The State of Data in the Music Industry
Tools, trends, and opportunities
2024
Water & Music
Prologue: Music’s data paradox
From creation and discovery, to distribution and monetization, data now permeates every aspect of the music business.
But is that data actually accessible, meaningful, and useful? In a noisy market, what does modern “data strategy” look like for today’s artists and industry professionals — and where do they most need help?
To find out, we ran one of the largest, most comprehensive surveys in history on how the music industry collects, manages, and leverages data. In total, we received responses from 600 professionals across 6 continents and 12+ industry sectors, including record labels, music publishing, artist management, live events, academia, and more.
What we found is a landscape marked by paradox:
1. Music professionals are awash in data, yet in many cases starving for actionable insights.
Music data is abundant and diverse. Our respondents use an average of 3 different types of tools in their data operations — ranging from first-party streaming and distributor dashboards, to third-party market intelligence platforms, fan CRM tools, and in-house solutions.
But despite data's ubiquity, the music data that people actually want is still fragmented and expensive. In an organizational context, music data is often siloed, with only a few employees in key positions having access to data tools — even for in-house solutions. Cost is also a major obstacle, especially for smaller teams with limited budgets.
2. Companies across the board are increasing their data investments, while struggling to quantify their returns.
There was a pervasive feeling among our respondents of a "data arms race," with 59% of respondents expecting their company to increase data budgets.
Yet, over 40% of respondents are unsure about or unable to assess the ROI from their company's current data strategy. There is a lot of data acquisition happening without the proper contextualization — namely, understanding how the data being acquired fits into a company’s broader strategy to drive tangible, sustainable business outcomes.
3. Addressing fundamental data challenges appears more urgent than investing in cutting-edge technologies.
Adoption of emerging tech solutions for music data remains limited. 40% of respondents use AI in their data operations, facing challenges including lack of expertise, high costs, and ethical concerns. Web3 adoption has decreased significantly, from 24% of respondents in 2023 to just 14% in 2024.
Meanwhile, notable gaps in data literacy and tooling persist across the industry. Only a third of respondents say their company offers data literacy training, and many still rely on basic tools like email and manual spreadsheets for fan data management. General-purpose CRM solutions are more prevalent than music-specific ones, indicating a lack of tools tailored for the industry's unique needs. There's a clear opportunity for music companies to strengthen their core data foundations, providing a better base for future technological advancements.
Through this report, we aim not just to present statistics, but to spark conversations and provide a blueprint for action in a fast-moving landscape.
In particular, our findings challenge the notion that more data automatically leads to better decisions. As the music industry moves forward, we believe its focus should shift from merely acquiring more data, to asking better questions of existing data — and investing in tools and skills that can translate information into tangible value for artists, fans, and the industry at large.
We invite you to use this report as a catalyst for transforming how you approach data in your own work and organizations. Together, we can leverage the power of data to build a more transparent, equitable, and innovative future for the music industry.
Thank you for reading!
Cherie Hu
Founder, Water & Music
Michael Zhang
Lead Author, Data Scientist
Michael has built a wealth of experience at the intersection of music and tech, having been a founding member of data teams at Spotify, Splice, Tidal, and Yousician. Throughout his career, he has focused on zero-to-one strategies, launching numerous new products, campaigns, and initiatives. Outside of data science, he makes music as one-half of the indie pop duo, corner club. His diverse background allows him to bring a unique perspective to issues concerning music, tech, and data. He's a longtime Water & Music member and contributor.
Cherie Hu
Lead Editor, Project Manager
Cherie is the founder of Water & Music, where she spearheads strategy for the company's editorial, educational, and consulting projects on music and tech. Previously, she served as a leading music-industry analyst for publications including Billboard, Forbes, and Music Business Worldwide, and taught classes on digital music strategy at Syracuse University and NYU. A sought-after expert commentator, Cherie has made interview appearances on CNBC and NPR, and spoken on panels and keynotes at over 40 conferences worldwide. "Mr. Brightside" is her favorite karaoke song.
Alexander Flores
Head of Engineering
Alexander is a full-stack developer with a focus on data and information processing. At Water & Music, he oversees technical projects including website development and backend research tooling, as well as member onboarding and support. His current interests are in AI and interface design, specifically, leveraging it to process and shape large amounts of information, while presenting it in the appropriate context.
