Perspectives · Market Structure
Enterprise Cost or Startup Risk? The Private Markets Data Dilemma
Enterprise data platforms are built for firms ten times your size, and spreadsheets get riskier with every fund. How to escape the squeeze.
· 11 min read
If you run data and operations at a private markets firm managing $10 billion in AUM or less, it probably feels like you are being squeezed from both sides.
On one side, established data platforms are selling enterprise solutions built for firms ten times your size. The license fees, implementation timelines, and change management overhead are calibrated for organizations with dedicated data teams, large technology budgets, and months of runway to deploy.
On the other side, doing nothing means continuing to run on spreadsheets, email chains, and PDF reports that grow riskier with every new fund, every new portfolio company, and every new LP reporting request.
The cost of established solutions is large, but the risk of doing nothing is also large, and the gap between the two is where many private markets firms are.
This article breaks down what that squeeze looks like in practice, why it is getting tighter, and what you can do about it today without signing a vendor contract. For firms that reach the limit of what makeshift solutions can do, there is a path forward.
The Enterprise Side of the Squeeze: Solutions Built for Someone Else
Walk into a demo of an enterprise investment data platform and the first thing you will notice is the price tag. Enterprise data solutions in the investment management space are designed for large institutions with complex multi-fund structures, cross-jurisdictional reporting requirements, and dedicated operations teams. Licensing models reflect that, implementation cycles run six to eighteen months, and change management requires steering committees, stakeholder alignment, and phased rollouts.
If the person who evaluates data infrastructure at your firm is the same person who builds the LP reports, then there is no dedicated data team and no six-month deployment window. The decision-maker is a practitioner who needs something that works with the existing stack, not a multi-year transformation program.
Deloitte's Investment Management and Wealth Technology Information Survey found that when it comes to acquiring data tools, 42% of firms prefer a hybrid approach, 33% prefer to buy, and 25% remain agnostic. The driving factors are cost, integration complexity, and the ability to scale1. For mid-market firms, cost and integration complexity are not abstract concerns. They are the difference between moving forward and staying stuck.
Many enterprise platforms are genuinely capable. The problem is not that they are bad products, but that there is a positioning mismatch. When a platform is built and priced for a $50B+ institution, a $5B firm ends up paying for capacity it doesn’t need, features it won’t use, and an implementation model it can’t sustain. The total cost of ownership over a three-to-five-year horizon often exceeds what a firm at that scale can justify, especially once the internal time and effort required for maintenance are factored in.
Deloitte's research on data-driven strategies for alternatives and private equity firms makes the stakes clear. Firms with strong AI, data management, and architecture foundations are already outperforming their peers by 15 to 20% in portfolio value creation2. The firms capturing that advantage are not necessarily the ones with the biggest platforms. They are the ones with data infrastructure that matches their scale and their pace.
The Startup Risk Side: Spreadsheets, Shadow Books, and Fragile Workarounds
If the enterprise side of the squeeze is about overpaying for scale, the other side is about underestimating risk.
The Financial Times reported that despite the growth of private markets, much of the infrastructure underpinning the industry belongs to an earlier era. Excel spreadsheets, email, and PDFs remain the most-used tools in private markets data workflows, a reality that has not meaningfully changed in a decade3. Nasdaq's analysis of private markets data expansion echoed this finding, noting that many organizations managing multi-billion-dollar alternative portfolios still rely on quarterly PDFs, fragmented GP portals, and spreadsheets, creating a widening gap between the scale of opportunities and the information available to act on them4.
This is not just inefficient; it is dangerous. Academic research on spreadsheet errors is sobering. Raymond Panko, a leading researcher on spreadsheet errors, has documented that cell error rates in spreadsheet formulas range from approximately 0.8% to 1.8%, and that in large spreadsheet models, roughly 50% contain at least one material defect56. In a private markets context, where waterfall calculations, LP allocations, and performance attribution all live in spreadsheets, a single formula error can cascade into misstated returns, incorrect capital calls, and damaged LP relationships.
