Authored By
Ted O’Connor
Ted is a Senior Vice President focused on Business Development at Arcesium. In this role, Ted works with leading financial institutions in the capital markets to optimize data, technology, and operational needs.
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Data is at the heart of capital markets and global banking. It powers nearly every function, from operations and regulatory reporting to client service, treasury, finance, and so much more. Now, as data volumes expand and artificial intelligence (AI) accelerates the expectation for speed and efficiency, the ability to effectively serve and manage data across the organization has become paramount to its success.
With this in mind, why then do many banks continue to face fragmented systems and legacy architecture that hinder timely access to trusted, actionable data? Whether at the firmwide level or even within a specific business line, building a connected, governed data ecosystem is key to delivering timely insights, supporting decision-making, and unlocking the full potential of one of the bank’s greatest assets.
As the market signals toward favorable economic conditions and growth opportunities, the imperative to invest strategically in modernizing technology infrastructure becomes clear.1 The complexity of capital markets makes data even more important. Even the execution of a single transaction depends on data flowing accurately and securely across systems. The strength of any report relies on the integrity of its underlying data. Similarly, client relationships are strengthened through timely information and transparency.
Among the most important requirements for data management is its quality and availability. When data is accurate, well-governed, readily accessible, and timely, it can help unlock pathways to sharper insights and sustainable growth. Conversely, when data is fragmented across disparate systems, compiling necessary information can waste critical time with the potential to delay important decisions, slow down settlement, or even jeopardize key relationships.
While data has always been a strategic asset for banks, now is the moment to elevate data management and better use data as a catalyst for growth, moving beyond its traditional role as merely a technical resource.
What’s holding firms back? Despite the recognized importance of data, many banks continue to face long-standing barriers. Here are a few key challenges that stand out:
Challenge: Legacy systems and past M&As complicate data management
Banks have accumulated decades of siloed infrastructure, often reinforced by mergers and acquisitions, where systems were integrated minimally or not at all. Client records, transaction data, and lifecycle events are typically distributed across front, middle, and back-office platforms. With disconnected systems, core operations, reporting, or lifecycle tracking for complex products become resource-intensive and time-consuming.
Challenge: Poor quality data hinders AI initiatives
As AI advances accelerate across our industry, banks face increasing pressure to ensure their data is accurate, complete, and consistent. Without reliable, well-governed data, even the most strategic and well-funded AI initiatives can risk falling short. Addressing this requires modern tools, unified data architectures, and strong governance practices.
Challenge: Inefficient access to timely data across the organization
Banks often struggle with inconsistent data standards and lineage, with departments and business lines relying on different formats, definitions, and sources. The result is fragmented reporting, discrepancies that complicate aggregation, and erode trust in critical data. At the same time, many institutions remain tied to batch-driven architectures that process data in overnight windows, creating delays and limiting visibility.
Addressing the challenges of fragmented, inconsistent, and delayed data requires more than short-term fixes. Banks need a sustainable foundation that not only resolves today’s issues but also positions the organization to scale for the demands of advanced analytics, growth, transparency, or AI adoption. Three areas stand out as essential building blocks:
Bringing together disparate systems and sources of data is the first step toward unlocking better value from data. A platform-based approach that integrates and normalizes data enables a single, reliable foundation. This limits duplication and enables teams with consistent information to act on across or even within business units.
Quality and trust are non-negotiable when it comes to banking, and data should be no exception. Data governance frameworks that are supported by catalogs, lineage tracking, and automated data quality checks help ensure consistency and accountability.
Once data is unified and governed, the next step is activation. Self-service reporting options, interactive dashboards, and other insights enable teams to turn raw information into actionable strategies. Beyond analytics, effective data egress ensures that trusted data can seamlessly flow into other critical systems such as risk monitoring, client reporting, or other internal applications, so teams can access the information where they need it.
When navigating a data transformation effort, firms typically find themselves in one of two common scenarios, or a blend of both.
The first approach, often spearheaded by a Chief Technology Officer (CTO) or Chief Data Officer (CDO), aims to unify data across the bank through a comprehensive, enterprise-wide overhaul of systems and processes. This comprehensive strategy holds immense transformational potential, aiming to make data a strategic asset embedded in every function, although it may involve longer timelines or resource demands.
The second approach adopts a strategic path of incrementalism, recognizing that large-scale transformation is sometimes not feasible due to time or resource constraints, and that day-to-day challenges often persist within specific functional areas or business units. These teams need timely access to reliable data to manage operations, serve clients, and meet reporting obligations, often long before enterprise-wide solutions can be fully realized. As such, some firms may focus transformation efforts within a line of business or functional area. This more targeted method emphasizes making iterative, small-scale improvements rather than replacing entire systems outright.
Flexible architecture and integration between legacy systems and newer technologies can play a crucial role here, as firms can implement a data layer that serves as an intermediary. These initiatives can also be designed with scalability in mind, enabling integration with future enterprise-wide programs and laying the groundwork for broader transformation.
Ultimately, both approaches have merit. The right path depends on a firm’s priorities, governance structure, and pace of change. Whether pursuing a comprehensive transformation or taking a "think big, start small" philosophy with incremental improvements, success hinges on aligning data strategy with business objectives to drive meaningful impact in both the short and long term.
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