Why Data Ownership in Financial Services Defines Modern Transformation
Digital transformation is a term that strikes stress, if not fear, into many C-Suite leaders in investment management. What was once a question of “if” has evolved to a question of “when” to a question of “how” and how much money. Progress is uneven due to ongoing reliance on fragmented legacy systems. Asset managers allocate on average 60% to 80% of their technology budget to run-the-business initiatives, leaving 20% to 40% for change-the-business operations. Most firms have fragmented systems supporting different asset classes, making digitization particularly challenging.i
Success of your platform hinges on more than just the features. There are sensible approaches to transformation initiatives that can set firms up for success, not only now but in the future. Firms should avoid reliance on platform models that lock them into a single vendor, add disruption, come with a heavy integration burden, or restrict data ownership. Instead, firms should adopt open architecture principles that solve 80% of core issues while allowing seamless integration of specialized external tools for edge cases and customized needs, providing the ultimate competitive advantage: data portability, ownership, and scale.
Different approaches to control, speed, and data ownership
There is no silver bullet solution that works across the board. Every business is unique in its own structure. Firms must carefully consider different trade-offs around control, speed, cost, and data ownership when selecting between best-of-breed models, legacy single-vendor platform models, proprietary build models, and modular/composable architecture models. The legacy monolithic enterprise IT platform was the most popular choice for years, especially by large asset managers. Various tech solution providers have successfully promoted the all-in-one platform premise, appealing for its perceived one-vendor simplicity, value, and low technology risk. And naturally, cost is a major factor. Boston Consulting Group found that 3 out of 4 decision makers in IT and business — whether choosing a suite or best-of-breed approach — said that cost is their primary concern. Seventy percent cited adaptability as the most important technical consideration.ii
Cost would seem to point to the one vendor, end-to-end approach while adaptability would point to best-in-breed or modular/composable models. For some sectors, the broad and integrated functionality of all-in-one enterprise platforms suffices. Manufacturing and utilities/energy are industries for which these traditional single-vendor platforms make a ton of sense, so they overwhelmingly choose them (65% and 73%, respectively). But when we talk about telecommunications and banking-finance-insurance, they rarely choose single-vendor, all-in-one systems (35% and 27%, respectively) because “IT systems are not viewed as a commodity but a core operational capability,” per BCG.
Open data architecture: solving the 80% while enabling the edge
For many firms, we recommend adopting open architecture technology that should be maintained for solving a specific set of business problems. All-in-one, centralized platforms and point applications designed to solve one use case do not usually play well with other systems. And, since our industry and the markets are like a runaway train of unrelenting change, firms need systems that are flexible enough that, if a challenge pops up that is not supported, they can integrate it with another solution easily.
Think of your iPhone (if you’re an Apple person). It solves a holistic set of issues, everything from calls, music, and messaging to driving directions, emails, and internet searches. Your iPhone has an operating system (OS) that solves 80% of the issues that we'd turn to our cell phone for. But it also has open architecture, so if you need tools for budgeting or real estate search, there are applications that provide those services which you can integrate and download or build into your ecosystem.
Back to the world of buy side investment institutions: If a firm has a buy side technology platform that solves a particular problem, and it is able to plug in other market data sources or applications on top of its infrastructure, it could solve many edge cases and unique scenarios. This brings us to the new kid in town, the ecosystem model, which offers modern firms malleability, scalability, and data portability for asset managers.
