28 Jul Modernising Data Platforms for Advanced Analytics in Financial Services
Many financial organisations have multiple technology stacks, with years of legacy development. Systems optimised for discrete and static reporting result in data silos, while bespoke workflows hinder integration. As a result, model outputs rarely flow into customer interactions, risk assessments, or compliance processes. Personalisation falters when data across channels cannot be integrated. Fraud detection suffers when key alerts and signals remain trapped in legacy repositories.
Governance adds to the strain. Tools often lag the pace of development, making audit and regulatory reporting difficult. In Australia, regulators such as APRA (the Australian Prudential Regulation Authority) are increasing scrutiny on data risk and operational resilience. Standards like CPS 230 elevate the need for auditable data lineage and strong controls to executive and board accountabilities. Teams are forced to spend limited resources on maintaining legacy outdated systems instead of improving models or delivering modern capabilities.
Designing a Modern Data Platform
Overcoming these barriers requires a new architectural approach and a foundation that will enable AI based use cases in the future.
Beyond technical gains, modern platforms help to deliver real business value and build a foundation for an AI future. A modern data analytics stack also helps attract and retain top data talent, who expect efficient, scalable tools.
Implementing Change for Lasting Impact
The first step is a clear assessment of the current environment. Leaders need to map where data lives, identify integration gaps, and quantify technical debt. This informs a reference architecture that balances short-term needs with long-term goals.
Initial success often depends on targeting a single, high-value use case, such as transaction monitoring, personalisation or customer segmentation. Rapid, measurable results build momentum and provide a blueprint for broader transformation.
Equally important is changing how teams work. Data scientists, analysts, and operations staff must collaborate in shared environments where data quality and traceability are guaranteed. Training and change management are essential to help teams use insights confidently in daily decision-making.
A Recent Modernisation Case Study in Financial Services
Modernising data architecture is a significant undertaking. It requires executive focus, coordinated teams, and disciplined execution. Financial institutions that commit to this path will move beyond pilot projects to real enterprise impact. By building modern platforms that support secure, real-time intelligence, organisations can shift from being data-rich but insight-poor to achieving sustainable competitive advantage powered by embedded intelligence.
A leading financial institution faced significant challenges following growth through acquisition. With numerous disparate and siloed customer systems, the organisation struggled to create a unified view of the customer: a key capability for driving insights, operational efficiency, and personalised service.
InfoCentric was engaged to design and implement a best-in-class, modern data platform that could serve as a single source of truth (SSOT). The brief included establishing a foundation for advanced analytics and reporting, creating a scalable architecture for future acquisitions, and building a robust operational model to support ongoing platform management and future AI use cases.
The approach began with a comprehensive data strategy to define the business case, investment priorities, key use cases and transformation roadmap. A technology pilot gave business stakeholders early confidence in the target-state architecture. The team then designed and built the SSOT platform, integrating data from across acquired entities and embedding managed services into the operating model.
The results were significant!
Sixteen complex and disparate customer data sources were consolidated into a modern enterprise data platform that could easily source, ingest, transform, and integrate new data sources. The institution gained advanced reporting and analytics capabilities, enabling a single customer view that supported operational improvements and targeted segmentation. New insights emerged on cost-to-serve, profitability, customer retention, and pricing strategies.
The platform ultimately transformed how the institution engaged with its customers and made data-driven decisions.