Maximizing Private Equity Data Pipeline Efficiency with Robust Data Integrity Practices

As private market general partners (GPs) face headwinds challenging traditional value creation levers, data integrity has become paramount. Increasingly, advisers seek to leverage granular portfolio company insights to drive new value creation strategies. From improving portfolio company profitability to successfully integrating acquisitions and implementing ESG improvements, advisers collect and analyze vast amounts of data to understand the drivers behind fund and portfolio company performance and unlock new opportunities.

However, data exists on a broad spectrum, and data integrity concerns continue to afflict firms’ front, middle, and back office workflows. From posing compliance burdens to wasting resources and contaminating downstream analytics, poor data quality impacts the entire investment lifecycle.

What Is Data Integrity?

Data integrity refers to data’s accuracy, consistency, and completeness throughout its lifecycle — from ingestion to downstream utilization. It describes the trustworthiness and reliability of a dataset.

Core characteristics of data integrity include:

• Accuracy. Does the collected data reflect a given metric’s true and correct value, and is it displayed in the proper format? Data accuracy is compromised if a portfolio company reports quarterly revenue of $200,000 but the actual amount is $300,000.

• Completeness. Data completeness refers to the wholeness of a dataset. Are there missing fields in ESG surveys and quarterly portfolio reporting templates? Incomplete data typically results from deficiencies in data collection.

• Consistency. Is data uniform across different sources, formats, and periods? If a portfolio company’s financial information is stored in various sources, is the information the same in both? If changes are made in one database, is it reflected in another? Or consider the vast differences in how portfolio companies report ESG metrics. When a fund’s portfolio companies have unique reporting conventions for health and safety, DEI, and other metrics, GPs can struggle to obtain a portfolio-level view of ESG performance.

These core attributes of data integrity work in conjunction with one another. Incomplete data leads to inconsistencies and errors that impact accuracy.

The Role of Data Integrity in Private Equity

Data plays a critical role throughout the investment lifecycle — from deal sourcing and due diligence to portfolio management and reporting. Private equity firms rely on data to make informed investment decisions, assess risks, and create value in their portfolio companies.

Pre-deal insights empower GPs to substantiate and validate a target company’s claims, identify ESG risks, and refine key valuation model inputs. GPs use financial and operational KPIs throughout the holding period to gain deeper insights into portfolio company performance, unlock opportunities, and drive value creation.

At exit, data plays a leading role in forming a compelling exit story that showcases performance vs. the original investment thesis. When potential buyers can quickly and efficiently analyze the value created in a portfolio company, it streamlines the closing process. Data also serves a host of internal workflows. Firms rely on data for forecasting, accounting, and compliance, among others.

From determining which portfolio companies to acquire to helping those companies perform better and leading them through a successful exit, quality data that can be efficiently processed and analyzed plays a fundamental role across firm operations. However, not all data is created equal, and decisions made from poor data can have significant downstream consequences.

How Poor Data Integrity Impacts Private Equity Operations

GPs have access to more data than ever before. Yet, many struggle to fully harness available information. Generating meaningful insights through legacy data pipelines is time intensive, and many GPs worry they are built from incomplete datasets.

Consider a GP relying on operational improvements to drive value creation. They must have confidence in the reliability of the KPIs they collect. Inaccurate metrics could significantly impact their ability to create value in their portfolio companies.

Poor data integrity poses various consequences, including:

• Wasted resources. Poor data integrity wastes time — decision makers must direct their time away from value-add tasks to troubleshooting data pipeline issues and inaccuracies.

• Inaccurate insights. Poor data integrity leads to misleading insights, resulting in bad decisions that affect the bottom and top lines. When incorrect figures are reflected in line item expenses, it can negatively impact the perception of a segment or product’s profitability, causing GPs to direct resources ineffectively.