This undermines trust. Industry publication The Drawdown reported that even in 2026, one of the hardest problems in private equity is trust in data. Firms run shadow books because they do not fully trust the outputs coming from fragmented systems7. The shadow book’s informal set of records is maintained outside the official system because the official system cannot be relied upon. It is a workaround born of necessity, and it compounds the problem by creating yet another data source that must be reconciled.
Chief Investment Officer, reporting on a survey by the Private Markets Forum, found that 45% of senior operators identified building a unified data model across systems as their single biggest operational challenge8. The same article noted that front-, middle-, and back-office teams are often working from different representations of the same exposures, making even basic risk questions difficult to answer with confidence.
Deloitte's research reinforces this picture. Many firms cannot reliably collect and integrate trusted data from their portfolio companies and third parties because of limited infrastructure capabilities, data management process challenges, and limited data governance. Manual data collection and review processes are not scalable, and firms often lack executives accountable for data, governance, and analytics9.
The compounding effect is that every new fund commitment, every new portfolio company acquisition, every new LP onboarding adds another data source, another reporting format, another spreadsheet to maintain. What starts as a manageable workaround grows into an operational liability. The risk does not always grow linearly but can grow exponentially with complexity.
Why the Squeeze Is Tightening Now
This is not a static problem. Several forces are converging to make the cost of inaction rise.
LPs want more, faster. Alpha FMC has argued that infrastructure in alternatives is evolving from a back-office afterthought to a core value lever. As retailization in alternatives deepens, secondaries gain momentum, and allocators demand more transparency and frequency, the pressure on operating models is intensifying10. LPs who once accepted quarterly PDF reports now want more granular, more frequent, and more reliable data. Meeting those expectations with manual processes is becoming untenable.
Regulatory pressure persists. In August 2023, the SEC adopted the Private Fund Adviser Rules, which would have required SEC-registered advisers to provide investors with quarterly statements detailing fund fees, expenses, and performance, along with annual financial statement audit requirements11. A federal court vacated these rules in June 202412, but the regulatory direction is clear. Transparency expectations are not going away. Even without the specific rules in force, the industry is moving toward more detailed reporting and disclosure, and firms that cannot produce clean, auditable data on demand will find themselves at a disadvantage.
AI readiness requires data readiness. Celent, in its research on private markets data management, found that while private market firms have commendable aspirations for expanded use of AI, a "walk before you can run" dynamic is evident. The essential data management foundations required for large-scale AI and machine learning deployments have not yet reached a sufficient level of maturity across many areas of private markets13. Firms that want to use AI for portfolio analytics, deal sourcing, or operational automation will find that fragmented, unstructured data makes those investments unreliable. AI is only as good as the data beneath it.
The performance gap is widening. Deloitte's research indicates that firms with strong data foundations are already outperforming peers by 15 to 20% in portfolio value creation2, and the gap is growing. Firms that invest in data infrastructure now are pulling ahead. Firms that delay are falling further behind with each reporting cycle.
Alpha FMC's analysis of data control and AI enablement in private markets makes the case directly: an outdated technology infrastructure can severely restrict a firm's data management capabilities. Legacy systems often lack the scalability and features necessary for modern practices. As AI-driven analytics shape private markets, firms need infrastructure that supports AI workflows, from data processing to real-time analytics14.
Collecting data and surviving reporting cycles used to be the objectives, but now it is to turn data into action. Firms that cannot do that are losing ground to competitors who have already made the shift.
AI is only as good as the data beneath it.
What Mid-Market Firms Can Do Today
You do not need to wait for a vendor to start reducing risk. Here are practical steps that any private markets firm can take immediately, using existing tools and internal resources.
1. Standardize Your Data Definitions
Document a single definition for every key metric (NAV, EBITDA, distributions, exposure, fee accruals) and share it across your team and with your portfolio companies. The most common source of data inconsistency is not a technology problem; it’s a definition problem. The same term can mean subtly different things to different people, and those differences compound across funds and reporting periods.
2. Build a Data Inventory
List every data source your firm touches: fund administrators, custodians, GP portals, portfolio company financials, market data vendors, internal spreadsheets. For each source, note what data it provides, how it arrives (PDF, Excel, API, portal), how often it arrives, and who is responsible for it. You cannot fix fragmentation that is not mapped.
