When the cracks begin to grow in your finance team’s foundation 

Are cracks beginning to show in your carry and compensation process? Growing complexity may be the cause, and rehauling your data management foundation can help. 

At a recent gathering of private equity and venture capital leaders in New York’s Times Square, 12 percent of attendees named the complexity of allocation and incentive plans as their biggest challenge compared to one-off exceptions, vesting schedules, the volume of joiners and leavers, tax complexities, lean staff or reporting. What to do? 

Private equity and other private capital firms – including private debt, venture capital, real estate, infrastructure, and hedge funds – are actively evolving their technology and data models to improve business operations and manage their increasingly complex allocation and incentive plans. Some firms are in the nascent phase of implementing core accounting and CRM solutions generally to replace spreadsheets, while others are exploring machine learning, big data, and robotic process automation. 

In the middle of the pack are firms deploying solutions to easily aggregate data, conduct forecasting, and quickly produce analytics for internal and external reporting. These firms aim to better organize their data on investors, portfolio companies, prospective investments, and carry and co-investment plan types – all of which are typically siloed across various sources. Deploying analytical solutions allows individuals across the organization to access data quickly, reduce reliance on spreadsheets, and free up time for other teams. 

This article discusses the various data and analytical solutions available to solve challenges related to data access for analysis, forecasting, and internal and external investor reporting. Additionally, it outlines considerations when improving a data framework and deploying an analytics solution. 

Problem: Disparate systems and constricted access 

Given the complexities and uniqueness of each firm in the alternative investment industry, there are typically disparate systems used for various functions within a private capital firm. This is for good reason, since it is important to use the best application available for each department or process. Some firms have over 100 separate systems across third-party vendors and proprietary tools. Given the common practice of data housed in various locations, individuals that need certain information may not have direct access without other teams’ help. This approach especially contributes to the complexity of new allocation and incentive plans, as each requires specific reporting. one-off exceptions, and vesting schedules. 

For example, fund accounting systems include all capital balances on investors, investment transaction data, valuations, and the data used for performance calculations (such as IRR, TVPI, DPI, and RVPI). Investor relations, investment professionals, and tax team members often rely on the accounting staff (internal or external) who own these systems and provide information upon request. Alternative investment firms that have acted on resolving this issue, in contrast, have begun implementing aggregator and analytics solutions outlined throughout this article. 

Solution: Centralized data portal with built-in analytics 

Deploying a centralized data portal allows firms to better organize their data across key areas of their business, including details on prospective and existing investors, cash balances, credit lines, portfolio companies, total reward statements, and prospective investments which are typically siloed across various sources. Additionally, firms intend to utilize available market data to produce better assessments of their investments’ operating metrics, often through improved dashboarding and drill-throughs. There is also a trend to improve internal reporting to employees related to their carried interest, co-investment (personal capital contributions), and other forms of compensation. Deploying analytical solutions allows individuals across the organization to access data more quickly, cutting down time on spreadsheets. 

Deploying a centralized data portal allows firms to better organize their data across key areas of their business, including details on prospective and existing investors, cash balances, credit lines, portfolio companies, and prospective investments which are typically siloed across various sources. It particularly helps manage the reporting, one-off exceptions, and vesting schedules of the industry’s increasingly complex allocation and incentive plans. 

Considerations when deploying analytics solutions 

Outlined below are considerations when improving a data framework and deploying analytical solutions: 

1. Build a solid foundation 

To generate quality analytics and retrieve meaningful raw data, information must be well organized through normalization data practices. Examples include: 

  • Unique keys: Create a standard naming convention across systems (e.g., unique IDs for investors and investments). Many firms have the same information across systems but naming conventions vary, causing mismatches and breaks. 
  • Lookup fields: Use drop-down fields as opposed to free text to tag/label when possible. Examples include investment referential data (e.g., standard sectors, industries, and strategy classifications) and investor referential data (investor type, family, and class). 
  • Data management and configuration: Restrict configuration abilities to certain individuals who control certain types of data (i.e., look-up values, new user-defined fields, and mapping between systems). Ensure business users can request updates and the individuals responsible for updating system configurations understand the business needs and any downstream impacts on reporting. 
  • Data categorization: Well-defined transaction mapping and grouping mechanisms are critical when migrating granular transactional data, for example, from accounting systems to a data warehouse. Additionally, once the dashboarding or data portal layer is implemented, it is crucial to focus on the data model and persisted data versus calculated data to get accurate and meaningful results. 

2. Continually invest time and resources 

Success is only feasible if the proper resources are allocated to build the foundation and properly manage firm details. Anecdotally, firms that have built masterful data management modules have dedicated adequate internal resources to the integration or have partnered with third-party administrators and technology services providers. Many have done both. This approach lets firms scale for growth, improve controls, and make reporting more efficient. 

3. Use the right tool for your business 

For large, and complex operations with sufficient staffing, it may be appropriate to develop a solution in-house. However, this option will require significant investment to establish the data model and then to maintain the data and reporting layers on an ongoing basis. Expectations of improved reporting for participant experience, expansion of carry pool members, and new vesting arrangements have also made this option less tenable. 

Many firms have been successful in developing a data warehouse and creating Tableau or Microsoft PowerBI reporting. Other external solutions also provide an interactive, digital experience through web browsers and mobile devices. In both cases, firms might need to develop specialized reporting to retrieve the analytics required. It is important to consider all available options when undertaking a data and analytics effort. 

4. Embrace an iterative process to an ideal solution 

Establishing the ideal solution is an iterative process. It may take years to perfect or may never be complete due to competing priorities. To document progress from the onset, it is helpful to compose a roadmap listing small to moderate goals. The private capital industry is complex. Strategies are constantly evolving, delaying initiatives and moving targets. It is important to be aware of these risks while moving forward on the path to improvement. 

The industry is transitioning from spreadsheets as the key data storage mechanism to data analytics tools used in conjunction with fit-for-purpose applications. The migration allows for easier access to data and forecasts, self-service report building, interest calculations for loan facilities, and improved tear sheet reporting. 

Next steps 

The alternative asset industry is complex. Teams are lean. A continued reliance on spreadsheets and manual processes is expected. However, a strong data model with quick and easy access to information for both internal and external parties via a user-friendly platform is within reach. 

The industry is moving from spreadsheets to fit-for-purpose applications. The migration allows for easier access to data and forecasts and improved tear sheet reporting. Existing solutions like custom-built tools, data warehouses with business intelligence capabilities, and specialist vendors such as PFA Solutions. Each offers different opportunities. Whichever solution firms select, the foundational elements of the data model they deploy are critical for the long-term success of a highly usable platform. At PFA, we look forward to being a part of this evolution as a trusted partner with our clients and those who launch data and analytics initiatives. 

PFA Solutions

Comprised of technologists with extensive experience in Private Capital Markets, PFA Solutions brings technical expertise and deep business acumen to every problem we solve. 


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