Photo Credit: Bastian Riccardi
A step-by-step framework for music businesses of every size – because a solid data infrastructure isn’t optional, it’s the foundation for growth.
The following article, from DMN partner Equalizer Consulting, offers a step-by-step framework for music businesses to develop a deliberate, documented metadata strategy.
The strategy’s purpose is to govern how data is created, validated, stored, and maintained, ensuring musical assets are protected and receive proper compensation. If you’re looking to update, reshape, or overhaul your company’s metadata stack, this is an absolute must read.
About Natalie of Equalizer Consulting
For over 20 years, Natalie Jacobs has operated where music, data, and technology converge. Having overseen a $17 million portfolio of music tech and data projects, she has seen firsthand how easily revenue disappears into the ‘black boxes’ of poor infrastructure.
She founded Equalizer Consulting to solve that problem; bridging the gap between creative vision and operational precision, building the metadata systems and rights strategies that ensure that musical assets are protected and receive the compensation that’s due.
In This Article
I. Introduction
II. The Data Audit
III. Define (and Document) Your Standards
IV. Build Validation Into the Workflow
V. Assign Ownership Across Teams
VI. Plan for Ongoing Maintenance
Introduction
Music companies don’t set out to have a data problem. It’s something that accumulates over time. A catalog grows faster than the infrastructure that supports it. Teams expand without discussion or documentation of shared standards. Systems are added without proper integration or migration planning. What started off small turns into an overwhelming backlog of inconsistent metadata without a clear path forward.
I’ve seen this pattern across companies of varying size – from boutique indie labels to high-volume catalogs with hundreds of thousands of assets. As discussed in my previous article, trying to clean up the existing mess without addressing the underlying processes that created it turns clean-up into a treadmill. New bad data enters the system as fast as (if not faster than) bad data gets corrected.
This is where developing a metadata strategy comes into play – a deliberate, documented framework that governs how data is created, validated, stored, and maintained across the organization. Let’s look at the steps to building this in practice.
The Data Audit
Before you can define where you’re going, you need to understand where you are. An audit doesn’t have to be a negative – by looking at your existing catalog and data infrastructure, you can identify the most critical gaps and inconsistencies. Which fields are incomplete? Where is formatting often inconsistent? Where are match rates below acceptable thresholds? Which data sources are the primary cause of low quality input?
An audit will also map your data flows: where data originates, how it moves between systems and teams, and where it tends to degrade. Reviewing inputs and outputs, both upstream and downstream, is essential. The source of a data problem probably isn’t where it first becomes visible, so identifying the earliest point of entry is key to mitigation.
For example: A royalty team conducts an audit of their catalog due to a discrepancy that keeps showing up in their quarterly reports. They identify a migrated data set that has the wrong release date format. By implementing a process change for verifying and normalizing release date during catalog ingestion, the loop has been closed going forward.
Define (and Document) Your Standards
The absence of documented internal standards is a common, but fixable, metadata problem. Teams across a company tend to view and format data with their particular work in mind – conventions for legal vs performance names, which field version information is housed in, what date formats are captured. The next team to receive the data may be looking at it with a different lens, and may look to apply alternate rules based on their own usage. When it’s not written down, there is no consistent baseline to refer to or enforce.
A data dictionary is a foundational part of metadata strategy – a document that defines data fields, how they are used, acceptable values, formatting rules, and the purpose of that data element. It’s worth emphasizing that this is a living document that requires maintenance as your business, and the industry, evolves.
For example: A label’s A&R/marketing teams use stage names while legal teams use legal names. Both teams are correct in context, but creating guidelines around name capture allows for seamless, autonomous linking, ensuring clarity that all teams are referring to the same deal, release strategy, and royalty recipients. A data dictionary specifies name formats for each field to remove ambiguity before it compounds.
Build Validation Into the Workflow
Now you’ve defined the standards that everybody will adhere to, they need to be enforced at the point of entry. It is most efficient to embed validation into workflows where the metadata is created – not as an afterthought but as a required step. This might mean evaluation of required fields and dropdown values, completed checkboxes, or an automation that flags data anomalies before they flow into the system.
The goal is to make it easier to enter correct data and make it more difficult to enter incorrect data – moving away from the cycle of junk in, junk out. Additionally, when teams better understand the data they are responsible for, it empowers them to confidently and proactively prevent poor data hygiene before it becomes a problem that will require expensive remediation.
For example: Mandatory ISRC validation can prevent assets from being entered without identifiers, therefore alleviating downstream matching issues before they happen. Incomplete or inaccurate data submissions are flagged and corrected at the source, prior to entering the pipeline.
Assign Ownership Across Teams
Data governance fails when it’s treated as someone else’s problem. Every team that touches data needs to have clearly defined responsibilities when it comes to what they create, what they validate, what they escalate when they find an issue, and to whom.
This doesn’t necessarily require a dedicated data team, but clarity is necessary. Who logs ISRCs? Who confirms songwriter splits for a new release? And who is responsible for entering deal terms into a royalty system? When everybody has a clear understanding of their role, and a better understanding of the data they are responsible for, it brings both accountability and efficiency.
For example: An A&R admin team receives label copy from an artist manager, which appears to have a mismatched producer credit. While the data did not originate within the company, the A&R admin is the owner of that data and should clarify with the management team prior to finalizing entry in the label system.
Plan for Ongoing Maintenance
A metadata strategy doesn’t have an end date. Catalogs grow, deals change, standards evolve, and new systems are put in place. Without ongoing maintenance, the strategy will start to decay.
This means scheduling ongoing audits and compliance checks, establishing a process for corrections and escalations, and building in feedback loops so that downstream data users can flag issues to the source, while upstream data users have a better understanding of changing data workflows that may affect the data they are handling. Sustaining data quality is an ongoing investment – requiring buy-in as an essential component of the business, rather than an optional one.
Building a metadata strategy from scratch can feel overwhelming, especially if there is already a growing data backlog that requires management. In a very practical way, start with what you can control, leverage industry standards that are already in place, find high-value quick wins, and build incrementally towards a more comprehensive framework.
The key is to start. You don’t have to have all the answers to take the first step – and the cost of waiting will only grow.
