Why Private Markets Need a Modern Tech Stack to Scale in a Data-Intensive Era
Private market tech stacks are almost as complex as today’s capital markets. For those portfolio and fund managers mired in a tech nightmare, decisions on build vs. buy, point solutions or end-to-end, and now vs. later are just a few of the stressors. Some firms that still run legacy operational technology run into problems when it comes to slogging workflows, integration incompatibility, unsynchronized data, and other issues that ultimately cause problems in scaling AUM.
Firms running fragmented data infrastructure encounter problems dealing with the colossal volumes of new data flooding into and out of their systems, failing to keep their information ingestible, auditable, reportable, and accessible to teams who need it. Piecemeal technology acquisition leads to fractured workflows, data silos, and rising integration costs, ultimately slowing growth and increasing risk. Let’s examine how private markets players can alleviate and solve these ongoing struggles.
Private market tech priorities
Investors are not bashful about increasing private market exposure, from pension funds and insurance companies to endowments and foundations, for whom allocations to private debt (+24%) and private equity (+26%) are expected to see the largest net increases over the next three years, extending a three-year trend of rising private exposure.i Managers dealing in alternatives have expressed two distinct priorities: unified data platforms and automated private credit loan workflows.
Firms are urgently seeking data infrastructure that seamlessly shuttles data from different systems into one golden source, rather than routing out to different places. With a centralized data platform, they can accelerate and modernize LP reporting, portfolio analytics, and performance computation, with easily accessible data, all in one place. Firms have a handful of approaches to data modernization at their disposal. Firstly, they can focus on bringing existing data together to create a single source of truth for performance computation, even if they are not replacing their current tech stack. Alternatively, if unable to rip out their existing tech stack, firms can easily replace existing operational data sets, such as current accounting or trade booking systems.
Perils of piecemeal technology stacks
If using piecemeal technology solutions, where more handoffs occur between systems, the process is more prone to error. For example, an operations team uses a system where they generate an output, and that output then must feed into an investment accounting system rather than seamlessly integrating or flowing through one end-to-end system. This necessitates a complex, time-consuming data mapping exercise, as any change in one system requires changes across the entire layer. It's very hard to get that all-important consolidated view. Visibility into portfolios is a premier capability in a time when investment strategies are increasingly complicated with cross-asset class strategies and a variety of innovative vehicles and structures at play. This is particularly critical when dealing with the nuances of private credit strategies.
Speeding over private credit roadblocks
In private loan workflows, credit agreements often exist in PDFs. Someone must read the PDF, set up security, and book transactions such as drawdowns, interest accruals, payments, etc. Firms run their workflows aground when trying to scale securitized pools of loans that come with 1,000 or 10,000 PDFs arriving monthly. Credit research analysts and structured credit teams spend countless hours processing those PDFs, entering them into loan accounting systems, generating the reports, and releasing LP statements.
The explosion of continuation vehicles, evergreen funds, and secondary funds offers yet another complicated curveball to workflows, each bringing distinguishing factors in investment lifecycle events, valuations, and liquidity. Private debt attracted $16.1 billion of secondary capital during Q1-Q3 of 2025, compared with a meager $1.7 billion raised for the full-year 2024.ii Piecemeal private market tech stacks will not suffice for firms in urgent need of data-based workflow automation, harmonization of datasets from disparate sources, and real-time valuations.
Vendor-agnostic infrastructure to scale
Another factor at hand is that popular asset-based finance (ABF) strategies are flooding systems with mountains of disparate data from various alternative lending platforms, originators, and servicers. Managers need the precise extraction and efficient aggregation of data from alternative lending platforms to enable loan modeling for accurate security masters and, ultimately, a real-time view of portfolio performance.
For instance, let’s say a $15 billion-plus firm wants to add $2 billion in separately managed accounts (SMAs). To effectively do so, they move forward in hiring several external managers, each of which has their own systems for trade booking, for reporting accounting, for reporting positions, and P&L, etc. Bringing all these managers' positions together to generate performance or compare them like-to-like at a scalable level will be challenging if not prohibitive for the client's incumbent system. The firm’s critical resources are sinking underwater, with accounting departments stretched too thin to take on more work. This is where vendor agnostic infrastructure and centralized data infrastructure become indispensable.
A single golden source of data allows firms to manage and access all data at once, compare like-to-like, and run smoother operational flows. Firms' ability to scale cannot be dependent on an inflexible tech stack. Any firm that wants to grow should adopt this type of malleable, vendor-agnostic infrastructure.
Taming the investment data mushroom cloud
Aside from the complexity of investment data that comes from today’s elaborate public-private, cross-asset class strategies, there is an honest to goodness data volume problem. The ascent of agentic and generative AI has only magnified the data quantity issue.
“AI agents may soon be able to analyse such vast quantities of structured and unstructured data that they could generate signals offering a meaningful competitive advantage. A specific example given was that AI agents might be particularly well suited to sentiment analysis of investor calls or Fed meetings. While optimism is growing, most AI leaders emphasised that human oversight remains essential. Even among the most advanced firms, Gen AI is viewed as an augmentation tool, not a replacement, for investment professionals. Trade execution and portfolio decisions, in particular, are expected to remain firmly under human control for the foreseeable future.” — AIMA, Charting the course: Lessons from AI leaders in alternative investmentiii
The above quote is commentary from 2022. The volume of data has exploded, with 181 zettabytes expected to be created, captured, copied, and consumed in 2025. This is nearly three times as much as 2020.iv Therefore, the Mount Everest of data from 2022 will likely become a planet of data in 2026. Modern, automated data platforms are not optional. They must be able to gather ABF and non-ABF, public and private data and valuations together to perform portfolio analytics and performance attribution. And they must conquer the unstructured data problem.
For public asset classes, systems like Bloomberg and FactSet already do a lot of data cleanup. However, when it comes to syndicated loans, each loan agent has a different way to report the interest or the payment-in-kind that is paid on the loan it is agenting. Firms are struggling to find systems that can understand the context of that PDF. Further, even if the legacy system understands context, it fails to accurately extract the unstructured data and transform it into structured formats. AI agents are proving particularly useful in the ingestion, normalization, and reconciliation of unstructured data.
As firms aggressively acquire new technologies, the goal of achieving fast, accurate, single pane of glass visibility becomes exponentially harder. Vendor-agnostic infrastructure and unified data platforms are key to automated private credit loan workflows, eliminating data silos, allowing scale, and decreasing risk. These steps allow private market firms to thrive in complexity before the technology stack becomes a liability.
Authored By
Ankit Mittal
Ankit Mittal is a Principal Solution Architect at Arcesium, where he works closely with clients to design and deliver scalable data, accounting, and analytics solutions for the investment management industry. He partners across client, product, and engineering teams to translate complex business requirements into production-ready implementations. With over a decade of experience in investment management technology, Ankit has led complex platform implementations across traditional asset classes, private markets, and digital assets. He brings deep expertise in data architecture and investment workflows and is focused on helping clients operationalize sophisticated analytics with confidence.
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[i] FundFire, December 1, 2025. https://www.fundfire.com/lead/enroll/5031944/703234?
[ii] Pitchbook, December 1, 2025. https://pitchbook.com/news/articles/secondaries-fundraising
[iii] Deloitte, November 29, 2023. https://www.deloitte.com/us/en/insights/industry/financial-services/alternative-data-for-investors-from-discovery-to-institutionalization.html
[iv] BlackRock, 2025. https://www.blackrock.com/aladdin/discover/global-insurance-report-2025-key-takeaways
