Published 29 June 2026 · 12 min read
What PE analytics software actually needs to do
Private equity analytics is not generic business intelligence. The data is illiquid, irregular, and arrives in formats that a standard BI tool was never designed to handle. Capital call notices in PDF. Portfolio company KPIs in spreadsheets that change structure every quarter. NAV packs from fund administrators with different naming conventions for the same fields.
Before you look at any product, it is worth being clear about what you actually need an analytics layer to do in a PE context:
- Portfolio performance measurement across IRR, TVPI, DPI, RVPI, with the ability to cut by vintage, strategy, geography, and sector.
- Investor reporting that meets ILPA standards or your own LP reporting templates, without manual assembly every quarter.
- Cash flow analysis covering capital calls, distributions, and liquidity forecasting across funds and vehicles.
- Multi-currency consolidation with proper FX handling at the transaction, fund, and portfolio company level.
- Vintage year benchmarking so you can compare fund performance against relevant peer groups and market indices.
- Exception and outlier detection to surface data quality issues, NAV discrepancies, or portfolio company KPI movements that fall outside expected ranges.
The difficulty is that most of these requirements sit downstream of a more fundamental problem: getting the data into a clean, structured, governed state in the first place. That is where the real differences between products emerge. Some tools assume you have already solved the data problem. Others try to solve it for you. A few try to do both.
The landscape in 2026
The PE analytics market has consolidated significantly over the past three years. FactSet acquired Cobalt (formerly Chronograph). BlackRock owns eFront. Allvue has expanded from its deal management roots into a broader platform play. Meanwhile, newer entrants like 73 Strings have come at the problem from the valuation side, and firms like daappa have built analytics as part of a broader data and operations stack.
What follows is an honest assessment of seven products. We build one of them, so take our view of our own product with the appropriate scepticism. But we have tried to be fair about where each competitor is genuinely strong, because if you are reading this, you are probably making a decision that will affect your operating model for years.
How the main options compare
FundCount
FundCount's approach is accounting-first. It combines a real-time general ledger with investment management in a single system, built around what they call "continuous accounting." The GL supports multi-currency, multi-book (IFRS and GAAP simultaneously), and handles complex partnership structures including waterfall calculations and nested entity hierarchies.
Where FundCount is particularly strong is in firms that want one system for both accounting and reporting. It extracts data from fund manager statements, runs partnership accounting, and generates personalised investor statements through its Advanced Report Set. For a single-system shop, it is hard to beat.
Strengths
- True accounting engine with real-time GL, not a reporting overlay
- Multi-currency, multi-book (IFRS/GAAP) natively
- Strong partnership accounting with waterfall calculations
- Integrated investor portal tied directly to accounting data
Limitations
- Analytics are reporting-from-accounting, not a standalone BI layer
- If you already have a fund accounting system you are happy with, adopting FundCount means replacing it
- Less suited to firms that need analytics across multiple accounting sources
Allvue Systems
Allvue covers the widest functional breadth of any platform in this list. It spans GP workflows, LP reporting, portfolio monitoring, and fund administration in a single platform. The portfolio monitoring module includes dynamic dashboards, an IRR Hub for performance forecasting and scenario analytics, and automated KPI collection from portfolio companies with budget-to-actual consolidation.
The trade-off with breadth is depth. Allvue's analytics capabilities are solid but rely heavily on standard integrations with Power BI and Excel for more advanced analysis. If your team already lives in Power BI, that is fine. If you want analytics that work out of the box without a BI project, the experience is more limited.
Strengths
- Broadest functional coverage: GP, LP, fund admin, deal management
- IRR Hub with scenario analytics and performance forecasting
- Automated portfolio company KPI collection
- Multi-fund, multi-strategy support across the platform
Limitations
- Analytics depth relies on external BI tools (Power BI, Excel)
- Breadth of the platform means each module is not always the deepest in its category
- Implementation can be substantial given the scope
Chronograph (now Cobalt, a FactSet company)
Chronograph, rebranded as Cobalt after the FactSet acquisition, is primarily a monitoring and reporting tool. Its strength is in LP-focused workflows: "push-button" reporting that refreshes Word and Excel deliverables from live data, flexible benchmarking, scenario analyses, and ESG data collection. The newer "Chrono AI" capability lets LPs synthesise large data sets for pattern recognition.
