When Data Stops Speaking for Itself in Private Markets
Private markets have spent a decade promising sophistication without always delivering on it at the operational level. Funds are growing in size, structural complexity, and number of investors. Evergreen and semi-liquid vehicles are proliferating. Waterfall structures are being negotiated with ever more layers: differentiated preferred return rates by tranche, carry with multiple IRR thresholds, clawback provisions that can be triggered years after a deal has closed. And while all of that is happening, in many fund administration organizations, the person who understands how that distribution model works is still someone with an Excel file open on their desktop.
That is not a minor anecdote. It is a structural fracture that transforms a knowledge asset into a single point of failure.
The article by Jose Sobrinho, Director of Technology for the United States at Aztec Group, published in Forbes Technology Council, offers a technical diagnosis of how connected reporting and dynamic waterfall models are beginning to replace that fragile arrangement. The central argument is straightforward: data alone does not move a fund forward. What moves it forward is the capacity to convert that data into a decision, in a reasonable amount of time, with sufficient traceability to survive an audit or a difficult conversation with an institutional investor.
That argument deserves to be developed beyond the technical diagnosis, because behind the promise of automation lies an organizational question that many firms are still not asking themselves with full honesty.
The Distribution Model as a Test of Operational Maturity
Few decisions in a private markets fund reveal as much about the maturity of its operational architecture as the way in which distributions are calculated and executed. A waterfall is not merely a financial formula. It is the materialization of the contract between the manager and its investors: the order in which each dollar flows, what conditions must be met before the manager participates in the gains, how those flows are adjusted if the fund enters a recovery phase or if the investor composition changes.
The distinction between European-style and American-style structures illustrates well the complexity that is at stake. In a European structure, the manager's carry cannot materialize until the fund as a whole has returned the committed capital plus the agreed preferred return to all investors. In an American structure, the manager can participate in profits deal by deal, even if other positions within the same fund are in a loss position. Each model creates different incentives, different timelines, and a different risk exposure for the limited partner.
When that calculation lives in a spreadsheet that only one or two people know how to handle with confidence, the problem is not technical. It is one of organizational dependency. The fund may have a brilliant investment team, a solid acquisition strategy, and long-term relationships with its limited partners, and yet be fundamentally fragile at the moment when that person leaves, falls ill, or simply makes a mistake that nobody detects until the money has already gone out the door.
What dynamic waterfall systems promise to resolve is precisely that dependency. The distribution logic ceases to live inside someone's head or in a hidden cell of a model, and instead becomes a codified, auditable rule that can be executed under different scenarios before the decision to distribute is made. The manager can model what happens if a sale closes before year-end, if an investor requests a partial exit, if the fund's IRR drops by a point and a half. That is not just operational efficiency. It is the kind of capability that allows better-grounded decisions to be made under pressure.
The Integration That Nobody Wants to Pay For Until They Need It
The diagnosis on systems integration is where Sobrinho's article touches the sector's most sensitive nerve. The statistic he cites from MuleSoft, indicating that 95% of organizations face difficulties integrating data across systems, surprises no one who has worked close to the operations of a mid-sized fund. What is surprising, if anything, is that this difficulty is accepted so naturally.
In practice, fund administration organizations tend to operate with layers of technology that accumulated through incremental decisions that were individually rational at the time: a fund accounting system from one generation, an investor portal from another, a regulatory compliance module incorporated when the rules changed, and on top of all of that, spreadsheets acting as glue between systems that do not communicate with each other. Each layer has its own logic. The whole is fragile.
The operational consequence is not merely inefficiency. It is the risk of undetected errors, delays in distributions that damage investor relationships, and above all, an inability to respond with agility when conditions change. If the waterfall model is disconnected from the accounting system, any adjustment in the valuation of a position requires a manual reconciliation process. If the investor report does not pull data from the same place as the carry calculation system, there is a real possibility that the numbers the manager sees and the numbers the investor sees will diverge for reasons that nobody is going to want to explain in a quarterly review meeting.
Connected reporting, as the article describes it, aims to resolve precisely that discontinuity. When the accounting system, the waterfall engine, the investor portal, and the compliance modules all share a common data source, the report ceases to be a manual reconstruction of what happened and becomes instead a real-time reading of what is happening. The team that previously spent weeks on the monthly close can dedicate that time to reviewing the quality of the data rather than manufacturing it.