Julie Knibbe
Contributing Editor
Julie has been a trailblazer in music data innovation, driving music recommendation and discovery at Deezer and leading the product team at Soundcharts, a top music analytics solution. As the founder of Music Tomorrow, she continues to create cutting-edge data products that empower music professionals. In 2021, Julie launched the first edition of the "State of Data Report" to track how music pros leverage data in their field, to which she now contributes. She has also written a series of insightful articles for Water & Music, showing how industry pros can effectively leverage data to their advantage.
Maarten Walraven
Contributing Editor
Maarten Walraven operates at the intersection of music, technology, communities, and education. There are many different hats, from Co-CEO at Symphony.live to teaching music business at Utrecht University to being coordinator for the Water & Music academy and working with Revelator Labs. If you want to follow his thinking, the best place is MUSIC x, the newsletter he co-edits.
This year's survey built off of themes from Music Tomorrow’s 2023 report, with additional questions related to data access, fan CRM, and emerging technologies like AI and Web3.
We distributed the survey through Water & Music's email lists and social media channels from May 3 to June 14, 2024. In total, we collected 600 responses across over a dozen different verticals in the music business.
Which of the following best describes your company?
N=600
Small, multidisciplinary teams
Our survey responses skew towards North American operations and smaller, more entrepreneurial teams in music and tech.
- Most respondents belonged to small companies with 10 or fewer employees.
- Over half of known annual data budgets were in the range of $10,000 or less.
- Respondents reported having an average of 3 different job responsibilities (marketing, label management, business development, etc.) — reflecting a common need to wear multiple hats on the job.
Which geographic territories does your company focus on?
N=600
How many employees are currently working at your company?
N=600
What is your company's annual budget for data processing, analytics, and research?
N=600
Which of the following best describes your job responsibilities?
N=600
Our respondents are relatively advanced in their exposure to the music industry and music data:
- Nearly half of respondents reported having 10+ years of industry experience.
- 78% of respondents indicated that they engage with music data and analytics at least once a week, with 42% reporting it as an everyday practice.
- 51% of respondents ranked their own data literacy and skills as "intermediate," with another 22% self-reporting as "advanced."
How many years of experience do you have in your field?
N=600
How often do you engage with music data and analytics in your work?
N=600
How would you rate your own level of data literacy and skills?
N=600
Respondents reported a wide range of goals in working with music data, reflecting the diversity of industry verticals in our sample.
Top goals cited include:
- 1. Measuring artist and release performance
- 2. Analyzing market trends
- 3. Planning, executing, and reporting on marketing campaigns
- 4. Benchmarking performance against other artists and releases
- 5. Optimizing company processes and operations
Overall, what are your main goals when engaging with music data?
N=600
02 Defining the modern music data stack
Our survey categorized music data tools into 10 distinct groups. For the most widely used tool types, if a respondent indicated that they or their company used them, we asked a series of tool-specific follow-up questions to gain deeper insights into their application. The top 6 categories of data tools are defined in the next two slides.
On average, respondents reported using 3 different types of data tools in their work. These tools span various aspects of the music business, including consumption tracking, fan CRM, brand development, and competitive intelligence.
The diversity of tools used underscores the importance for modern music companies to familiarize themselves with the broad spectrum of available data solutions. This knowledge is particularly crucial when collaborating with external partners and artists, who often have varied data requirements.
What types of solutions are part of your company's current data stack?
N=600
Defining: Music-specific solutions
These tools are tailored to unique industry needs, offering specialized insights into music consumption, performance, and market trends.
First-party music analytics
What: Performance and usage data, maintained and provided directly by music consumption platforms.
Why: These tools offer artists and rights holders direct access to performance metrics, audience demographics, and engagement data specific to each service, enabling targeted strategies for each platform.
Examples:
Distributor-sourced analytics
What: Aggregated performance and royalty data across multiple platforms, provided by distributors.
Why: These consolidated tools simplify reporting and royalty tracking for artists and labels, providing a holistic view of a release's performance across various digital storefronts.
Examples:
Third-party music market intelligence
What: Broader music industry data aggregators.
Why: These platforms compile data from multiple sources — often encompassing social media metrics, playlist/chart data, and more — to provide market trends and competitive insights that individual platforms or distributors may not offer.
Examples:
Defining: General data solutions
While not music-specific, these tools play vital roles in managing broader business operations and customer relationships in the music industry.
Non-music analytics dashboards
What: Analytics portals for products and services that do not contain music-specific data, or are not necessarily oriented around music use cases.