The focus is monitoring, not data management. Cobalt works well when the data feeding it is clean and structured. Where things get harder is when GP data feeds are inconsistent, because the platform does not have a deep extraction or data governance layer to clean up what comes in.
Strengths
- Excellent LP-focused monitoring and reporting workflows
- "Push-button" report generation from live data
- Strong benchmarking data, bolstered by FactSet's broader data assets
- Serves all private capital strategies, not just buyout
Limitations
- Primarily a monitoring tool, not a full analytics or BI layer
- Relies on GP data feeds being clean and consistent
- Less suitable for GPs or fund admins who need to govern and transform data before analysing it
eFront (BlackRock)
eFront is the enterprise-grade option. Backed by BlackRock and integrated with Preqin's benchmarking data, it offers the deepest data set for performance comparison in the market. eFront Insight uses machine learning for automated visual analytics drawn from data on over 14,000 funds. The Whole Portfolio View lets institutions see public and private allocations on one platform.
The trade-off is scale and complexity. eFront is built for large institutional investors, pension funds, and sovereign wealth funds. For a mid-market GP or a fund administrator running 20 funds, it is likely more platform than you need, and the implementation and pricing reflect that.
Strengths
- Deepest benchmarking data in the market (Preqin integration, 14,000+ fund data set)
- ML-driven analytics via eFront Insight
- Public and private portfolio consolidated view
- Enterprise-grade security, compliance, and audit trail
Limitations
- Enterprise pricing and implementation timeline
- Overkill for mid-market GPs and fund administrators
- Six modular products can mean a long evaluation and onboarding process
Cobalt GP (FactSet)
Not to be confused with the Cobalt rebrand of Chronograph (both are now under FactSet), Cobalt GP is the GP-facing side of the platform. It focuses on self-service portfolio monitoring with in-platform analytics: IRR, TVPI, DVPI, RVPI calculations, track record analysis, PME benchmarking, scenario modelling, and deal scoring. It also automates KPI collection from portfolio companies using templates.
The user experience is notably more accessible than some of the heavier platforms on this list. Over 100 firms are deployed. The limitation is that it is monitoring-first. It is not a governed data platform, and it needs clean data feeds to work well.
Strengths
- User-friendly, self-service approach to portfolio analytics
- Good mid-market GP fit without enterprise-scale overhead
- Solid PME benchmarking and scenario modelling
- Established with 100+ firm deployments
Limitations
- Monitoring-first, not a governed data platform
- Needs clean data feeds from fund administrators or internal systems
- Analytics are tied to what the platform collects, not a flexible BI layer
73 Strings
73 Strings comes at private markets analytics from the valuation side. It offers five valuation engines, draws from 12 data sources, and uses NLP and machine learning for document extraction. The platform is structured around three modules: Value (valuation), Monitor (portfolio dashboards), and Extract (data ingestion).
If your primary problem is valuation accuracy and speed, 73 Strings is worth a close look. The extraction and monitoring capabilities are genuine. The limitation is that analytics is secondary to the valuation workflow. If you need a broad BI layer for investor reporting, cash flow analysis, and vintage benchmarking beyond valuation metrics, the platform does not go as deep in those areas.
Strengths
- Strongest valuation capabilities of any product on this list
- Multiple valuation engines with broad data source coverage
- Credible document extraction using NLP and ML
- Good fit for firms where valuation is the primary bottleneck
Limitations
- Analytics and monitoring are secondary to the valuation workflow
- Less depth in investor reporting, cash flow analysis, and vintage benchmarking
- Narrower platform scope compared to broader alternatives
daappa Analytics (part of daappa Studio+)
daappa Analytics is not a standalone analytics tool. It is one layer in the daappa Studio+ platform, which runs from document extraction through to investor delivery. The idea is that analytics should operate on data that has already been extracted, validated, and governed, rather than requiring you to solve the data problem separately.
The stack works like this: Extractor AI pulls data from fund manager statements and portfolio company reports with ~99% extraction accuracy, with human review on exceptions. That data flows into DataHub, a governed data model that normalises and structures everything. Analytics then provides fund performance dashboards, portfolio KPI trends, vintage analysis, and exception reporting on top of that governed data. And the same data feeds the Investor Portal for LP-facing outputs.
The advantage of this embedded approach is that there is no separate data feed to build and no reconciliation between your analytics tool and your operational data. When something changes in DataHub, it changes everywhere. The disadvantage is that daappa is newer to market with a smaller installed base than the incumbents on this list.
