What a Fund wants, what a Fund needs

There’s a predictable moment in the life of a growing business as operations, departments and processes mature and become engrained. More data, more stakeholders, more decisions. What once felt intuitive begins to feel fragmented, with questions taking longer to answer and conversations starting to loop. Teams begin to operate with slightly different assumptions and understandings about the same reality.

Photo by Ricardo Gomez Angel

It is a natural evolution in the growth cycle of any business, yet if left unchecked the side effects become a significant drain on the material value a business can deliver, and the cost of it doing so. What can seem as minor inefficiencies initially begin to compound into something more structural, with wider reaching effects.

More often than not, the root of it sits in how the business handles its data. One of the more enduring ideas from Jim Collins' Good to Great (one of my all time favourite books, and a must read for any business leader) is that sustained performance is not typically the result of a single breakthrough or new technology. It comes from the consistent application of discipline across how a business thinks and operates.

That principle translates directly into how organisations approach data.

Control is not the same as access

Most organisations would say they have their data under control. They can point to systems, dashboards, shared drives and reports. Information exists, and can be retrieved when needed. Access, however, is not the same as control (or context!).

Control requires a shared, consistent understanding of what that data actually represents, and context is required to increase confidence that when different teams look at the same information, they can interpret it in the same way, and act on it with certainty.

Across a business, that understanding touches everything. Finance teams are trying to build a picture of what income should exist, not just what has already been received. Legal teams are navigating the nuances of what rights can be enforced, transferred, or are approaching expiry. Commercial teams are making decisions about what can be licensed, and where, and under what constraints. Leadership is attempting to synthesise all of this into a coherent view of opportunity and risk, and build a strategic holistic plan to move the entire business forward (hopefully through the lens of their “hedgehog concept”, in Jim Collins parlance).

When that shared understanding is missing, each of the business functions continues to operate but on slightly different versions or interpretations of the truth. Alignment becomes something that has to be constantly reassembled, rather than something that is baked into how the organisation works.

The opportunity in shared context

There is a tendency, particularly in data-heavy industries, to assume that improvement comes from increasing sophistication. More data sources, more complex models, more advanced tooling.

In practice, some of the most meaningful gains come from something much more fundamental: giving the right people the right access to the right context.

That might mean a legal team having a clear view of when underlying agreements expire, allowing them to anticipate changes in rights ownership rather than reacting to them. It might mean a finance team being able to compare expected income against actual receipts in a way that surfaces discrepancies early, while they are still actionable. It might mean marketing teams understanding which parts of a catalog are performing in specific markets or platforms, and being able to prioritise accordingly.

None of this relies on hidden insights or proprietary algorithms. It relies on clarity, consistency, and the ability to connect information and context across functions.

And yet this is often where the greatest value is left unrealised.

How friction takes hold

A vacuum of clarity is only filled by friction.

Sometimes this can be very visible- teams repeatedly asking each other for information that should be readily available. The same analysis is recreated in multiple places because no one fully trusts a single source of truth or does not have direct access. This realises as delayed decisions, or worse incorrect ones as key context is unavailable- let alone slowing down those needing to constantly provide various reporting to other areas of the business.

More often, though, the impact can be less obvious. Decisions are made based on partial information, with assumptions quietly filling in the gaps. Different parts of the business optimise for their own priorities, without a clear view of how those decisions can have an impact elsewhere.

In the context of music, these disconnects have tangible consequences. I’ve heard apochrophally of advances being offered without a complete understanding of how existing catalog is performing. I’ve also seen first hand contractual and licensing decisions being made without full visibility into how rights are currently able to be exploited. Revenue analysis can also become very reactive, driven by what has already been reported rather than what should be happening in a forward thinking, proactive, way.

Each of these outcomes is understandable in isolation, but together they create a system that is significantly slower, less efficient, and more exposed to risk than it needs to be.

Discipline before technology

Before any system can be effective there needs to be agreement on what is being measured and, critically, why. Data structures and adjoining context need to be consistent enough that information can move between teams without losing meaning. Processes need to exist that define how decisions are made and how information can be accessed by the organisation. Teams need to be aligned on what success looks like, and how their individual roles contribute to it.

Technology plays an important role in all of this, but its impact is shaped by what already exists. As per Good To Great, it is only typically an accelerator and amplifier to existing structures and processes. Where operations are clear and data is well understood, technology, correctly applied, will naturally increase speed, agility and scale. Where they are not it tends to only amplify the effects caused by underlying issues rather than resolving them.

These challenges are not unique to any one sector, but are particularly pronounced in music rights investment as a still maturing sector.

Funds are operating across a landscape that is inherently complex. Rights are fragmented across territories and income streams. Data flows in from a wide range of sources, often with significant delays and varying levels of both consistency and transparency. Underlying agreements come with their own terms, obligations, and timelines, many of which have a direct bearing on future value (and can be incredibly challenging to accurately capture- as anyone who has read through amendment after amendment will attest!)

Within this environment the need for clarity is acute.

Understanding what rights are actually controlled, and in which territories, is fundamental to any commercial strategy. Building a reliable view of expected income, and comparing it against what is received, is central to both valuation and ongoing performance. Identifying where agreements are approaching expiry, or where rights can be restructured, has a direct impact on long-term upside.

Alongside these needs sits a different set of expectations. Funds want to move quickly! They want to make decisions with high confidence, without having to repeatedly validate the underlying data. They want to be able to identify opportunities and act on them before they become obvious to the rest of the market.

Meeting those expectations depends less on access to information, and more on how effectively that information is organised, shared, and understood across the business.

What about AI?

The current wave of interest in AI inevitably shapes how these problems are approached- and as we’ve discussed previously it is a very powerful amplifier (for both good and bad).

There is a growing belief that many of these challenges can be addressed through automation and intelligent systems. In certain areas, that belief is well founded- AI can help process large volumes of data (with extreme caution), identify patterns, and reduce the manual effort required to maintain, consume and analyse information.

However, its effectiveness is closely tied to the quality and structure of the data (and processes) it operates on. Where data is fragmented or inconsistent, the outputs tend to reflect that. Where there is no shared understanding of what the data represents, even accurate outputs can be difficult to interpret or act upon.

In that sense, AI tends to reinforce the strengths and weaknesses of the underlying system and operations. It can accelerate progress where strong foundations are already in place, but it won't compensate for their absence.

Decisions, decisions, decisions.

For business leaders, this ultimately comes back to a question that is both simple and difficult to answer honestly.

When decisions are made, is everyone working from the same, shared understanding of the underlying data? Do systems and teams connect in a way that allows information to flow naturally, or does it have to be actively pulled together each time? Is context readily available, or does it depend on knowing who to ask?

For music rights funds, the answers to those questions have a direct bearing on performance.

As the asset class continues to mature, advantages that once came from access to capital or deal flow are becoming less pronounced. The differentiator increasingly lies in execution- how effectively a fund can understand, manage, and maximise the assets it owns.

Execution, in this context, is shaped by something deceptively simple: how well the organisation understands itself.