the public cloud, but that also incorporates a private cloud. No matter the architecture you select, it must support your advancement toward a single source of truth to drive next-generation customer experiences and AI-powered strategies. Having a north star architecture provides a framework for the whole organization to work toward, even though different groups may advance at different paces. .Step three: Normalize and modernize .At many banks, a significant percentage of data resides in outdated mainframes. These won't support your data goals — or your selected north star architecture. You'll need to replatform your data, with refactoring being a recommended step. To streamline refactoring, consult your data map to initiate the data normalization process, eliminating redundant and unstructured data. Refactoring your data involves transforming it and legacy applications into modern systems. Automated refactoring and testing dramatically accelerate the process. Once complete, you migrate your modernized data and applications to the infrastructure specified by your north star architecture..Ramya Patel is a data intelligence leader at software and services company Amdocs.