Why: These tools allow music businesses to track site traffic, social media engagement, and overall digital marketing performance, complementing music-specific analytics with broader digital insights.
Examples:
In-house data analytics
What: Proprietary data solutions hosted, developed, and managed internally by companies.
Why: These tools allow music businesses to tailor their data analysis to their unique workflows, integrate multiple data sources, and maintain control over sensitive information.
Examples:
CRM tools
What: Software solutions used to collect, organize, analyze, and manage fan data.
Why: These solutions are essential for developing direct artist-fan connections, enabling targeted marketing, and tracking fan engagement across multiple touchpoints.
Examples:
Our data shows a strong link between a streaming platform's market share and the popularity of its first-party analytics tools.
According to industry estimates, Spotify, Apple Music, YouTube Music, and Amazon Music collectively hold a 65% share of music streaming subscriptions globally. Their analytics tools are also the most widely adopted among our respondents, with Spotify for Artists leading at a 95% adoption rate.
This correlation goes beyond mere market dominance. Our analysis suggests that the leading platforms are also investing the most in developing robust, feature-rich music analytics tools. For artists, the combination of market reach and sophisticated tools offers a dual advantage — not only higher revenue potential, but also more in-depth tools for audience understanding and growth.
It's a self-reinforcing cycle: Better analytics attract more artists, which in turn helps maintain the DSP's market leadership.
Which first-party music analytics platform does your company currently use?
N=320
Founded in 2016, Chartmetric is by far the dominant player in third-party music market intelligence, with their all-in-one solution integrating streaming, social, and audience data.
Runners-up include Chartmetric competitors Soundcharts and Viberate, as well as Luminate, which powers the Billboard Charts.
Unlike platform-native analytics, third-party tools like Chartmetric offer a broader industry perspective. By aggregating and normalizing data from multiple sources, they enable cross-platform insights, competitive analysis, and historical tracking that individual platforms or distributors often can't provide.
This holistic view enables music professionals to benchmark against industry standards, identify emerging market trends, and make data-driven decisions in key areas such as A&R, marketing, and partnerships.
Which third-party music market intelligence tools has your company used in the past 12 months?
N=280
Unlike with first-party analytics and market intelligence tools, our survey shows a more balanced landscape for distribution solutions. This diversity reflects the competitive, fragmented market for music distribution, where the core service of delivering music to platforms is increasingly commoditized.
The current distribution landscape includes:
- Open/DIY platforms for independent artists (e.g. CD Baby, TuneCore, DistroKid)
- Invite-only services for more established acts (e.g. Absolute Label Services, Believe Artist Services, Stem)
- White-label solutions for labels and music companies (e.g. FUGA, Revelator, Consolidated Independent)
- Major label-owned distributors offering extensive networks and resources (e.g. ADA, AWAL, The Orchard)
This range allows artists and labels to choose partners aligned with their specific requirements, resources, and career stages.
Which type of distribution solution does your company currently use?
N=153
Creating an in-house data hosting solution requires significant financial and technical resources that are out of reach for many companies.
Cloud providers offer comprehensive data storage, management, and infrastructure maintenance services that can scale efficiently to meet almost any demand. Even established music-tech companies like Spotify opt for these external hosting services, rather than developing proprietary solutions.
Our survey reflects this trend, with the majority of respondents reporting the use of Amazon Web Services, Google Cloud, Microsoft Azure, or a hybrid cloud/on-premise approach for their data hosting needs. Together, Amazon, Microsoft, and Google command a 65% share of the wider cloud infrastructure market.
Where is most of your company's in-house data hosted?
N=268
Data visualization tools have become central to in-house data operations in the music industry.
Platforms like Tableau and Looker Studio offer user-friendly analytics capabilities that integrate with various data sources, from internal warehouses to cloud services and locally imported files. These tools democratize data analysis, allowing both technical and non-technical staff to interpret complex datasets and drive data-informed decisions across teams.
This trend extends to music distribution, with most users in our survey accessing distributor analytics via web-based dashboards. The move towards dynamic, web-based formats aligns with the approach of major streaming platforms, and signals a broader industry shift away from static reports to more intuitive, real-time data insights.
Which of the following tools does your company use to manage in-house data operations?
N=268
How does your company typically access distributor-sourced data?
N=153
The most popular CRM solutions in the music industry today were not designed for music.
Instead, there's a heavy industry reliance on generic tools — particularly email and manually maintained spreadsheets — for collecting and managing fan data.
