Introduction: The Challenges of Manual Data Management

For small fund managers juggling transactional records without a dedicated fund accounting solution, the task of keeping data organized can feel like an uphill battle. You might not be ready to license a full-fledged platform just yet, but that doesn’t mean you can’t set your fund up for success by managing your data in the most efficient and scalable way possible. Having worked with hundreds of firms—whether implementing a fund accounting system for the first time or migrating from a solution that no longer meets their needs—I’ve seen firsthand how crucial it is to lay the right foundation. Thoughtful data practices not only simplify daily operations but also ensure a smoother transition when your fund outgrows its current tools. This is part 1 of a 3-part series where we will explore how to make the most of Excel today while preparing for a more sophisticated system tomorrow.

But first, how do you know when it’s time to get off of Excel. If you find yourself spending hours recreating the same reports from scratch or worrying about the risks of working with stale or misaligned data copied between spreadsheets, it’s a clear sign your processes are straining under the limitations of manual systems. Delaying the transition to a dedicated fund accounting solution can have significant downstream consequences. As your data grows more fragmented across disparate worksheets and systems, the complexity of migration multiplies, driving up costs and timelines. Studies show that businesses often underestimate the costs associated with data migration, particularly if data is not properly prepared. For example, poor data quality or disorganized formats can lead to lengthy cleanup processes and increased reliance on consultants during the implementation phase, resulting in higher-than-expected costs.

Beyond the financial impact, delays can also hinder your ability to scale efficiently and accurately. Many firms discover, too late, that the money saved by sticking with spreadsheets for another year is eclipsed by the expenses incurred at best, during an overdue and chaotic system transition or at worst, fixing an error that was already reported on. Acting before your operations reach this critical point ensures smoother implementation and reduces stress on your team.

That said, Excel can still be a great tool if managed properly!

Building a Solid Database Foundation

When setting up a database in Excel, the goal is to strike a balance between efficiency and comprehensiveness. Consolidating too much data into a single file can lead to sluggish performance and challenges with updates. On the other hand, spreading data across too many disparate worksheets risks losing track of relationships and introduces complexity when reconciling or referencing information. A thoughtful structure minimizes these issues and creates a scalable framework for your operations.

Core Databases to Focus On

1. Master Data

Master data represents the foundational entities in your fund’s operations. This includes:

·     Investors: Information about individuals or institutions contributing to the fund. This should be a single master list across your organization and can handle data points for tax or other regulatory reporting.

·     Portfolio Companies: Companies the fund has invested in where you can assign industries, geographies, and more.

·     Investment Types: Types of investments the fund has made with those portfolio companies. This about how you can use this dataset to automate your schedule of investment and fair value measurements.

·     Investments: The child records of your portfolio companies that represent the individual investments the fund is making (by Investment Type). These should have their own primary key IDs that are independent from the Portfolio Company’s.

·     Entities: Structures or vehicles associated with the fund family, such as GP entities, partnerships, SPVs or holding companies.

·     Entity Groups: Groups that can be used to facilitate common aggregation in various reports.

This will certainly grow as you scale, but I would suggest strategically separating investment and investor relations data as they develop their own initiatives. Organizing master data properly allows for efficient aggregation, reporting, and downstream reconciliation. However, when working with simple tools like Excel, you are limited by what you can reasonably build and manage yourself. Conversely, Entrilia has hundreds of master data tables right out-of-the-box, with thousands of pre-defined fields, specifically designed to automate robust, real-time reports. This level of sophistication ensures that no matter how complex your fund’s structure, your data remains consistent, scalable, and ready for immediate use.

2. Utilize Parent-Child Relationships

Master data should reflect relationships within your entities to enable flexible analysis and aggregation. For example: 

·     For investments, consider separating the portfolio company from specific investment types (e.g., debt, equity, or convertible notes). Each investment type should have its own record, allowing you to differentiate transactions while still consolidating at the portfolio company level.

·     For investors, maintain records of fund-level participation versus co-investments, ensuring clean separation while preserving the ability to report at an aggregate level.

Designing Your Data Structure

1. Primary Keys for Reliable Linking

Each record in your master data should have a unique identifier (primary key) that isn’t based on the record name, and that will be used across your entire organization. This avoids issues when names change and ensures consistent links to transactional data. For instance:

·     Assign a unique ID like “PC001”for each portfolio company.

·     Use a similar structure for investors, such as “LP001”.

This practice eliminates the need for error-prone find-and-replace processes when updates are necessary, and you have consistent names across all reports.

2. Anticipate Detailed Transactional Reporting

Think ahead to the most granular level of reporting you may need for transactional aggregation. You’re safest if you assume all reported balances should be achievable using a pivot table off of your general ledger or sub-ledgers (which we’ll get to later). For example:

·     Entities: Regardless of whether you combine or consolidate your reports, you want to maintain independent records for every legal entity.

·     Investments: Maintain separate records for each investment type to facilitate detailed performance reporting and cost tracking.

