Information is valuable currency for Private Capital firms. It drives data-based decision making across every business function, from originating deals, to overseeing portfolios and reporting on returns.
As fund managers continue to invest in best-of-breed digital tools, their Private Capital data is split across a multitude of different systems. In turn, this has increased reliance on reporting solutions like PowerBI that gather data from source systems and spreadsheets to provide analytics and dashboards. Holland Mountain’s Michael Montgomery asks, is this enough or does your data deserve better?
Private Capital data is complex
Typically, fund managers’ data is low volume and high complexity. It isn’t always stored in recognised systems either, it might be held in a spreadsheet, or an external system at the fund administrator.
Getting data right is increasingly a key differentiator for GPs, but if there’s no clear data strategy in place it can be tricky to know where to start. One of the most compelling reasons to improve data quality and reporting focuses on fundraising due diligence and investor relations activities. When an investor asks about their exposure in specific countries, or the impact of Covid-19 on their portfolio, the GP will need to be able to provide the details confidently (quality of answer) and quickly (speed of response). A lot of these questions are triggered by external events, which are harder to plan for. Responsiveness is key.
Additionally, where questions are linked to specific external events, a firm needs to think through the likely connections between portfolio data and external factors. If the baseline data set isn’t robust, they aren’t equipped to interrogate the data set and provide an answer in a repeatable fashion.
Source system reports
Plenty of industry-specific applications offer a built-in reporting solution. These are designed specifically for Private Capital and with the source system in mind, so can usually handle many of the common reporting requests for that specific data set. By utilising the reporting functionality that already exists, a firm can avoid a data restructure and will not need to lean on any additional technical capability if the system vendor can provide support for reporting writing. The end-result is self-service business intelligence, where business users familiar with the data can pull off the reports they need.
However, there are some challenges to relying solely on source system reporting, including:
- It can be difficult to link multiple systems together, which leads to siloed data across the firm
- There is often a lack of standardisation where calculations are performed differently, or business logic is applied inconsistently in different places across the reporting solution
- Source systems can be complex and not optimised for reporting for end users.
Some companies try this route for a while, before realising they need a more strategic, managed way; to control, enrich and provide their data.
Can PowerBI solve these problems?
Layering a popular reporting solution on top of source systems is a tactic often used by firms that want to improve data visualisation and create a ‘one stop shop’ for all reports across the business. It reduces the demand on training users as there’s one common solution, standardises the look and feel of outputs and can be easily connected to additional data sources as new systems are added to the firm’s infrastructure. This is a sensible step towards improving reporting, but it’s still the same data set, just arranged in a different way.
Often, firms will have a go at using PowerBI on top of existing systems and then give up. The available tools do not scale well and can’t manage the complex transformations required to deliver end users with the outputs to respond to business requests. Or, the problem is solved but understanding of the solution is limited to the person or team who built the report. This can lead to key man dependency issues and a lengthy process to untangle or replace later down the line.
The challenge lies not in the reporting solution, but in the missing link between the data and the report. When you build a chart within a spreadsheet, or from data held in a system, you tend to look at each data field and think about how to move each field into the report. Let’s call this the bottom-up approach. The key difference when building bespoke reporting solutions is that you begin with the questions you need to answer. Working backwards from here, it becomes easier to map the data that is required and to consider how best to arrange this within the data repository. By modelling the data, it becomes possible to slice it up in different ways and consider new dimensions.
“PowerBI is a great tool but there’s some basic prep that needs to happen to avoid the ‘garbage in – garbage out’ problem. We recommend firms think about reporting as a broader consideration. It can feel like there’s a quick and tactical solution but quite often you get tied up in the complexity that comes hand-in-hand with that.” – Harpreet Lakhan, Head of Product and Services at Holland Mountain
What are the potential benefits of a Data Platform solution?
By comparison, a well-designed data platform solution will provide firms with one source of truth, across all business systems. Data is centrally stored and managed, removing the need for manual reconciliation and reducing the chance of errors or discrepancies between data sets. A central data repository also provides better control because data isn’t held on individual laptops so security can be managed at a range of levels from user or report, through to mart, cube or data level.
From a usability perspective, a data platform is optimised for reporting and can be customised to the specific needs of each business, team or user. For example, segregated data marts can provide data to different departments whilst allowing senior level management to view the end-to-end picture. In addition to providing real-time snapshots, users should also be able to look back and see a historical view of data. Data platforms allow for offline reporting, so there’s also less impact on the source system.
Critically, a data warehouse will also enable centralised business logic. Decisions about how a calculation should be applied can be agreed as part of the design phase, ensuring users adhere to common definitions across the firm regardless of report, role or department. A data platform can also provide the foundation for more advanced analytics, like machine learning. Once a robust solution is in place, users will be able to ask different questions and do more with their data to deliver value for managers.
Michael is a Solutions Architect at Holland Mountain with over 10 years’ experience in BI and data analytics, working on technical project design and implementation. He has been involved with several large data warehouse and bespoke analytic solutions projects in both Lloyds insurance and Private Equity markets.