At first glance, this trend suggests an opportunity for a new class of music-tailored CRM tools to better serve the industry's unique needs. However, developing such a tool presents significant infrastructural challenges, as it would need to integrate diverse data sources across social media, email, ticketing, and merchandise sales.
The highly bespoke nature of fan engagement in music also makes it challenging to create a one-size-fits-all solution — leading to the current reliance on adaptable, if imperfect, generic tools.
What type of solution does your company currently use to store and manage fan data?
N=243
Direct email submission remains the dominant method for fan data collection in the music industry, with a 55% adoption rate among CRM tool users.
Email's popularity stems from its simplicity, providing a direct line to fans without intermediary algorithms. This is a sharp contrast to social media, where reach is often limited by unpredictable algorithms.
While pre-saves and smart-links have become standard ways of driving hype for music releases, they are often limited in the amount of data they collect or expose from fans. Part of this is due to the gatekeeping nature of DSPs, who don't share data around their users.
Consequently, despite the availability of newer technologies, email continues to be the most reliable and accessible method for building fan databases.
What are the primary ways in which your company collects artists' or brands' fan data?
N=243
In a free-response question, we asked respondents to directly name the CRM tools they use to store and manage their fan data. After manually tagging each response, we identified 68 unique external tools.
Traditional non-music CRM tools such as Mailchimp, HubSpot, and Salesforce rank among the top tools mentioned.
There is still a long tail of competition for music-specific CRM: The most popular music CRM tool in our dataset, Feature.fm, was only cited by 7% of CRM tool users.
Top CRM tools
N=153
Regardless of whether the industry adopts music-specific or general-purpose CRM tools, fan data remains highly fragmented. Multiple factors contribute to this issue — including the diverse nature of fan touchpoints, privacy regulations around user data, platform gatekeeping, and persistent reliance on manually maintained internal databases and spreadsheets.
The result is a complex ecosystem where artists must rely on an ever-changing patchwork of third parties — labels, management teams, and tech platforms — to manage their fan data. In fact, the power in artist-fan relationships often resides with these third parties, rather than with artists or fans themselves.
Who owns the majority of fan data for artists or brands that your company works with or represents?
N=243
As an optional free-response question, we asked respondents: "What data would you like to have access to but currently do not, in order to better support your work in music?"
Nearly two-thirds of survey participants (388 out of 600) provided responses, which we manually categorized for analysis.
The accompanying graph showcases the music industry's diverse "data wishlist" — highlighting particular demand for data related to fans, streaming consumption, live music, and publishing metadata.
Data Wishlist Categories
N=388
The music industry's growing focus on "superfans" has intensified the demand for detailed fan data — whether that involves identifying and targeting potential high-value listeners, or enhancing engagement with existing dedicated fans.
Many respondents believed they should be able to get more fan data from first-party platforms in particular.
"...identification and access to fans and potential fans… better analytics on [and access to] 'like audiences'"
"...detailed demographic information, engagement patterns across various platforms, and sentiment analysis of fan interactions…"
"...which brands they associate with, what do they spend their money in, what do they spend their time in, what are their hobbies, I mean everything that helps understand the buyer persona and journey"
"...my greatest wish is to able to interact with fans and listeners directly through Spotify somehow… a direct contact form or way to capture an interactable data point - mail, message, phone number…."
Artist and label teams frequently express a desire for more granular streaming metrics to gain deeper insights into their content's performance. Many respondents report frustration with the perceived opacity of available data in this area.
Meanwhile, entities without direct artist-level access, such as startups, often find themselves completely locked out of primary data sources — forcing them to consider expensive third-party alternatives.
"...better segmented streaming reports from ALL stores, including territory breakdown [and] market size, to better gauge how much a stream is worth."
"...data for various international streaming services outside the U.S. (Melon, JioSaavn, everything on Tencent, etc.)…"
"...skip ratio, at what point are listeners skipping, what do they repeat, pre-cluster of similar artists or genres they are listening to… to create [our] own models and dashboards."
"...validity and legitimacy of data is questionable, decisions are being made on things such as 'fake' streams but there is a severe lack of transparency and the tools used seem unfit for purpose."
The live music sector is notorious for its fragmentation and lack of transparency. Unlike streaming or social media, the live music industry has not established a robust culture of data sharing among key stakeholders such as booking agencies, ticketing platforms, venues, and promoters. Even dominant players like Ticketmaster typically don't provide public access to their data.
