Common Inefficiencies in Valuation Preparation and Data Validation

Valuation preparation remains one of the most resource intensive processes in private capital. Even in experienced funds with established methodologies, the cycle of updating models, validating inputs, and preparing committee materials can stretch far longer than expected.

Much of this strain is not caused by valuation complexity itself. It is driven by structural inefficiencies in how data is collected, reviewed, and governed. These inefficiencies compound over time, increasing risk and slowing decision making.

Understanding where friction typically occurs is the first step toward building a more disciplined and scalable valuation process.

Rebuilding rather than refining

In many funds, valuation models evolve incrementally. Adjustments are layered on top of previous versions. New assumptions are added. Market data is copied into updated tabs.

Over time, models become more difficult to navigate and harder to control. Instead of refining a structured framework, teams often find themselves reconstructing large portions of it. Version control weakens. Logic becomes opaque. Institutional knowledge concentrates in a few individuals.

This rebuild approach consumes time and increases dependency on key people rather than on a controlled system.

Fragmented input collection

Valuation preparation depends on multiple data streams. Financial results, forecasts, comparable market data, capital structure information, and qualitative assessments all feed into the final outcome.

When these inputs arrive in inconsistent formats or without clear ownership, consolidation becomes a major task. Teams spend days normalising figures, clarifying definitions, and aligning assumptions before meaningful analysis can begin.

Fragmented inputs delay judgement and increase the risk of inconsistency across assets.

Validation that happens too late

In many processes, detailed validation begins only once draft valuations are nearly complete.

At that point, teams reconcile numbers against financial statements, review assumptions, and confirm comparables. If discrepancies appear, models must be reopened and adjusted, triggering further review cycles.

Late validation compresses timelines and amplifies the impact of small errors. What could have been resolved incrementally becomes a last minute bottleneck.

Manual reconciliation across systems

Valuation inputs often sit across accounting systems, portfolio monitoring tools, market data providers, and spreadsheets.

Moving data between these sources manually introduces friction. Figures are exported, reformatted, and checked line by line. Each transfer point increases the likelihood of delay or inconsistency.

Even when underlying data is accurate, manual reconciliation extends preparation timelines and diverts attention away from analysis.

Inconsistent application of methodology

Firms may define valuation principles centrally, but the application of those principles can vary across assets.

Different discount rates, comparable selections, or forecast treatments may be used without clear visibility at portfolio level. While asset specific judgement is necessary, inconsistent documentation makes portfolio level oversight more difficult.

This variability increases review time and reduces comparability across investments.

Documentation created after the analysis

Explanations of assumptions and changes are often assembled after valuations are calculated.

Rationale is reconstructed from emails and memory. Movements from prior periods are summarised under time pressure. Supporting evidence may be scattered across files.

Reactive documentation slows the process and weakens traceability, making future cycles harder rather than easier.

Compressed review and approval

When upstream steps run late, committee review windows narrow.

Materials are circulated close to meetings. Questions generate rapid back and forth. Adjustments are made under time pressure. This environment increases stress and limits the depth of challenge and discussion.

A compressed approval process does not improve rigour. It reduces it.

The cumulative effect

Each inefficiency may appear manageable on its own. Together, they create structural drag.

Preparation timelines lengthen. Risk accumulates quietly. Key individuals become bottlenecks. And valuation becomes more stressful than it needs to be.

Over time, this affects not only workload but also confidence in the numbers and the speed at which investment decisions can be made.

Conclusion

Common inefficiencies in valuation preparation and data validation are rarely about technical ability. They stem from fragmented inputs, late validation, manual reconciliation, inconsistent documentation, and compressed review cycles.

Addressing these structural issues transforms valuation from a recurring operational strain into a controlled and repeatable process. In a more demanding private capital environment, disciplined valuation preparation supports both accuracy and strategic confidence.

How Untap can help

Untap enables a more structured approach to valuation by centralising inputs, assumptions, and outputs within a single operational environment. Standardised data collection and clear ownership reduce fragmentation, while embedded workflows support earlier validation and consistent documentation. By keeping valuation models connected to portfolio performance data, teams can focus on analysis and judgement rather than reconciliation and rework, strengthening both efficiency and confidence in the process.