By Vera Huang, Director of Data Services
Earlier this year, IQ-EQ sponsored the Tech Innovation Conference hosted by Real Deals and the Drawdown in London. I was delighted to take part in the conference and hear from key players – both fund managers and service providers – the latest thoughts of the industry on technological advancements and potential use cases for these technologies in the investment process.
With a 2019 survey sponsored by UBS Asset Management having shown, in a pre-COVID context, asset management firms at varying stages of a four-year journey to advance their alternative data and advanced analytics capabilities (55% of surveyed participants were still in the early stages, while 10% had just started their journey), it was interesting to revisit such insights in the post-pandemic era.
Key takeaways from panel discussion on data strategy for fund tech transformation
I was particularly pleased to moderate a panel discussion on the theme of ‘Technology is worthless without data: what makes a great data strategy’.
With distinguished speakers such as Lyndon Arnold, Head of Technology at Triton Partners; Roberto Bonanzinga, Co-Founder of InReach Ventures; Hind El Gaidi, Head of Financial Information & Valuation at Astorg; and Brian Mason, Head of Technology at BC Partners; moderating this thought-provoking session provided me with a window into how quality data can influence investment decisions, transparency, reporting and more. Broadly, we discussed how fund managers think through their data strategy against the backdrop of broader tech transformation taking place in private markets.
One of the biggest takeaways for me from the panel discussion was this: Since there is no single, uniform way of approaching technology and data transformation, fund managers need to consider the following holy trio of factors.
- User experiences: Consider the user journeys of all stakeholders involved – be they investors, investment professionals or service providers
- Data maturity: Understand where data is coming from and how good the data quality is (among other factors), to make better informed decisions
- Data enrichment: Finally, consider what third-party data sources could be brought in to enhance their pipeline and risk management
These are just some of the key points mentioned, but of course there is a lot more that fund managers will need to think through – especially when it comes to execution and prioritisation. With competing priorities and finite resources, fund managers need to be disciplined in selecting which challenges they will tackle first and focus their energy on those challenges to demonstrate value quickly.
Fund managers see great potential in Generative AI
One point that is very topical is Generative AI (GenAI), which attracted a lot of attention from the panellists and audience members alike.
Based on broader industry insights and takeaways from the panel discussion, it appears that fund managers and service providers are generally very optimistic about the speed of technological advancement and think that GenAI will be adopted more widely across the private markets sphere.
A likely use case is for an investment professional to ask GenAI to retrieve and organise data on past deals that had the highest success rate. While a very promising use case, the success of this application depends heavily on fund managers first and foremost getting historical data into a usable and query-able format.
As mentioned above, data maturity is a journey that forward-looking and savvy fund managers must embark upon while planning for the adoption of cutting-edge technology such as GenAI for enhanced insights.
Data strategy to bloom alongside business and digital transformation strategies
Going beyond the panel to the wider event, my key insights would be that there are new ways of looking at tech transformation, be it business transformation with technology, or taking the leap from ‘cost reduction’ to ‘scalability and flexibility’.
On challenges in the broader uptake of futuristic technologies, it appears that Artificial Intelligence or Machine Learning (AI/ML) has the highest adoption rate at deal sourcing stage, while the maturity of the data platform and the data itself are major hurdles to their wider deployment.
Furthermore, as key considerations for anyone embarking on a data platform journey, it is essential to receive senior leadership buy-in and demonstrate value quickly while remaining cognisant of the efforts and talent required along this fulfilling but challenging ride.
I would like to conclude by highlighting that a good data strategy cannot exist in isolation and should be considered in tandem with the firm’s business and digital transformation strategies. Indeed, when business strategy, data strategy and tech strategy all interact effectively with one another, true alpha-generating insights can emerge.