What few firms are calculating with real honesty is the cost of not making that investment. Not the cost of implementing integration, but the accumulated cost of continuing to operate in disconnected mode: hours of work by well-compensated professionals dedicated to reconciliations that a system could perform in minutes, distribution errors that generate legal and reputational costs, delays in regulatory reports that in several countries already carry direct consequences in the form of fines or operational restrictions.
The 68% of executives surveyed by KPMG who stated that integrating risk management systems had a significantly positive impact on decision quality is not merely a metric of technology satisfaction. It is a signal that organizations that have already done that work are making better decisions with the same information as those that have not.
What Artificial Intelligence Cannot Do Without Prior Infrastructure
The third dimension of the article's argument is the one that carries the greatest long-term strategic weight, though it is also the one most easily turned into an empty promise if read without rigor.
The thesis is that artificial intelligence will widen the gap between firms that have clean, connected, and governed data and those that do not. McKinsey estimates that the potential impact of AI-based automation on the cost base of an average asset manager could represent 25% to 40% of that base. That is a figure that deserves attention, but one that also requires a clarification that the article makes with precision: those benefits depend on the existence of a mature data infrastructure.
What is being observed in firms that are investing in advanced automation is that projects fail most frequently when they are applied on top of a non-standardized data environment. Not because the technology does not work, but because there is nothing coherent for it to work on top of. A language model cannot generate a reliable distribution analysis if the input data is inconsistent across systems. An automation agent cannot execute a closing process if the rules governing that process live in implicit decisions that were never documented.
Data maturity is not the result of implementing artificial intelligence. It is the prerequisite for that implementation to produce anything useful. The firms that understand this are investing now in standardizing definitions, mapping data flows, documenting business rules, and building integration layers. Those that are waiting for advanced technology to solve the data problem are postponing it, not avoiding it.
There is a pattern of dependency here that deserves to be named with precision. Many fund services organizations have built their operational reputation on the competence of specific individuals: the senior analyst who knows how to adjust the model when there is a special distribution, the accountant who has the exceptions of each fund's LPA memorized, the reporting team that knows which cells in the Excel file need to be touched manually before the statements are sent to the investor. That individual competence is valuable. The problem is when it becomes the only guarantee that the system works.
Well-implemented automation does not eliminate those people. It changes their role. Instead of being the sole repository of knowledge about how the fund operates, they become responsible for validating, governing, and improving the system that operates the fund. It is a distinction that seems subtle but has direct consequences for scalability, operational continuity, and the firm's capacity to grow without being overwhelmed by complexity.
The Gap Between What a Firm Says It Is Building and What It Actually Ends Up Producing
What Sobrinho's article describes as a practical imperative — that sequence of connecting reports, bringing waterfall calculations into scenario-based operation, and building integration layers on top of what already exists — is correct in its logic. But there is an organizational tension that the roadmap does not address directly, and that determines, in many cases, whether the transformation actually happens or whether it remains a perpetual pilot project.
That tension is the distance between the discourse of operational maturity that many fund services firms use in their sales materials and the real investment they are making to build that maturity internally. It is relatively easy to speak with clients about the importance of traceability, data governance, and the automation of distributions. It is much harder to accept that those same capabilities are precisely what is missing in one's own operation, and that building them requires investing in infrastructure that does not generate immediately visible revenue.
The firms that are advancing most consistently in this direction are not necessarily the largest ones or those with the most generous technology budgets. They are the ones that have been capable of articulating, with internal clarity, what it is costing them to operate the way they are currently operating. Not in abstract terms of efficiency, but in concrete terms: how many hours per close cycle, how many errors detected before they reach the investor, how many regulatory reports that go out on time without someone working a weekend to make that happen.
Once that cost is visible and is owned by someone with the authority to make investment decisions, the conversation about connecting systems and automating waterfalls ceases to be a technology proposal and becomes a decision about operational architecture with measurable financial consequences. That is where transformation begins to carry real weight. Not before.
Private markets will continue to grow in structural complexity over the coming years. Semi-liquid vehicles, regulatory pressure around ESG and transparency, and the diversification of institutional and retail investor profiles within the same fund will multiply the variables that any distribution and reporting system must handle. Firms that arrive in that environment with connected, governed infrastructure will operate with an advantage that accumulates silently. Those that continue arriving with the expert and their model will face a friction that becomes more costly with each growth cycle, until it is no longer invisible.
