Real assets investing has always been a game of detail. Every property, loan, or infrastructure position comes with its own paperwork, its own currency, its own local quirks.
Multiply that across a global portfolio, and the detail becomes a data problem, one that can quietly slow down even the most experienced teams.
Our recent case study, unlocking real-time insights from fragmented global data, shows how this challenge affected a leading UK-headquartered asset manager with approximately $25 billion in real estate assets under management across global alternatives. On paper, the business was thriving. Behind the scenes, its data was working against it.
When good data hides in plain sight
The manager's data wasn't missing. It was scattered. Information arrived from multiple sources, in multiple formats, currencies, and languages, much of it from regions with limited digitisation. Bringing it all together meant slow, manual consolidation that ate into the time teams needed for actual decision-making. Invoice approvals still ran through email, adding friction to routine processes. And because so much of the work was reactive rather than predictive, risks and opportunities were sometimes spotted later than they should have been.
None of this is unusual. It's a familiar pattern for real assets managers operating across jurisdictions, and it's one of the reasons the asset class has been slower than others to benefit from real-time reporting.
Building a single, trusted view
The fix wasn't a single piece of technology. It was a combination of automation, standardisation, and smarter workflows designed to work with the data as it existed, not as anyone wished it did.
That meant ingesting data directly from XLS, PDF, and CSV files without manual conversion, then automatically normalising, enriching, and deduplicating it. A "map once" approach let the same configuration standardise data across every source, so the consolidation work didn't have to be repeated each time. Invoice approvals moved onto a workflow with automated reminders, removing a persistent bottleneck. Artificial intelligence/machine learning-driven anomaly detection added a layer of real-time alerting, so issues could be flagged as they emerged rather than discovered after the fact. Throughout, managed services supported onboarding, validation, and governance, ensuring the new approach was sustainable rather than a once-off fix.
What changed
The results were tangible. The manager gained real-time, consolidated dashboards with drill-down capability, giving teams a clear, current view of the portfolio rather than a historical snapshot. Invoice approval cycles sped up by 60%. Decision-making improved, supported by predictive insights and alerts rather than after-the-fact reporting. Errors fell, governance strengthened, and the new data infrastructure integrated cleanly with the manager's existing reporting and analytics tools.
The broader lesson for real assets
This example reflects a wider shift across the industry. As portfolios grow more global and more complex, the ability to consolidate data quickly and accurately is becoming as important as the investment decisions the data supports. Managers who can see their whole portfolio clearly, in real time, are better placed to act on both risk and opportunity. Those still working from fragmented, manually assembled data are, in effect, making decisions with a lag built in.
Solving this doesn't require managers to overhaul their entire operating model. It requires the right infrastructure to sit underneath it: one that can ingest data in whatever form it arrives, standardise it consistently, and surface it in a way that's genuinely useful for decision-making.
This is the kind of challenge we work through with real assets managers every day, bringing together data, technology, and expertise to give clients a single, trusted view of their portfolios.
If you'd like to explore how this could work for your business, visit our real assets solutions page to learn more.
