At the latest Intertrust Group event in London, an expert panel discussed the ongoing surge of data management in private capital – and the technology required to handle it.
What are the risks of persevering with legacy technology in private equity? And what are the challenges of upgrading to new and better systems? These issues were both high on the agenda at the latest Intertrust Group panel event in London.
Colin Leopold, head of research and insights at Private Equity Wire, chaired a discussion on “meeting the technology and data challenge”. Expert insight was provided by consultant Mark Rowe, a former partner at Partners Group, alongside Iyran Clunis, director at BlackRock, and Chitra Baskar, COO and head of global funds at Intertrust Group.
There was general agreement across the panel that doing nothing is not an option. Growing regulator and investor demands around private markets, ever-increasing data sources and the need to streamline operations require a technological response.
What form that response will take was the subject of much of the discussion.
As firms look to scale, operation by spreadsheet is becoming a clear business risk. But Mark Rowe made it clear that even best-in-class systems could become barriers to progress if they don’t talk to each other.
Sophisticated data management systems would be needed to avoid duplicity of data across technologies. They would make information widely available across teams and ensure everyone was working from the same “golden record”, he said.
Chitra Baskar noted that the challenge of integrating systems was being magnified by increased merger and acquisition activity. This was creating cumbersome hybrid solutions and further integration headaches.
Integrated systems for better data management
The panellists agreed that there was a pressing need for integrated systems. But how should firms develop them?
Iyran Clunis said that building a private funds operating model from scratch was difficult. Despite deeper pockets and more developer talent than most, BlackRock acquired alternative investment software suite eFront for precisely this reason.
Clunis added that even large firms had to acknowledge where they had gaps in knowledge, and to look to work with third parties.
Rowe agreed and said that many firms are looking for an outsourcing or co-sourcing partner to help update and maintain their technology stack.
This involves working with a partner to build a tailored system based on specific requirements, rather than simply implementing an off-the-shelf solution.
For newer, smaller firms, the first priority is to develop and articulate a data strategy, Rowe added. Knowing what data to collect from where, and to what purpose, is the first step towards getting the right technology solution to meet those needs.
Private capital technology doesn’t stand still
At the same time, the panel acknowledged that those needs were not static and that technology was under continuous development.
Baskar said that artificial Intelligence (AI), machine learning (ML) and blockchain are no longer simply industry buzzwords. AI and ML in particular are currently being applied most frequently in front-end investment management.
Clunis added that in private markets, most of the research and development around AI and ML is focused on retrieving data from documents. Natural language processing, for example, has the potential to transform private capital reporting by automating hitherto slow and error-prone manual processes.
Rowe agreed – but said that, at the moment, these technologies might replace parts of a process rather than the whole thing. Real revolution would arrive when automation could create straight-through processing with little human intervention.
Nevertheless, technology is already a major differentiator in areas such as managing data, reporting and compliance, and firms have to keep abreast of developments, he added.
The data challenge around ESG
In one area at least, a data-gathering revolution is required sooner rather than later. Clunis argued that one important use for AI and ML is to scrape public sources for data to meet growing ESG compliance requirements.
Baskar agreed that ESG is creating greater challenges around data. Finding, validating and reporting data from across a portfolio company’s supply chain is hugely complex.
Many portfolio companies are not sophisticated enough to collect the required information. Clunis said that a combination of internal, external and proxy data may currently be the most realistic option.
For Rowe, the key was to focus on what is achievable. There is no perfect ESG data set. But the more granular the data the more valuable it will be, leading to more meaningful outputs, more sophisticated analytics and, ultimately, better decision-making.
This was perhaps the overarching message from a stimulating debate. There are significant challenges around data and technology. Meeting them head-on can create more efficient processes and leaner, smarter firms. That could be through in-house development, outsourcing partnerships or a combination of the two.
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