Strengths
- Embedded stack: extraction, data governance, analytics, and portal in one flow
- No separate data feed or BI project required
- ~99% extraction accuracy at the intake layer; ~99% less processing time
- 20+ years building private markets technology
- Governed data model ensures analytics and reporting use the same trusted source
Limitations
- Newer to market with a smaller installed base than incumbents
- Not a standalone analytics product; requires the Studio+ stack
- Less relevant if you only need monitoring on top of already-clean data
Summary comparison
| Product | Primary approach | Strongest area | Key limitation | Best for |
|---|---|---|---|---|
| FundCount | Accounting-led | Unified GL + investment management | Analytics tied to accounting; requires platform commitment | Single-system fund admins, family offices |
| Allvue Systems | Broad platform | Widest functional coverage across GP/LP workflows | Analytics depth relies on Power BI/Excel | Mid-to-large GPs wanting lifecycle coverage |
| Chronograph (Cobalt) | LP monitoring | Push-button reporting, benchmarking | Monitoring-first; needs clean GP data feeds | LPs and allocators |
| eFront (BlackRock) | Enterprise | Deepest benchmarking data; public-private consolidation | Enterprise pricing; overkill for mid-market | Large institutions, pension funds, SWFs |
| Cobalt GP | Self-service monitoring | Accessible GP portfolio analytics | Not a governed data platform | Mid-market GPs |
| 73 Strings | Valuation-led | Valuation engines and fair value measurement | Analytics secondary to valuation workflow | Firms where valuation is the bottleneck |
| daappa Analytics | Embedded stack | Extraction to analytics to portal in one flow | Newer to market; smaller installed base | Fund admins and GPs wanting end-to-end data flow |
How to evaluate: questions to ask during demos
Every vendor will show you a polished demo on their own data. The questions that actually matter are the ones that reveal how the product works with your data, your processes, and your team.
- Where does the data come from? Ask the vendor to show you how data gets into the system. Is there an extraction layer, or do they assume you have already solved that problem? If your data arrives in PDFs and inconsistent spreadsheets, this is the question that matters most.
- What happens when data is wrong? Every system will visualise clean data well. Ask what happens when a fund administrator sends a statement with a different format, or a portfolio company changes its KPI reporting structure. Where do exceptions surface, and who resolves them?
- Can I see the data lineage? For any number in a dashboard, can you trace it back to its source document? This matters for audit, for LP queries, and for your own confidence in the outputs.
- What does the integration look like? If you already have a fund accounting system, how does the analytics tool connect to it? Is it a real-time integration, a batch export, or a manual upload? What breaks when the accounting system upgrades?
- How does LP reporting work? Ask to see the actual workflow for producing a quarterly LP report. How much manual intervention is required? Can LP-specific templates be configured without developer involvement?
- What is the real implementation timeline? Ask for references from firms of your size and complexity, and ask those references how long it actually took, not how long the vendor said it would take.
- Where is my data stored? Especially relevant if you are regulated in a jurisdiction with data residency requirements (Luxembourg CSSF, UK FCA). Can the platform run on-premise or in your own cloud tenancy?
What to watch: the convergence of extraction and analytics
The most significant trend in this market is the convergence of data extraction and analytics. Historically, these were separate problems solved by separate tools. You would use one product (or a team of people) to get data out of documents and into a structured format, and then a different product to analyse it.
That separation is breaking down. The firms that struggle most with PE analytics are not struggling because their dashboards are bad. They are struggling because the data feeding those dashboards is incomplete, inconsistent, or arrives too late to be useful. Solving the analytics problem without solving the data problem just gives you well-formatted uncertainty.
The products that will win this market over the next few years are the ones that close the gap between raw data (PDFs, spreadsheets, ad hoc reports from GPs and administrators) and governed, analytics-ready data. Whether that happens through built-in extraction, better integrations, or acquisitions, it is the direction the market is moving.
If you are evaluating tools today, the key question is not just "what can the analytics layer show me?" but "how does data get into a state where the analytics layer can be trusted?" Any product that requires you to solve that problem separately is adding a significant integration and governance burden that will affect your operating costs for years.
Next steps
If you are exploring how an embedded extraction-to-analytics approach could work with your existing fund accounting and operations setup, we are happy to walk through daappa Analytics in the context of your actual data and operating model.
See how daappa Analytics works with your existing data→
Or explore the individual components: Analytics · DataHub · Extractor AI · Studio+ overview