How a Flexible Data Model Enhances Private Equity Data Collection

Private equity firms have access to more data than ever before. Statistica projects that data generation will reach an estimated 181 zettabytes — or roughly 169 trillion gigabytes — in 2025, up from 120 in 2023. For many private equity firms, data utilization remains a significant challenge due to legacy systems that lack flexibility and advanced technology. These solutions typically rely on fixed formatted templates that pose several barriers to data collection workflows. 

Templates Fall Short in Private Equity Data Collection

Most private equity firms use semi-automated legacy systems for data collection that rely on a hybrid approach of manual input into templates and data ingestion into a cloud-based central repository. These systems regularly involve administering a universally defined template across an entire portfolio, meaning all portfolio companies report the same KPIs. 

While templates can offer a suitable data collection solution for certain workflows, adopting them as a global data strategy can pose challenges. 

Templates Cannot Easily Adapt to New Line Items or Metrics

Traditional template-based data extraction involves defining a fixed frame around data fields in a highly-structured file, such as an Excel spreadsheet. Programmed rules within the template guide the extraction process, such as identifying revenue in one cell and EBITDA in another. The frame’s position and dimensions remain constant over time, meaning they lack the agility to recognize or integrate new metrics, especially when the same concept can be referred to differently, e.g., TTM and L12M.

When a portfolio company introduces new line items, such as APAC or EMEA revenue, templates often fail to adjust. They might still extract the data but copy the wrong metric. For instance, EMEA revenue might inadvertently end up in the repository since it occupied the cell designated for total revenue. Absent effective data validation, this threatens data quality and the integrity of downstream analysis. 

Universal Templates Don’t Work for Diverse Portfolios

In a portfolio with homogenous company reporting, template-based extraction may prove manageable. However, for most GPs, templates don’t account for the unique reporting preferences across portfolio companies within different sectors and asset classes. 

Even similar portfolio companies often maintain distinct internal preferences when labeling and reporting metrics. One portfolio company may report adjusted EBITDA while another reports pro forma adjusted EBITDA. The way companies prefer to display revenue also varies. With templates, GPs can’t harmonize these variances. 

Prominent firms with significant majority stakes and influence can often enforce the adoption of a universal reporting template upon portfolio companies. However, for GPs lacking such leverage, aligning portfolio companies’ internal metric interpretations with standard templates leads to unnecessary inefficiencies. Frequent exchanges with portfolio companies to clarify metric meanings often extend data processing timelines and detract from impact.

A universal portfolio reporting template also disregards the evolution of a portfolio company over time and lacks flexibility for company-specific metrics. When a company ventures into a new vertical or geography, the need to report new KPIs arises. If confined to a portfolio-defined template, GPs must engage their portfolio monitoring provider to incorporate new metrics, a process that may take weeks or months to collate. 

Templates Limit Ability to Tap and Access Unstructured Data

Roughly 80-90% of enterprise data is unstructured, projected to grow annually at 65%. Since templates only work for highly structured files, GPs using a template-based legacy system face limitations in accessing and utilizing the troves of unstructured data at their fingertips.

For example, the board decks, PDFs, and other documents GPs receive house information in charts, tables, and other graphics. Without a tool for extracting and transforming this unstructured data, GPs risk losing valuable insights.

In addition to providing a more flexible data collection system framework, source document extraction technology also unlocks other benefits, including efficiency gains powered by automation, sophisticated data validation, and customizable, unlimited approval workflows. 

Flexible Data Models Enhance Data Access and Quality

Private equity firms manage diverse data collection workflows in their portfolios, including monthly financials, ESG surveys, operational KPIs, board decks, and ad-hoc non-financial data. A one-size-fits-all approach for these varied tasks lacks the flexibility required for optimal data capture and workflow optimization. Private equity professionals might opt for a templated method for some portfolio companies while favoring source document extraction for others. Doing this allows them to collect core KPIs across their portfolio, and also gain the flexibility to introduce new metrics on a company-by-company basis. 

The data collection method suitable for one workflow might not prove optimal for another. Qualitative data, like ESG surveys, are significantly limited by Excel template methods given the prevalence of conditional questions, tabular data, or other qualitative measures. There is also significant variability in the question sets that may be appropriate for different types of businesses or geographies. While a consistent taxonomy found across a portfolio may be appropriate for a template, capturing monthly financials that regularly have new line items or distinct calculations warrants a more sophisticated data collection approach than templates can offer. 

The varying needs across workflows underscore the vital role of a flexible data model. Private equity professionals need the agility to use templates when appropriate and leverage advanced technology to extract unstructured data and easily and quickly configure KPIs. A flexible data model empowers private equity firms to tailor each data collection workflow according to their preferred method. They can automate data capture for board decks and use templates for ad-hoc requests. Importantly, initial data structuring choices aren’t binding; workflows can be easily adapted and restructured as required.

Key Benefits of a Flexible Data Model for Private Equity

A flexible data model offers private equity firms a versatile toolkit to customize their data collection strategy. In contrast to legacy systems that require adoption of universal templates across portfolios, flexible data models unlock limitless workflows and personalized configurations, giving private equity professionals complete control over their firm’s data flow.

• Capturing financial metrics and KPIs becomes possible regardless of strategy, industry, or security type
• Data can be appropriately associated with the relevant database objects (e.g. cross fund deals requiring multiple associations for a single portfolio company)
• The alignment of differing labels for the same metric can happen during data capture, allowing portfolio companies to retain their reporting preferences 
• Data fields can be reordered, modified, or restructured without compromising data integrity
• KPIs and other metrics can be effortlessly added or removed as assets mature and evolve
• Workflows can be configured to leverage various data strategies based on specific requirements

Request a demo to see how Chronograph’s flexible data model enhances data access and quality.