Automating the Private Equity Data Pipeline: Key Benefits and Considerations for GPs

Private market general partners (GPs) face many disruptive challenges. An increase in industry participants has driven deal-making costs up. Consequently, more than traditional value-creation levers are needed to generate outsized returns. Additionally, growing demand for talent and increased compliance scrutiny pose additional burdens.

In this evolving landscape, data has become an invaluable asset for GPs. However, despite having access to more data than ever before, private equity firms often struggle to leverage it in a truly transformative manner. Legacy manual processes mean data is often scattered and fragmented, making it difficult to access and integrate across a firm.

Automating the data pipeline unlocks a host of use cases and benefits for GPs. Leveraging next-generation technology for data ingestion and storage reduces the time spent on non-value add workflows while providing crucial insights at the portfolio company and fund level. Front, middle, and back offices can redeploy time toward their most critical tasks.

Ultimately, automated data management fundamentally transforms a firm operationally and provides a competitive edge for GPs in driving alpha.

Automation in the Private Equity Data Pipeline

Historically, GPs have relied on offline spreadsheets for data aggregation and analysis. Firms have implemented technology into these workstreams to varying degrees — often in the form of legacy systems that rely on templates. Many still rely on manual and one-off processes in their data pipelines. These manual data strategies may be manageable for GPs operating a handful of homogeneous funds with few portfolio companies. Still, manual data entry and reconciliation take time and resources, exposing firms to costly errors. One missed zero in a portfolio company financial statement has significant downstream impacts.

Moreso, manual data pipelines lack uniform standards, traceability, and flexibility. If a reporting template needs to be updated, it has to be manually adjusted to fit the new format. These limitations affect GPs’ capacity to track exposure, risk, and performance across their portfolio. Alternatively, automating data management provides advisers with a single source of truth, delivering trusted insights for understanding portfolio company and fund-level performance drivers.

A data pipeline has three key phases: ingestion, transformation, and storage. Automation transforms each phase and has implications for GPs.

Data Collection and Ingestion

GPs collect data from various sources, including portfolio company reports, fund and accounting data, third-party benchmarks, and more. Automated data ingestion tools leverage technologies such as APIs, optical character recognition (OCR), and intelligent document processing (IDP) to extract this data into a centralized repository. In private equity, data ingestion typically occurs in batches; data enters the pipeline as a group according to a schedule of investments or in response to external triggers, such as quarterly reporting requests to portfolio companies. Importantly, data ingestion forms the bedrock of a GP’s data pipeline. All downstream reporting and analytical processes are only as reliable as the data quality they are built from.

Data Transformation

The data GPs receive is not universally structured. Some data may be logically organized in a spreadsheet-like format, while other sources — such as an ESG survey — may be unstructured, composed of primarily text elements. Once data is ingested, it must be cleansed, mapped, and enriched for storage and usability in downstream analysis and reporting. In cloud-based automation tools, extract, transform, and load processes (ETL) are the most common transformation mechanism. ETL involves loading data to its final destination before applying transformations.

Learn about Chronograph’s Snowflake service, which provides ETL at scale

Data Storage and Analysis

After data transformation, data is stored in a central database where firms can access it for downstream analysis, research, and reporting.

The Benefits of Cloud-Based Data Pipelines in Private Equity

A manual data pipeline makes deal sourcing, portfolio monitoring, reporting, and accounting susceptible to breaks in the information chain. This data vulnerability ultimately affects the top and bottom lines. More time spent on data collection and processing is less time spent on fundraising, closing deals, and generating returns.

Manual data pipelines also limit the volume of data firms can collect, further restricting alpha. Portfolio companies have unique KPIs, systems, and data schemas. They might have different naming conventions for key metrics or collect data across multiple sources. At scale, that becomes difficult for GPs to manage.

Automating data collection allows GPs to extract data from various sources, convert it to a unified format, and access it from a central location. Automation reduces bottlenecks between teams, empowering stakeholders with the information they need to make decisions faster.

Key benefits of cloud-based data pipelines for GPs include:

Scalability and Efficiency

Automating data ingestion empowers GPs to quickly collect and process large volumes of unstructured and structured data. As firms scale and invest across various verticals and investment strategies, automation is extremely powerful in quickly aggregating and delivering crucial insights.

Enhanced Data Accessibility

Data doesn’t offer value if the right stakeholders cannot access it at the right time. Data is typically siloed in manual pipelines and stored across various platforms in a firm’s tech stack. Fund and accounting data might be housed in one place, while portfolio company KPIs and LP information are stored elsewhere. As a result, data accessibility is limited and stockpiled between departments.

Automation brings data into a central location, creating a single source of truth. Data becomes accessible for front, middle, and back offices, improving collaboration, communication, and critical business functions — every team can access the insights they need for their unique workflows.

Improved Data Quality

Manual data processes are prone to inaccuracies and human errors. Automated data pipelines provide tools that validate and verify data as it’s collected, checking for missing fields, outliers, and other anomalies — such as portfolio company restatements — that can easily be missed.

Detecting an error on the front end increases the dataset’s quality and minimizes downstream impacts. By identifying and addressing redundant and erroneous data, analytics work more efficiently and accurately at the end of the pipeline.

Increased Productivity

Most importantly, automated data pipelines free up resources for GPs. Investors can redeploy time away from manual reconciliations to focus on value-add tasks. They can make faster decisions, drive alpha, and gain a competitive advantage.

What Should GPs Consider When Automating Their Data Pipelines?

Private equity firms have unique business needs, investment strategies, and portfolios. However, as GPs look to automate their data pipelines, there are several things to consider to ensure success:

• Interoperability. Automating data management should break down information silos. This only works when data ingestion tools are compatible with the applications, databases, or technologies in a GPs’ workflow. Looking for a solution that will seamlessly integrate with existing tools is essential.

• Flexibility. The data GPs receive comes in a variety of formats. They might use templates for portfolio company data collection or want to capture data directly from a source document. Other reports — such as ESG surveys— may be housed in PDFs. A flexible data tool that can accommodate a variety of sources and easily adapt to new reporting requirements is vital for maximizing value.

• Volume. Data ingestion tools can handle varying volumes of data. GPs must determine the amount of data they’ll need to collect, ensure solutions can accommodate that amount, and scale in parallel with their portfolio over time.

• Data validation features. GPs should implement data ingestion tools with validation and cleaning features. Ensuring numbers fall within an expected range, checking input data types against expected data types, and flagging syntax inconsistencies are a few features GPs should look for.

• User-friendliness. Data pipeline automation solutions should be value-add tools. Often, complex legacy solutions detract from a team’s core business responsibilities by burdening them with templates and other tasks. Considering a tool’s interface, ease of configuration, and overall learning curve is essential.

• Security. Data is vulnerable to security breaches whenever it transfers from one destination to another. GPs should pick data automation tools that meet relevant privacy and compliance standards.

Automated data management provides immense advantages for private equity firms. GPs can optimize their operations, enhance decision-making capabilities, and gain a competitive edge in the market. Data automation enables efficient data collection, organization, and analysis, providing critical insights and reducing the risk of human errors.

With improved data accuracy and accessibility, GPs can make well-informed decisions and effectively manage their portfolios. Moreover, automated data management facilitates compliance with regulatory requirements and enhances transparency, fostering trust and credibility with investors. Embracing automated data management is no longer just an option but necessary for private equity firms aiming to thrive in an increasingly competitive and data-driven industry.

Learn how to automate your data pipeline and streamline portfolio operations with Chronograph.