Hence, there is strong demand for comprehensive data on ticket sales and attendance figures across musical genres. In the context of live music, our respondents seek diverse insights, including audience acquisition channels, geographic distribution of ticket buyers, and even real-time fan sentiment analysis.
"I would love to access ticket sales data for artists across all genres to help inform our roster's tour routing."
"...artists' touring data: frequency, ticket sales, ticket price, geographic distribution of ticket buyers, booking fees."
"I want to know every person who attends a show... how far did they drive... how did they hear about the show…"
"...having access to real-time data on fan sentiment during live events or digital interactions would be invaluable for tailoring our strategies and content to better resonate with our audience…"
The music industry has long struggled to establish a unified, accessible digital database of music rights ownership. Despite decades of effort, industry-wide publishing metadata remains fragmented, incomplete, and often outdated, making it difficult to maintain and integrate across multiple stakeholders.
Many respondents requested comprehensive publishing metadata in standardized formats. Their primary motivations include optimizing content monitoring processes and streamlining royalty claims processing.
"A unified global PRO database acting as one source of truth for the whole industry that allows everyone to reference up-to-date songwriting claims on a work, or conflicted claims, and extends the work of PRS and MCPS in tying works to recordings."
"PRO data transparency and usability… i.e. royalty rates at the point of reporting, which channels do not pay/sample days at major networks in key territories… more international data from international PROs…"
"Songwriter credits available through APIs of DSPs – e.g. Spotify, Apple – would be very helpful for the Publishing industry to be able to identify infringing content."
"Broader adoption throughout the digital supply chain of ISO identifiers for Performing Artists and Contributors (ISNI) and for Compositions (ISWC) to increase process automation and decrease text-string matching and manual cleanup."
Other areas where respondents wanted more visibility included royalties/sales data, social media performance, marketing and attribution, radio airplay, and wider market trends.
One-third of respondents indicated a desire for data even further beyond these categories we derived, which demonstrates the breadth of potential data that could be used to better inform music companies and their decision-making.
"I'd like to understand the causes of an artist's growth… events that influenced their trajectories, both intentional and unintentional/world events plotted against their corresponding points in growth charts."
"…followers/listeners of independent (community) vs commercial/national broadcasters (mainly radio)… impact of radio on streams/sales…"
"…revenue and profit generated from all music licensing, including specific segments such as sync and public performances, broken down by segments as well as countries."
"The social media to DSP pipeline data that shows which social media platform contributes most number of streams to what DSP."
From fan data fragmentation, to restrictions on first-party data access from DSPs and ticketing companies, to the lack of a centralized database for comprehensive publishing information — accessibility and cost are the top barriers our respondents face to getting the music data they want.
What are the main barriers preventing you from accessing or using the data on your wishlist?
N=445
Over 40% of respondents are unsure about, or unable to assess, the ROI from their company's current use of data relative to resources invested.
Companies at different stages of data adoption face distinct challenges in measuring ROI. While larger companies often have dedicated data teams and established ROI metrics, smaller firms frequently struggle to isolate data-related expenses from other operational costs, complicating value assessment.
Despite ROI uncertainty, there was a pervasive feeling among our respondents of a "data arms race," driving continued investment even without clear ROI. However, our findings suggest that maximizing data's value extends beyond mere acquisition. The key lies in contextualization — understanding how acquired data fits into the broader music ecosystem and contributes to achieving tangible, sustainable business outcomes. Companies need to prioritize this "why" to optimize their data strategy.
How would you rate the return on investment (ROI) from your company's current use of data and analytics, relative to the resources invested?
N=600
Beyond challenges around measuring ROI, smaller music companies (10 employees or fewer) are also navigating data investments with less resources than their larger counterparts.
49% of respondents working at smaller companies reported a data budget of $2,000 or less, compared to 8% of those working at larger companies. This feels especially restrictive considering how premium subscriptions to third-party music market intelligence platforms can cost anywhere from $500 to $4,000 annually per individual account.
Data budget for smaller companies
of music professionals working at smaller companies reported a data budget of $2,000 or less
Less than a third of respondents say their company currently provides training or resources for building data literacy.
Despite rising data budgets, companies face trade-offs among additional hiring, training, and acquiring more data and tools for their operations. Generally, there seems to be a preference to bring people in with existing data skills, rather than train from within the company.
Does your company provide training or resources to help employees improve their data literacy and skills?
N=600
For each type of tool we asked about, respondents tended to report that only "a few employees in key positions" had access.