“Given that many applications are often based on vertical silos with a huge amount of legacy technologies, the target architecture has to decouple flexible, customer-facing front ends providing new functionality and data models from the existing core systems or other back-office processing engines. New micro-services therefore have to be designed for specific features that are linked with the legacy solutions through a flexible and open interface architecture. The target IT architecture defines the "playing field" and also provides guidance on how to integrate innovative startup technologies into the landscape.”— Roland Berger, Transforming Financial Servicesiii
The ecosystem model for data ownership
Everything about selecting financial technology ecosystems is a calculation of trade-offs. If a firm is relatively small, they can enlist their resident tech savant to build an application uniquely suited for their business from the ground up. It will come at a lower cost and in a more tailored, custom approach that speaks directly to the business needs of the firm. The issue is that it doesn't grow from the knowledge base of other clients, other applications, and use cases that come into play. It might handle your business today, but it doesn't handle the business of the future. In an ecosystem model, which has open data architecture, “customers are involved in designing the options of their product to fit their needs and desires,” which some refer to as “mass-individualization.”iv
The ecosystem model for data portability and scale
The ecosystem model’s value proposition is its configurability and flexibility to manage any future problem that you may run into. If all-in-one platforms are comprehensive, stretching end-to-end of the enterprise, then ecosystem models stretch end-to-end of the entire sector, as the name implies. It combines platforms, partners, and custom development in a fully transparent, extensible way that prioritizes data ownership. The foundational platform is the operating system, which is completely visible and accessible to anyone that needs information, whether it's the front office, treasury, or risk team.
Beyond that, there will always be edge cases that call for a custom solution. At that point, the firm can mobilize its developers, hire new developers if needed, or hire an implementation partner. Therefore, that foundational platform needs to play well with others or there will be bottlenecks and hangups that thwart progress. The open architecture, API-based infrastructure, and complete data lineage and observability make a smooth integration possible.
A golden source of truth for your data: see it, trace it, integrate it
In this connotation, transparency refers to the capacity to trace data all the way back to its inception to see how it changed and why: observability, full data lineage, full traceability and audibility of this information, even if it's coming from another system. This level of comprehensive transparency means that your tech team is happy because they can use APIs to integrate everything, and it's simplified on their side. Your operations team is happy because they can audit everything when they get questions. The single, golden source of data gives the entire firm flexibility around analytics and reporting, so the front office, management, and investor relations teams have a one-stop shop. That data infrastructure would also play well with others, like Snowflake and Databricks. The vision is to remove all barriers to expansion.
For example, a large hedge fund may have a fund administrator it outsources, a golden security master, an accounting engine that handles accounting, and an outside vendor that uses that accounting system as a managed service. Then, you may introduce a fifth point of connection, which might be the deal team who needs to consume this information to manage the daily underwriting of every investment, using over 200 unique data points that they track on an investment level. If the fund’s operations team is unable to make a switch to a new platform or is content with four systems but needs them to come together to solve one end-to-end problem, a centralized data infrastructure operating model supports these requirements in an ecosystem approach.
The right operating model for today — and tomorrow
The ecosystem model for technology architecture in the financial industry is driven by the need for open architecture, data centralization, and flexibility in response to growing asset complexity, fee compression, and competitive pressure. Asset level complexity is growing with more firms adopting digital assets and more complex private credit strategies and structures like asset-based finance (ABF).
The goal is to find the operating model that really suits your team in its current state, while staying ready for the opportunity that's afforded to you as you continue to grow in complexity and scale the team, the assets, and the investor base. Data portability ensures firms can adapt to new asset classes, vendors, and strategies without re-platforming. It’s your data, the best asset to drive alpha. The freedom of information afforded by the ecosystem model is your ultimate competitive advantage.
Authored By
Bernardo Cabada
As Vice President, Sales Operations & Enablement at Arcesium, Bernardo leads in showcasing the company's cutting-edge capabilities through executive-level engagements, delivering compelling presentations, technical demonstrations, and proof-of-concepts. His proficiency extends across crucial areas of middle- and back-office investment operations, with a particular emphasis on data governance and enterprise data management for investment managers and asset owners.
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[i] McKinsey, July 16, 2025. https://www.mckinsey.com/industries/financial-services/our-insights/how-ai-could-reshape-the-economics-of-the-asset-management-industry
[ii] BCG, August 5, 2025. https://www.bcg.com/publications/2025/seven-questions-smarter-applications-strategy
[iii] Roland Berger, December 2019. https://bit.ly/4rr8AZf
[iv] Y. Koren, S.J. Hu, Peihua Gu, M. Shpitalni, July 2013. https://www.sciencedirect.com/science/article/abs/pii/S0007850613002023
