By Ryan Burger, PFA Solutions
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. Some firms are in the nascent phase of implementing core accounting and CRM solutions to replace spreadsheets, while others are exploring machine learning, big data, and robotic process automations. In the middle of the pack are firms deploying solutions to easily aggregate data, conduct forecasting and quickly produce analytics for internal reporting and externally to investors. These firms aim to better organise their data on investors, portfolio companies, and prospective investments, all of which are typically siloed across various sources. Deploying analytical solutions allow individuals across the organisation to access data quickly and reduce reliance on spreadsheets and various other teams across their organisation.
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. For example, fund accounting systems include all capital balances1 on investors, investment transaction data, as well as valuation 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 can provide information upon request. Alternative investment firms that have acted on resolving this issue have begun implementing aggregator and analytics solutions as outlined throughout this article.
Solution: Centralised data portal with built-in analytics
Deploying a centralised data portal allows firms to better organise 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. Additionally, firms intend to utilise available market data to produce better assessments on the operating metrics of their investments. 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 allow individuals across the organisation to access data more quickly and reduce reliance on spreadsheets.
“ Deploying a centralised data portal allows firms to better organise 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. ”
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 organised through normalisation data practices. Examples include:
a. 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.
b. 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)
c. 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.
d. Data categorisation: Well-defined transaction mapping and grouping mechanisms are critical when migrating granular transactional data, say 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.
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 will require significant investment to establish the data model and then to maintain the data and reporting layers on an ongoing basis. Many firms have been successful in developing a data warehouse and creating Tableau or Microsoft PowerBI reporting, however there are other external solutions that provide an interactive, digital experience through web browsers and mobile devices. In both cases, specialised reporting may need to be developed 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, and strategies are constantly evolving, which can result in delaying initiatives and chasing moving targets. It is important to be aware of these risks while moving forward on the path to improvement.
“ The industry is evolving to transition from spreadsheets as the key data storage mechanism towards a data analytics tool used in conjunction with fit-for-purpose applications. The migration allows for easier access to data and forecasts, and improved tear sheet reporting. ”
The alternative asset industry is complex, and teams are lean so 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 evolving to transition from spreadsheets as the key data storage mechanism towards a data analytics tool used in conjunction with fit-for-purpose applications. The migration allows for easier access to data and forecasts, and improved tear sheet reporting. Existing solutions including custom built tools, data warehouses with business intelligence capabilities, and specialist vendors such as PFA Solutions, each with their own pros and cons. Whichever solution is selected, the foundational elements of the data model are critical for the long-term success of a highly usable platform. At PFA, we look forward to being a part of the evolution as a trusted partner with our clients and those who launch on data and analytics initiatives.