Avoid shortcuts that could complicate future reporting needs. Ensuring that each transaction can be mapped back to a unique master data record is essential.

Best Practices for Database Design

1. Avoid Overloading Your Workbook

While it’s tempting to consolidate everything into one file, this can lead to performance bottlenecks. Instead:

·     Use separate but linked workbooks for master data and transactional data.

·     Create summary sheets or pivot tables for consolidated reporting.

2. Maintain Data Hygiene

Consistently clean and validate your data to prevent errors from propagating. Implement tools like:

·     Data validation for drop downs to ensure consistency in inputs.

·     Lookups Off Primary Keys if you want to see more associated data of the parent in a child record, use lookups off of the primary key so you can minimize where you’re updating.

·     Conditional formatting to flag missing or inconsistent data.

3. Plan for Scalability

As your fund grows, your database should be able to scale without significant redesign. This involves:

·     Structuring your data to accommodate new types of transactions or relationships.

·     Regularly reviewing your database for opportunities to streamline or enhance.

While Excel works well for small-scale operations, scaling to meet the demands of larger funds will certainly require a system with robust data structures and automated workflows. Entrilia is built to handle the complexity of fund accounting with features like transaction-level automation, seamless data reconciliation, and advanced reporting capabilities that go far beyond manual efforts.

Drawing the Line: Master Data vs. Transactional Records

Understanding where to categorize data as either static master data or transactional records is essential for creating a well-organized, functional database. Misclassifying these elements can lead to inefficiencies and challenges when generating reports or making adjustments. Here’s how to distinguish between the two and optimize their use.

1. Master Data: Static by Design, Yet Useful for Context

Master data encompasses information that rarely changes but provides the foundational framework for your database. It’s typically descriptive and helps define the entities within your fund’s structure.Examples include:

·     Names and unique IDs for investors, funds, and portfolio companies.

·     Key attributes such as management fee rates, fund vintage years, and legal entity details.

Gray Areas in Master Data:

Some data, like management fee rates, can blur the line between static and transactional. While it might seem logical to store this in a master data file for quick reference, rates may change over time due to renegotiations or fund amendments. In these cases:

·     Store the base rate or side letter details in master data for general reference, but not to be used in lookups.

·     Have a separate tab in the transactional database, that maintains historically the rates and calculated amounts at each period.Avoid over-using things like “notes” or “comments” in cells, because when it comes to a migration – you don’t want to pay someone to worry about what note or comment might be hidden in a cell.

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As you scale, solutions like Entrilia automate much of this process. Our platform’s intuitive input forms and automated data creation workflows make it easy to maintain clean, dynamic records without the risk of misclassification or human error.

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2. Transactional Database: Capturing Change and PoweringCalculations

Transactional data focuses on dynamic, event-driven records that represent financial or operational activity over time. This category includes:

·     Capital commitments, contributions, and distributions.

·     Close-by-close data for fundraises, true-ups, or transfers.

·     Fees charged or earned at specific intervals.

For example, while a commitment amount might feel like astatic attribute, treating it as transactional allows for much more dynamic reporting. Tracking each close and true-up as separate records enables accurate calculations of unfunded amounts and clear audit trails for future reference.

3. Enhancing Master Data with Legal References

Master data can become a powerful resource for your team by linking directly to legal documents. While this won’t necessarily assist in future system migrations, it adds operational value by providing quick access to source materials, especially for new hires or during audits. For example:

·     Subscription agreements & side letters toInvestors

·     Limited partnership agreements (LPAs) to fund entities.

·     Purchase agreements to portfolio companies

Best Practices for Document Links:

·     Centralized Storage: Use a flat storage location like SharePoint, Google Drive, or Dropbox.

·     Avoid Path Dependencies: Store files with stable, shareable URLs rather than relying on file paths that can break if folders are moved or renamed. Explore using tags, if you want to simulate a folder structure.

·     Naming Conventions: Use clear, consistent naming conventions for your files so users can quickly identify and retrieve what they need.

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Though linking legal documents to your data in Excel is a great step, modern platforms like Entrilia take this further by embedding document management directly into the workflow. This ensures that your source files are securely stored and always accessible without relying on fragile folder paths or manual URL tracking.

4. Striking a Balance: Consider Future Reporting Needs

When deciding where a piece of data should reside, ask yourself:

·     Will this data change over time? If so, it likely belongs in the transactional database.

·     Does this data influence calculations? If yes, ensure it’s structured in a way that supports flexible reporting and historical analysis.

·     Who will need this data, and why? For example, future hires or auditors may benefit more from a well-organized master data file with links to original documents, while dynamic reporting will rely on well-maintained transactional records.

Eventually you'll adopt a mature fund operations platform, like Entrilia, where reports are generated instantly using live data, eliminating the need for manual updates or recreating pivot tables whenever something changes.

I hope this is helpful! Next we’ll be exploring transactional database best practices – stay tuned!

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