Enterprise AI: Build or Buy?
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Traditional ETL pipelines and canonical data models fail in capital markets because different teams require different, equally valid views of the same data. Federated data abstraction enables transformation on demand, delivering portfolio insights faster while reducing governance bottlenecks and operational risk.
Capital markets firms do not have a data problem. They have a transformation problem. The same raw data gets extracted, transformed, and loaded into separate destinations dozens of times because no one can agree on a single "correct" view.
The phrase “digital transformation” has been oversaturated in technology circles over the last decade, but it’s never been more appropriately assigned than to describe what’s needed in capital markets.
According to a 2024 DATAVERSITY survey, 68% of organizations now cite data silos as their top concern, up seven percentage points from the prior year. In capital markets, that number understates reality.
As Mark Freeman states in his piece, Financial Data Quality: Modern Problems and Possibilities, the typical mid-size financial institution manages between 85 and 120 terabytes spread across an average of nearly 14 siloed systems, many built on legacy infrastructure that dates back 15 to 25 years.
The typical response is to convene a committee, establish a canonical data model, and force everyone to conform. This approach has a near-perfect failure rate. The solution is not to pick a winner and force everyone else to adapt. The solution is to stop transforming data at rest and start transforming data on demand.
Let’s take a look at what this means in practice.
When multiple stakeholders need different views of the same data, traditional architectures force political battles over whose transformation logic becomes canonical. These battles consume enormous organizational energy and rarely produce satisfactory outcomes. Freeman's analysis of financial data integration found that integration errors alone account for 42.7% of all data quality issues across the industry. That is not a technology failure. That is a structural failure baked into how firms architect their data pipelines.
Portfolio managers want to see pending trades reflected immediately, not just settled positions. Speed matters more than precision in contexts where decisions happen in minutes. JP Morgan's 2025 trader survey confirmed this, stating across all product categories and regions, real-time data and analytics ranked as the single most valued capability for trading desks.
Then you have risk teams that need those same positions stressed against multiple scenarios. To make things more interesting, the back office has its own requirements. Operations need positions reconciled against custodian statements. They care about settled quantities, corporate action adjustments, and trade-date versus settlement-date accounting.
Compliance sits across all three, needing positions mapped to regulatory hierarchies, concentration limits, and reporting taxonomies that shift whenever regulators update their frameworks. Every one of these perspectives is legitimate. Every one requires different transformation logic applied to the same underlying data.
The enterprise data model project starts with good intentions and ends with governance paralysis. A canonical model assumes there is one correct representation of any given data element.
Capital markets positions have multiple correct representations depending on who is asking and why. A convertible bond can be viewed as a fixed income instrument, an equity derivative, or a hybrid. Forcing a single classification creates downstream problems for whichever team's perspective was not chosen.
This is not an abstract concern. The shift to T+1 settlement in North America has made it painfully concrete. Firebrand Research's 2025 study, conducted in collaboration with DTCC and Euroclear, found that 21% of settlement failures in 2024 were caused by data issues, including incorrect or stale standing settlement instructions.
When your canonical model takes six months to update and the settlement window just shrank from two days to one, the governance bottleneck becomes a financial penalty. Swift Institute research puts this in stark terms, stating banks and brokers now face roughly 80% less time to manage cross-border settlements under T+1 due to the added complexity of time zones and foreign exchange challenges.
Schema rigidity compounds the problem. Capital markets instruments evolve faster than committees can deliberate. New asset classes, new counterparty structures, new regulatory requirements. By the time a change to the canonical model is approved, the requirement has shifted again.
Research by McKinsey shows that financial institutions could reduce operational costs by up to 20% through better data integration and automation. The problem is that "better integration" through canonical models demands solving the politics before delivering any value. And in capital markets, the politics never get solved because the competing requirements are all legitimate. What can you do to overcome this?
The firms breaking free from this trap have discovered that the answer is not creating one view. It is creating a layer that can produce any view from the same underlying data.
Intelligent data abstraction separates what the data is from how it is presented. The concept extends naturally into what the industry increasingly calls a knowledge fabric, essentially a continuously learning semantic layer that connects and contextualizes data across silos without forcing consolidation.
Unlike static canonical models, a knowledge fabric adapts dynamically as the enterprise evolves. It preserves the semantic and operational context that traditional ETL pipelines lose in translation. Transformation logic can be versioned and audited. Historical queries apply historical transformation rules. New regulatory requirements do not demand re-extracting and re-transforming years of historical data.
The speed advantage matters more than most firms realize. Capgemini's 2025 capital markets trends analysis identified landscape fragmentation and legacy technology as the defining operational challenges for the industry. Firms that can define new data views in days rather than months hold a structural advantage, particularly as regulatory timelines compress and market complexity accelerates.
PwC's 2025 banking outlook reinforced this, noting that banks "can no longer sustain legacy systems alongside modern technology" and that the institutions harnessing AI and data effectively will be the ones that transform operations and elevate outcomes.
Intelligent data abstraction unlocks capabilities that are architecturally impossible with traditional pipeline approaches. And the most valuable of these capabilities compound over time.
An abstraction approach defines new transformation logic against existing data, deploys immediately, and produces compliant output without touching the upstream data extraction infrastructure. Given that compliance spending at some financial firms has increased over 60% since the 2008 financial crisis, the ability to absorb new requirements without proportional cost increases represents meaningful competitive advantage.
AI readiness is where abstraction delivers its most forward-looking value. Machine learning models require consistent, clean input features. An abstraction layer provides stable interfaces even as source systems change underneath, which means feature engineering happens in the semantic layer rather than in model code.
This is the foundational architecture that enables firms to deploy enterprise AI solutions that actually work with their data as it exists, not as some future data consolidation project imagines it will exist someday.
The firms still running 18-month data integration projects are not building competitive advantage. The question for every capital markets CIO is straightforward. Will your firm respond to increasing data complexity by adding more duct tape to existing pipelines?
The abstraction layer is not a feature. It is the foundation that determines whether your firm can move at the speed the market now demands. Federated enterprise data abstraction is how capital markets teams stop fighting transformation wars and start delivering the purpose-built views that each stakeholder needs.
Let Unframe help you deploy federated extraction architectures to deliver portfolio data in hours, not months. Click here to see a demo.
