Harmonizing Private Equity Financial Data for Bottom-Up and Fund-Level Analysis

Portfolio companies’ financial statements reveal key insights into their operations. Private equity professionals need this information to understand and monitor the drivers of their investments’ performance. However, most rely on legacy data collection methods that can’t extract granular line items and require significant data translation, limiting their ability to both maintain bottom-up company models and conduct efficient fund-level analysis.

With a flexible portfolio monitoring system, investors can enhance data access from financial statements, collect company-specific insights, and maintain a data harmonization layer that supports aggregate fund-level analysis.

Legacy Data Collection Methods Limit Financial Data Accessibility and Fidelity

Portfolio company financial statements and supplementary reporting vary widely depending on stage, sector, and unique company factors. A SaaS company’s income statement will contain starkly different line items from that of a manufacturing business. Similarly, an early-stage, venture-backed company will have very different value drivers compared to an established, profitable enterprise.

Despite these differences, most template-based data collection systems require investors to apply a universal data dictionary across their entire portfolio. For example, if one portfolio company’s valuation driver is ARR and another’s is net sales, a private equity firm may struggle to properly report beyond a general ‘revenue’ category. While this somewhat provides a fund-level data harmonization layer, it sacrifices the fidelity and context of the underlying metric. Net sales and ARR are very different valuation drivers, and with templates, private equity firms may find it challenging to efficiently report this nuance.

Additionally, when portfolio companies report multiple versions of the same metric — most commonly, adjusted EBITDA — most templates can only accommodate one. As a result, private equity firms can struggle to aggregate and maintain the different views of EBITDA they wish to use for internal analysis, valuations, and reporting.

The rigid nature and configuration of templates also restrict a private equity firm’s ability to ingest company-specific KPIs, as they require metrics to be collected across the entire portfolio. Collecting manufacturing inventory KPIs becomes impractical when the same metrics have to be included in the template for a healthcare business.

At scale, these limitations restrict financial data accessibility and compromise data fidelity. Moreover, when private equity professionals have to shoehorn metrics into a universal label, they’re less likely to trust and integrate them into downstream processes.

A Flexible Data Model Enhances Financial Data Collection

Private equity firms can enhance financial data access and quality by using portfolio monitoring technology built on a flexible data model. Unlike traditional template-based systems that require rigid, upfront data harmonization, flexible data modeling offers a harmonization layer post-data collection. This allows firms to access all reported metrics from their portfolio companies, maintaining metric granularity and also applying a harmonization layer that streamlines fund-level analysis.

Private Equity Professionals Can Access All KPIs and Value Drivers

With a flexible data model, investors can easily access and ingest all financial metrics and operating drivers reported by their portfolio companies. This allows them to better understand how bottom-up line items impact key value drivers, enhancing their ability to identify trends in their portfolio companies and monitor investments on a more granular level.

For example, say a private equity investor notices revenue is flat, but the gross margin declined in one of their portfolio companies. With a flexible data model, they could efficiently investigate company-specific metrics that build up to these revenue and margin figures, helping them gain insight and identify the underlying drivers for this decline.

Private Equity Professionals Can Harmonize Metrics for Fund-Level Analysis

Flexible data modeling allows private equity firms to maintain metric fidelity while applying a harmonization layer that streamlines fund-level analysis. Circling back to the above example, if one portfolio company’s valuation relies on ARR and another on net sales, firms can maintain the granularity of these metrics while also applying a separate, central revenue label to each company that allows for seamless querying and fund-level analysis.

Ultimately, flexible portfolio monitoring technology provides private equity firms with higher-quality data and more granular insights that help them better monitor their portfolio companies and accelerate value creation strategies.

Request a demo to see how Chronograph can help harmonize your portfolio company data for bottom-up and fund-level analysis.

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