Summary
Before evaluating cross-fund performance analytics platforms, private market firms should first establish clear business objectives, document current fund structures and data sources, identify operational pain points, assess integration and data readiness, and align stakeholders across the organization. Completing this groundwork ensures technology evaluations are based on validated requirements, helping reduce implementation risk and improve long-term success.
Growing private market firms eventually reach a point where fund-by-fund spreadsheets stop working: too many funds, investor classes, and fee structures to efficiently manage and update every month or quarter. The instinct is to start searching for cross-fund performance analytics platforms. But real due diligence starts with understanding your requirements and the goals you want a system to achieve. That groundwork is what makes a successful choice – and implementation – possible.
Firms that skip straight to vendor demos often discover mid-implementation that they never agreed internally on what “done” looks like, never fully mapped their own data sources, or never assembled the cross-functional team the project required. That work resurfaces later as delays, budget overruns, or a platform that is ill-suited for your funds and strategies. The cost of skipping it rarely shows up on the original project plan; it shows up three months into implementation as rework.
Use this checklist to get your organization implementation-ready before you start evaluating specific technology solutions in the category. It’s designed to surface the internal groundwork worth doing first, so that whichever tech product you eventually choose, the evaluation is grounded in requirements you’ve already validated rather than a feature list a vendor handed you.
Every implementation eventually gets judged against a definition of success, so it’s worth setting that definition before a vendor is in the room. Are you trying to cut the time it takes to close LP reporting each quarter? Reduce reconciliation headcount? Support a higher fund count without proportional headcount growth? Each of these implies different priorities and different ways of measuring whether the project worked.
Just as important is confirming that the sponsorship and budget behind the initiative match its scope. Cross-fund performance analytics projects touch fund and investor accounting, investor relations, compliance, and often data engineering. A project without a clear executive sponsor tends to stall the first time it needs a cross-team decision. It’s also worth checking that whatever success metrics you set internally are consistent with the commitments you’ve already made to LPs on reporting cadence and transparency, so the two don’t end up in tension later.
You can’t scope a new platform against requirements you haven’t written down. That starts with a complete inventory of what the platform actually needs to support: every fund structure, investor class, share series, and fee or waterfall variant across your book, including the edge cases that don’t fit your standard template.
The same discipline applies to data. Fund administrators, custodians, and prime brokers all deliver data differently, and most firms have never fully documented which service provider delivers what, in which format, on what schedule. That inventory becomes the specification a new platform has to meet, and skipping it is how firms end up mid-implementation discovering a data feed nobody accounted for.
With the current state documented, the next step is being honest about where it actually breaks. Most firms already know, informally, where the painful reconciliations happen, where a spreadsheet error nearly made it into an LP report, or the information demands they cannot practically meet. The goal here is to turn that informal knowledge into a prioritized list of requirements. Those requirements give you clarity on what you actually need a system to solve. Prioritizing matters because not every gap carries equal risk. A slow report is an inconvenience; a fee calculation error is a serious problem. If your firm has had a near miss or an actual reporting error in the past, that incident is worth documenting explicitly as a requirement, since it’s the clearest evidence of where your current process is exposed.
A cross-fund performance analytics solution doesn’t operate in isolation. It needs to integrate with your critical data sources and destinations: fund administrators, custodians, general ledger, and CRM or investor portal. Each point of connectivity carries its own complexity depending on whether data moves via API, file transfer, or manual upload.
It’s also worth taking stock of your data quality baseline before you implement, not after. Inconsistent date conventions, calculation methodologies, or asset class taxonomies across sources won’t go away just because you’ve adopted new infrastructure; they’ll simply resurface as data quality exceptions in the new system unless you’ve planned for the normalization work up front.
Implementations succeed or fail on people and process as much as technology. That means assembling a cross-functional team, typically spanning fund accounting and operations, technology, investor relations, and compliance, with clear ownership and decision rights before the project starts, not once the first disagreement comes up.
It also means planning for change management and training well before go-live, since the teams who will use the new technology daily are rarely the ones sitting in the vendor selection meetings. It also means agreeing on how the transition itself will work. Will you run your legacy process and the new solution in parallel until the numbers are validated, or cut over directly? Both approaches are reasonable, but only one of them fits your risk tolerance and timeline, and that’s a decision worth making deliberately rather than defaulting into.
This groundwork doesn’t disappear once you start evaluating vendors, it becomes the basis for that evaluation. A clear business case tells you which capabilities actually matter. A complete data and fund inventory tells you what a platform has to support. A prioritized gap list tells you where to focus the hardest questions. And organizational alignment tells you whether your firm is actually ready to absorb the change, regardless of which platform you choose.
Tech products purpose-built for this problem are designed around exactly the complexity this checklist surfaces: multiple fund structures, heterogeneous data sources, and cross-functional reporting requirements. But no tech product, however capable, can compensate for skipping the internal readiness work above.
Once you’ve worked through this checklist, you’re in a position to evaluate solutions on the merits, not on how good the demo looks.
Authored By
Lakshay Mehta
Lakshay is a Product Lead for Private Markets at Arcesium. He is responsible for the product roadmap, vision, and front-to-back development, while supporting the go-to-market strategy for this segment. Before Arcesium, Lakshay spent over a decade in front-, middle-, and back-office functions across hedge funds, institutional asset management, and commercial banking as both a financial services professional and product manager.
John Simeone
As Vice President, Product Management at Arcesium, John Simeone serves as a product manager on the PerformA team, Arcesium's fund and investor allocations platform. He engages closely with key clients to understand their workflows, translating their priorities into direction for the product team.
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