3. Add Validation Checks to Critical Spreadsheets
If your waterfall model, LP allocation tracker, or performance attribution spreadsheet is business-critical, build in automated checks. Use Excel's data validation features. Add cross-foot checks that verify totals. Have a second person review formula logic quarterly. Research shows that spreadsheet errors are pervasive and that structured review catches a significant portion of them5.
4. Prioritize Your Highest-Risk Workflows
Not every spreadsheet needs to be replaced at once. Start with the workflows where an error would be most damaging: LP capital calls, waterfall calculations, valuation reports, and regulatory filings. Focus attention there first.
5. Set Internal Data-Collection Deadlines
One of the biggest sources of reporting delays is waiting for data that arrives late and in inconsistent formats. Set and enforce firm internal deadlines for portfolio company data submissions. The discipline of timely, structured data collection reduces the last-minute scrambling that introduces errors.
These steps will not solve the problem entirely. They will reduce risk, improve consistency, and buy you time. However, they depend on discipline, individual knowledge, and manual effort, and they do not scale. At some point, the data complexity outgrows what spreadsheets and good intentions can handle.
When Makeshift Isn't Enough: A Purpose-Built Path Forward
For firms that have reached the limit of what spreadsheets and manual workarounds can do, the question becomes how to move to a real data platform without taking on enterprise-scale cost and risk.
This is where Aquata comes in. Aquata is the investment data platform from Arcesium, built specifically for the complexity of investment data. It consolidates fragmented data from every source into a single, unified layer, so your team stops chasing and cleaning data and starts putting it to work. For private markets firms, that means ingesting capital call notices, portfolio company financials in Excel, and valuation reports in PDF, normalizing all of it, and giving your team a single source of truth they can trust and act on.
What makes Aquata different for a mid-market firm is the adoption model. Aquata is part of the Arcesium product suite, and Arcesium is an established technology provider in the investment industry with a track record of serving investment firms. This is not a startup bet. Arcesium has done this before, at scale, across the industry.
Aquata also offers flexible licensing options. You can try Aquata before you license it, through a sandbox environment or a proof of concept. That means you can evaluate the platform against your actual data and workflows before committing. Aquata can onboard clients quickly, so you get value fast rather than waiting through an elongated implementation.
Aquata transforms fragmented data into unified, governed data you can act on. There is no enterprise cost or startup risk. Aquata is a purpose-built investment data platform from an established provider, with flexible adoption and pricing that meets you where you are.
Aquata can help you make all your investment data accessible, actionable, and AI-ready without being stuck on either end of the squeeze.
Sources
- 1.Investment Management & Wealth Technology Information Survey, Deloitte deloitte.com.
- 2.Private Company Outlook: Digital Investment, Deloitte deloitte.com.
- 3.Are private markets ready for the data age?, Financial Times ft.com.
- 4.Private Markets Data Expansion: What LPs and GPs Need to Know, Nasdaq nasdaq.com.
- 5.Spreadsheet Development Error Experiments, Raymond Panko panko.com.
- 6.Spreadsheet Errors: What We Know. What We Think We Can Do, Raymond Panko (arXiv) arxiv.org.
- 7.Why a dedicated private markets ERP is long overdue, The Drawdown the-drawdown.com.
- 8.Private Markets Don't Need More Data; They Need Data That Connect, Chief Investment Officer ai-cio.com.
- 9.Data-driven strategies: The winning edge in private equity, Deloitte deloitte.com.
- 10.The infrastructure behind alternatives: why the back- and mid-office is the next frontier for private equity investment, Alpha FMC alphafmc.com.
- 11.SEC Enhances the Regulation of Private Fund Advisers, U.S. Securities and Exchange Commission sec.gov.
- 12.Private Fund Advisers; Documentation of Registered Investment Adviser Compliance Reviews, U.S. Securities and Exchange Commission sec.gov.
- 13.Overcoming Fractured Data Chains and Achieving Operational Brilliance in Private Markets, Celent celent.com.
- 14.Data control and AI enablement: Driving success in private markets, Alpha FMC alphafmc.com.