• Lost opportunities. Inaccurate data can lead decision makers to overlook value creation opportunities. Incorrect reporting in a new sales territory or product domain could falsely indicate limited potential, leading GPs to abandon initiatives due to incomplete data and lack of historical context.

• Reputational damage. When incorrect values flow into fund reporting and to LPs, it poses reputational and relationship risks.

• Compliance. Poor data integrity often makes it difficult to accurately trace a data point’s origin, which can be problematic in the case of an audit, adding significant — and unnecessary — risk.

Suggested reading: Learn how next-generation technology can help streamline private equity compliance efforts

How Data Collection Pipelines Impact Data Integrity in Private Equity

Data collection pipelines play a fundamental role in shaping a firm’s data integrity.

Historically, GPs managed data ingestion and storage in Excel or other offline methods — in which large amounts of portfolio company data are collected and housed across disparate spreadsheets or databases. This manual strategy makes data highly susceptible to human errors, posing significant data integrity concerns. Offline spreadsheets also pose traceability and security challenges. Lack of version control functionalities and security limitations threaten data auditability.

Today, most firms leverage cloud-based software for data collection and storage. The cloud offers a single source of truth for data, resolving many issues with historical methods. However, most cloud-based solutions today are template based, posing several data integrity and quality considerations. While templates offer effective data extraction for structured data, they can’t effectively capture data from unstructured sources, such as quarterly PDFs. Since most data GPs have access to is unstructured, a template-based data collection pipeline can limit the completeness of data advisers can tap for decision making.

Further, templates pose integrity concerns for firms collecting data across many portfolio companies. Templates look for specific information in certain places on a file or document. If a portfolio company places EBITDA at the top corner where the template expects to find revenue, it could be accidentally copied into the repository. Absent effective data validation, this results in data inaccuracies.

Chronograph offers flexible data capture, providing firms with the best of both worlds. GPs can use templates in appropriate scenarios and automatically capture data directly from unstructured source documents, such as ESG surveys. Automated data collection enhances data integrity by ensuring consistent data collection methods and formats, eliminating variations and inconsistencies that usually occur in template-based alternatives. These solutions bolster data quality while meeting the growing demand for GPs to interact with complex data from various sources.

Suggested reading: Learn about Chronograph’s Snowflake service, which simplifies data Extract, Load, Transform (ELT) at scale

How Can GPs Maintain Integrity in Their Data Pipelines?

Optimizing data pipelines empowers GPs to extract trusted insights from their data. Reliable and accurate data safeguards GPs against misguided conclusions and erroneous judgments, enabling them to confidently navigate the complexities of investment strategies and capitalize on opportunities with a heightened level of certainty.

Best practices GPs can harness to enhance data integrity in their operations include:

• Standardizing data collection. Implementing standardized data collection processes ensures consistency and accuracy. GPs should consider relevant approval workflows for collecting and signing off on portfolio company documents and clearly define data fields and formats for data collection.

• Validating input data. Data should always be validated before it’s moved into storage. GPs should validate and verify data to identify anomalies, outliers, or missing data and remove duplicates. When outliers are identified, workflows and processes should be triggered to correct errors at scale.

• Implementing access controls. GPs should limit access to data accordingly, implementing permissions to restrict changes to information by unauthorized parties.

• Ensuring data auditability. GPs should maintain a data audit trail that can trace the source of data changes and modifications. This trail should be generated through automated processes and track data events such as create, delete, and update along with the individual who triggered them.

Additionally, firms should implement proper security measures to ensure compliance with relevant standards. Training staff on data integrity protocols and cultivating a data-driven culture are other vital considerations.

Data forms the foundation for decision making, risk management, and value creation. The ability to gather, analyze, and leverage high-quality data differentiates successful private equity firms by providing valuable insights to optimize investments and generate superior returns. Data integrity empowers firms to meet strategic goals, respond to market changes effectively — and ultimately remain competitive.

Learn how to enhance your data integrity and downstream analytics with Chronograph.