This article summarises a roundtable discussion with ten professionals from leading private equity firms in London, focused on the challenges, trends, and strategies shaping portfolio monitoring today.
The portfolio monitoring landscape is evolving rapidly, bringing with it new challenges and heightened expectations. With increased pressure to deliver timely and comprehensive reports to key stakeholders, private equity firms are placing greater emphasis on optimising their existing solutions and strengthening internal processes.
What firms are looking for from portfolio monitoring solutions:
- Enhanced data ingestion and auditability
- Seamless integration with other systems
- Stronger partnerships with vendors
- Clearer visibility into product roadmaps
As the landscape matures, the differences between vendors have become less distinct, making it more difficult for firms to distinguish which solutions best meet their specific needs. However, even small differences can significantly influence the success of implementation and user adoption.
At the same time, the technology stack supporting portfolio monitoring and reporting is expanding rapidly. It’s increasingly important for the core portfolio monitoring tool to integrate effectively with complementary systems such as capital structure, valuations, and ESG tools. When done well, these integrations can deliver substantial value to firms by enhancing strategic insights.
1. Data Foundations: Quality, Upload Discipline & Benchmarking
The challenge of effective benchmarking
Benchmarking remains tricky for private equity firms due to limited comparable data. Firms often turn to third-party providers for anonymised benchmarks, but meaningful insights are hard to achieve.
Key challenges firms face include:
- Lack of comparables: Public data is rarely suitable or directly comparable.
- Sector complexity: Benchmarking becomes especially difficult for investors operating across diverse sectors.
- NDA restrictions: Confidentiality agreements often limit the use of deal data during due diligence, unless it is fully anonymised.
Despite these hurdles, there is growing potential in the use of normalised metrics, which can enable more effective comparisons across companies of varying sizes and sectors. In addition, emerging AI solutions offer promising tools to enhance financial modelling and reporting.
Encouraging consistent data uploads
Ensuring portfolio companies consistently upload accurate data on a timely basis remains an ongoing priority. Firms have adopted several strategies to encourage this:
- Transparency: Regularly sharing portfolio snapshots, exit readiness assessments, and valuation updates directly with portfolio companies to promote transparency and encourage consistent data sharing.
- CFO forums: Firms organise CFO forums to highlight data importance and foster accountability.
- Clear ownership models: Depending on firm preference and workflow efficiency, choose between centralised teams or individual deal-team responsibilities for data acceptance.
2. Connecting the Stack: Warehousing, Forecasts & Valuations
Data Connectivity and Warehousing
Private capital firms increasingly rely on seamless integration with external systems like Anaplan, Dealsplus, Carta, and dedicated ESG tools. Real-time updates to capital structures are vital for accurate valuations, dilution tracking, and ownership analysis.
As reporting expectations grow, many firms are also connecting their portfolio monitoring tools to BI platforms like Power BI. However, a common challenge is that the data uploaded by portfolio companies often requires manual transformation and cleansing before it can be used in dashboards. This step can be time-consuming and resource-intensive.
One solution that firms are increasingly turning to is the use of data platforms. These platforms consolidate multiple data streams – from portfolio monitoring and forecasting to valuations – into a single, structured source of truth. In addition to improving data consistency, this also makes reporting via BI tools more efficient.
Forecasting and Modelling
Dashboards that incorporate scenario analysis and modelling enable firms to effectively monitor forecasted performance against actual results. However, automating the impact of these variances, and other scenarios, on valuations remains a challenge, with many firms still relying on manual, off-system adjustments.
To improve portfolio monitoring, firms should pay close attention to the recurring questions raised by senior stakeholders during portfolio reviews. These often signal critical data gaps and point to additional metrics that need to be systematically captured within monitoring systems.
Valuation Challenges
Integrating capital structure and performance data is critical for accurate valuation modelling. Cap tables, particularly in venture capital settings, can change frequently, requiring close-to-real-time updates to maintain accuracy.
Building detailed valuation waterfalls manually can be highly error-prone and cumbersome. Some firms use a dedicated valuation technology which help to significantly reduces these risks, improving consistency, reducing errors, and providing essential audit trails.
3. Intelligent Automation & AI
AI use cases in portfolio monitoring are still developing. Some firms have introduced internal AI tools that review documents, highlight discrepancies, and provide insightful commentary to support, not replace, human analysis.
Generative AI tools, such as Microsoft’s Co-Pilot, are increasingly used to draft investor communications based on quarterly board materials. These AI-generated outputs tend to improve over time with iterative feedback. Built-in audit trails also provide transparency around data sources and can help surface critical insights that may be missed by human analysts.
Yet significant challenges remain:
- Unstructured data: Converting unstructured data into a structured form remains a challenge.
- Siloed development: AI tools are also siloed when individuals develop their own agents without strategic alignment.
- Accuracy and reliability: Generative AI requires extensive human oversight and multiple refinements to ensure consistent accuracy.
4. Implementation & Change Management
Implementation Strategy
When rolling out new portfolio monitoring tools, firms increasingly favour pilot or “shadow-run” implementations. Starting small, with a limited group of assets, allows teams to refine processes and identify issues before scaling up more broadly.
Clear communication remains critical. Vendors often aim to be accommodating, but may inadvertently agree to poor design choices if clients aren’t explicit. Firms must clearly articulate their needs, while vendors should proactively highlight best practices and common pitfalls.
Data Discrepancy & Alignment Issues
Discrepancies frequently arise between internal monthly management reports, data in portfolio monitoring tools, and formal board packs.
Differences between board packs and portfolio monitoring data are particularly serious, potentially undermining confidence.
Smaller variances between internal monthly reports and monitoring tools may be acceptable, provided they are clearly explained and consistently accounted for.
Change Management & Resourcing
To effectively manage change, firms recommend adopting a quarterly “software release” mindset, incrementally rolling out new KPIs. This approach ensures manageable, ongoing improvement in data tracking and reporting.
Successful user adoption strategies include:
- Early and visible quick wins to build momentum.
- Dashboards backed by senior management to reinforce data importance.
- Structured refresher training to maintain engagement.
- Clear communication of the practical benefits of new tools for portfolio managers and deal teams.
On resourcing, firms differ:
- Some invest in dedicated data scientists to unlock maximum value from their data.
- Others focus instead on empowering existing operational teams, developing a strong internal culture built around clear, evolving data-driven use cases.
Want to drive more accurate portfolio analysis and reporting?
Our portfolio monitoring experts at Holland Mountain can help – whether you’re refining your data strategy, improving reporting, or implementing and optimising software solutions.
Book a conversation with one of our experts to explore how we can support you in building a more efficient, reliable portfolio monitoring function.
