For private equity firms, data forms the foundation from which opportunities are uncovered, decisions made, relationships with LPs maintained and competitive advantages built. A robust strategy for the collection, structure and maintenance of data should therefore be of critical importance… but is all too often neglected or completely ignored.
As Holland Mountain announces the expansion of its ATLAS platform we examine why products of this nature are so important for firms in today’s complex and competitive market, why data strategy has failed to capture the imagination in the past and why this is certain to change in future.
Where Does Your Data Live?
For most firms, the answer to this question lies in spreadsheets and the software applications which sit at the heart of private equity operations, with each tool powered by data drawn from its own independent database. As a result, the same entities exist as multiple database entries on different applications. Changes made to one are not reflected in others, leading to inconsistencies, inefficiencies and missed opportunities.
Adding to the complexity is the fact that application databases exist to serve the functionality of the software in question. Not only are entries duplicated, but the way in which they are logged also varies. Data logged as a relationship between two objects on one application could appear as fields attached to a single object on another. Using application to application integration to drive consistency is therefore not a viable strategy. Attempting to use Excel (a powerful spreadsheet application but a poor relational database substitute) as a central repository is fraught with similar issues.
End to end solutions can offer a degree of data consistency out of the box, but the extent to which data can be manipulated and analyzed is still dependent on features available in the software. The majority of these platforms, powerful as they are in their own right, are simply not designed to support an all-encompassing data strategy. Furthermore, adding additional applications or integrating market data can lead to similar issues in terms of consistency. An end-to-end application suite is therefore also not necessarily a complete solution to the data problem.
The Solution
The most robust approach is to achieve a ‘single source of truth’ via a data platform which reads and writes to individual applications from one consistent database. However, the complexities involved in data warehouse initiatives have historically limited their implementation to larger firms with access to engineering resources.
Even as the market has developed and costs associated with creating a data warehouse have dropped, there is still the problem of industry complexity to contend with. In the same way that generic CRM struggles to effectively serve the unique requirements of private equity, traditional data warehouse providers face an uphill battle to understand the complexities of the asset class. Understanding unique metrics and calculations, appreciating requirements for different departments and achieving familiarity with key industry tools is a significant challenge for any provider without a specific focus on private markets.
It is therefore encouraging to see products hitting the market which leverage industry knowledge, technical prowess and pre-built modular solutions designed to operate at scale. Holland Mountain’s ATLAS platform features prominently among a new breed of data solutions which offer firms of all shapes and sizes the opportunity to implement more robust data strategies.
As they do, here are five key advantages from which they can expect to benefit:
Flexibility
The use of a single source of truth can ease the process of adding new applications or migrating from one platform to another while reducing the risks associated with traditional data migration. Firms are able to exert more control over their technology stack and keep up with the latest products on offer.
Consistency
Entities appearing on multiple applications will be consistent, with changes made in one place applying across all products. This reduces the risk of errors and missed opportunities, with the potential for additional data validation and governance to highlight any issues and inconsistencies. In addition to achieving uniformity, the format in which data is delivered is consistent with the expectations and demands of specific teams.
Insights
The presence of complete data in one core database, with changes tracked over time, opens the door to custom dashboards, unique calculations and scenario modeling based on a complete dataset driven by AI and machine-learning technology.
Efficiency
The amount of manual work required to achieve consistency across applications is vastly reduced and should be less prone to human error.
Security
In addition to facilitating machine-learning and other data science initiatives, data archiving adds additional security for a firm’s most valuable asset while also allowing for robust audit of both internal and external stakeholders.
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