The Hyper-Modular Backbone: Flexible Infrastructure for Investment Firms
Investment managers are under pressure from every direction. The front office wants to expand into private credit and asset-based finance, operations teams want to onboard new lending platforms, and investors have hard due diligence questions about how firms run. At the same time, many firms still rely on Excel and platforms that are at or beyond the end‑of‑life, even as strategies keep evolving. In a Software Improvement Group study, 37% of legacy-based systems had a below-average architecture rating, more than three times higher than systems using modern tech.i
The technology story is now in its fourth stage. First came the monolithic platforms followed by service-oriented architecture, then more granular APIs, or microservices. Now we have reached a place where the talk is about “hyper-modular” architectures. In other words, we are headed to composable business apps built on a unified data fabric.
One way to think of the hyper-modular approach is as an intermodal freight network for your data. You standardize the containers and the track gauge so you can move from ships to rail to trucks. In investment management, you want to be able to transport instruments, positions, transactions, and risk measures from system to system without rebuilding every route. That backbone lets you replace the most brittle junctions first, such as an end-of-life platform or a risky Excel-driven workflow. You can decide later when to bolt on the next module, rather than a full-on, ground-up migration.
The current reality
Excel still sits at the center of many operating models. Almost 60% of finance leaders rely on spreadsheets as their primary automation tool.ii Critical processes run through spreadsheets with complex logic and reconciliations that only a few people understand. From a governance perspective, those workbooks are not as visible or auditable as they should be. We also see classic key-person risk: Someone is responsible, say on business day five, for pulling data and updating the file, and if that person is out, something breaks.
On the other hand, data that is updated in Excel may not have the powerful auditing capabilities that capture attribute value history along with critical metadata including who made the update, the type of database operation performed, and multiple date dimensions that distinguish between when information was known versus when it became effective.
Alongside Excel, many investment managers still run core workflows on platforms that are clearly at or near their end of life. These systems continue in daily use even though everyone knows that vendors or internal teams won’t upgrade them. A widely cited study said nearly six out of 10 banking leaders surveyed consider legacy infrastructure their top growth challenge.iii They often sit directly between teams. Every time positions, cash, or loan tapes need to move between portfolio management, operations, and accounting, someone must pull flat files, run macros, re-key fields, and load them in a very specific sequence just to get the system to accept them.
These multiple manual steps to get information in and out are costly. Instead of closing the books on business day one or two, they add extra days simply to reformat data for legacy platforms. It causes persistent operational bottlenecks that everyone works around, but no one really controls.
Taken together, Excel dependencies, end-of-life platforms, and manual handoffs create a chain of informal junction points that slow the journey from investment decisions to downstream activities like accounting, treasury, and reporting — exactly where a more intermodal backbone for moving data can make a difference.
To untangle those bottlenecks without a big-bang platform swap, we need an operating model that treats core systems as swappable cars running on a shared set of tracks.
Standard containers, shared tracks
Hyper-modularity means an operating backbone where each component can be assembled and reassembled to fit the needs of the firm. It applies both to technology architecture and to the fundamental approach to data. We start by bringing various differentiated data together and mastering it. This provides a unified record of how it arrives, when it arrives, when it gets updated, and who updates it.
That mastered data core becomes the shared asset that PM, quant, risk, operations, and back-office teams all rely on. On top of it, we layer modules such as accounting, reconciliations, treasury, asset servicing, reporting, and analytics. The mastered data backbone is the rail network and the standard track gauge. Containers for core business concepts like asset classes, portfolio reference data (for precise transaction categorization and classification within portfolios utilizing an omnibus structure), counterparties, and risk measures can move from one service to another without needing to be repacked at every stop. Each new module plugs in with minimal rework instead of demanding a bespoke integration or yet another Excel bridge.
The advantage is that we can start with a single, well-defined component that addresses a critical pain point and build from there, rather than forcing an “all or nothing” platform decision on day one. In private credit, for example, we increasingly see managers use this backbone to focus first on their data management platform, which often sits right at one of those brittle junctions. By standing up a data management platform, especially one that is comprehensive and purpose-made on top of the shared backbone, they solve high-risk issues like harmonizing private and public investment data, cash movements, performance track records, commitments, contributions, and distributions, while also creating a pattern for what comes next. The same containers and tracks can then support new modules in both cash and administrator reconciliations, investment accounting, treasury, or investor reporting as priorities and budget allow.
Operating model benefits and a staged roadmap
One of the biggest advantages of a backbone approach is stronger governance and clearer oversight. Too much operational logic still sits inside workbooks that aren’t as visible or auditable as they should be, and that becomes a real issue as strategies expand. A mastered data core includes transparent lineage from the moment data arrives through to reporting, with clear ownership and documented control points.
Investors and financing partners also look closely at how resilient the operating model really is. They want to know they are making a sound investment, not only in terms of generating alpha, but also from a risk perspective. They want confidence that the investment operations architecture will not buckle under increased volume, new asset classes, or unexpected events. As strategies grow more complex, the ability to show clean data flows, clear ownership, and a stable operational backbone becomes a differentiator with allocators and lenders.
The case for staged adoption
Firms like being able to start small rather than drinking from a fire hose and modernizing everything in one fell swoop. From a budgeting and cost perspective, it is far easier to begin with one clearly defined problem and solve that issue head-on without ripping out your entire architecture.
Once that immediate junction is fixed, the next one usually reveals itself. A new component goes live, and suddenly, teams realize that moving data into the next system is slowed by rigid technology downstream. That becomes the natural next candidate for replacement. Rather than adding people or building manual workarounds, we can look at the true cost of keeping the old step and decide whether to move to a module that takes the mastered data directly and passes it cleanly to the next stop in the journey.
This creates a practical, low-risk path forward: Address one burning issue, strengthen the backbone underneath it, and let each upgrade clarify the next place to modernize. Over time, this approach builds toward an operating backbone designed to evolve with the firm. Everyone is aligned around orchestrating and harmonizing decisions and data across the enterprise. In addition, we avoid carrying everything at once, avoid inflating total cost of ownership, and avoid long, disruptive implementation cycles. This step-by-step transportation planning becomes an ongoing model for modernization.
Authored By
Phillip Bodenstab
Phil joined Arcesium in 2024 after 16 years at FactSet Research Systems where he focused on specialty sales of investment portfolio performance, market sensitivity and risk analytics for insurers and asset managers. At Arcesium, Phil partners with sales teams on acquiring new clients as well as retaining and expanding existing client relationships through technical demonstrations of Arcesium's trade lifecycle management and domain-aware data platform solutions.
Share This post
[i] Software Improvement Group, 2025. https://www.softwareimprovementgroup.com/blog/legacy-technology-in-financial-services/
[ii] The CFO, 2024. https://the-cfo.io/2024/11/21/spreadsheets-forever-58-of-finance-leaders-choose-excel-over-ai/
[iii] Deloitte, 2025 (citing a 2023 report). https://www.deloitte.com/us/en/insights/industry/financial-services/financial-services-industry-outlooks/banking-industry-outlook-2025.html