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New sources, new signals, new vendors - on paper, this should be a golden age for data-driven alpha. In practice, many firms are hitting a wall. Onboarding takes months. Pipelines break unexpectedly. Formats drift. Engineers spend more time fixing data than using it. That tension - between exploding demand and operational reality - is what we mean by the external data maze.
This was the focus of our recent webinar, Navigating the External Data Maze: Challenges and Breakthroughs for Financial Institutions. In this post, we’ll summarize the core ideas. If you want the full discussion and real-world examples, you can watch the on-demand webinar here: 👉 Watch the webinar
Global financial market data spend keeps rising, but growth is starting to flatten. That’s not because firms don’t want more data - it’s because every new dataset carries integration cost, operational risk, and long-term maintenance overhead.
At the same time, alternative data has fundamentally changed the landscape. Firms are no longer limited to traditional pricing, fundamentals, and research. Web data, public data, machine-generated data - entirely new categories of signal are now in play. The opportunity is massive. So is the complexity.
No serious investment strategy runs on a single vendor. Firms need decades of history, multiple perspectives, and varying levels of granularity. That means stitching together Bloomberg, Refinitiv, FactSet, MSCI, niche providers, and public sources. The competitive edge doesn’t come from buying that data.
It comes from integrating, normalizing, enriching, and delivering it reliably - at scale. That’s where many teams struggle.
In the webinar, we made a clear distinction:
That last point matters more than most teams admit. Data that arrives late, incomplete, or inconsistently might technically exist, but it can’t drive decisions or models when it matters.
In reality, most external data does not arrive model-ready. Over half of sources are still unstructured or semi-structured, forcing teams to spend enormous effort just “unblocking” data before it can be used.
What breaks isn’t one thing - it’s everything at once:
None of these problems are rare. They’re the daily reality of operating external data at scale within hedge funds, and other financial institutions that thrive on timely data.
The firms that break out of the maze stop treating external data as an ad-hoc engineering task. They centralize it. They standardize it. And they design for scale from day one.
In the webinar, we discussed what “good” looks like in practice:
This is where operational alpha shows up - not as a buzzword, but as fewer failures, faster access, and better use of expensive talent.
This post is simply a snapshot. The webinar goes deeper - drawing directly on experience from large financial institutions operating thousands of external data pipelines, and unpacking how teams are adapting to the realities of alternative and public data at scale.
👉 Watch the full webinar: Navigating the External Data Maze
If external data reliability, timeliness, and cost are becoming constraints for your organization, this discussion will help:
👉 Schedule a Demo or Contact Us directly and we’ll be happy to discuss a free report and external data assessment fitting your use case.