This limited access can be attributed to several factors:
- 1. Cost: Many music data tools and APIs require additional per-user subscription fees, making enterprise-level access cost-prohibitive for smaller teams.
- 2. Data literacy concerns: Data teams may restrict access to prevent misinterpretation by untrained individuals who might draw inaccurate conclusions.
- 3. Company policies Even for in-house solutions, access remains limited, suggesting organizational or cultural barriers to data democratization.
Despite the general ubiquity of data, these factors create both internal and external obstacles to truly democratizing access and fully meeting current levels of industry demand.
What portion of your company’s employees has access to first-party music analytics?
N=320
What portion of your company’s employees has access to third-party music market intelligence tools?
N=280
What portion of your company’s employees has access to distributor analytics?
N=153
What portion of your company’s employees has access to in-house analytics?
N=268
What portion of your company’s employees has access to data from CRM tools?
N=243
Respondents cited significant challenges with analytics tools provided by streaming platforms and distributors, particularly in functionality, data visualization, and access to detailed information.
In this world, streaming services and distributors are de facto gatekeepers — deciding what performance stats and metrics are shown to artist teams, and how . Top-down decisions by these platforms are highly consequential, as they effectively limit the range and types of analyses that artists and their teams can do.
This selectivity also has broader second-order implications for business prospects and access to commercial opportunities , considering the lack of clarity on how specific metrics translate to various editorial and algorithmic outcomes on DSPs.
What are the key challenges your company faces when using first-party music analytics?
N=320
What are the key challenges your company faces when using distributor analytics?
N=153
Although generative AI is one of the hottest topics in music and tech today, enterprise adoption in music is lagging. Under 40% of our respondents reported that their company leverages AI and machine learning in its data operations.
When segmenting AI adoption by company size, we see a notable gap: Just 34% of respondents from smaller companies indicated "yes," versus 48% from larger companies. Broadly speaking, large corporations with ample resources and extensive training data are better positioned to develop their own AI models in-house, and experiment more deeply with emerging AI solutions.
Note: Our survey specifically asks about using AI/ML in the context of data operations, which does not necessarily include using generative AI products such as ChatGPT in non-data contexts (e.g. content creation for marketing purposes).
To your knowledge, does your company leverage AI and machine learning in its data operations?
N=600
Many obstacles remain for companies looking to implement and scale AI solutions:
- Lack of internal expertise: Since AI is still a burgeoning technology, there is understandably a lack of internal AI skills at many companies, and hiring people with relevant skill sets can be costly.
- Data quality: In-house solutions often face a lack of high-quality training data that is needed to build trustworthy, enterprise-scale models.
- Time and resource intensity: Training AI models is a lengthy, expensive process, often taking months or even years to become production-ready. This extended timeline necessitates large, consistent cash flows to support — a challenge even for larger companies in the industry.
What are the key challenges your company faces in implementing and scaling AI solutions?
N=239
In our free responses, there was a full range of emotions expressed on AI, from anxiety and doomsaying to excitement and hopefulness.
In the spirit of practicality, we wanted to highlight some concrete use cases that respondents cited for how they envision AI transforming their company's data operations.
"…automatically generate comprehensive amounts of metadata for entire music catalogues… AI already allows tagging from audio files including genre and mood, instruments and tempo…"
"Massive transformation of day-to-day office work through smart automation and ChatGPT-like layers on top of many tools that currently require technical expertise to use; democratization of analytics…"
"…streamlining our ability to curate for the businesses we serve and personalize music selections… bringing personalized listening more fully to scale while limiting and leveling up the role of humans in music curation…"
"I expect AI to be integrated deeper in administrative tasks, marketing content creation, paid marketing optimization, data analysis, data and content research tasks (always operated and supervised by humans, however)."
The state of Web3 and crypto has changed significantly from just a few years ago. Much of the previous interest has shifted to new technologies such as generative AI.
Reflecting this downward trend, only 14% of respondents indicated they were experimenting with Web3 in their operations — down from 24% in 2023. Unsurprisingly, those working at music-tech startups were significantly more likely to be leaning into Web3 (28%), versus those working at traditional music businesses like indie labels and artist services firms (12–13%).
Note: Given that Water & Music's community over-indexes on Web3 interest, we suspect that these figures still overinflate actual interest in using Web3 across the music industry.
To your knowledge, does your company leverage Web3 and blockchain technology in its data operations?
N=600
Music companies committed to Web3 have gone back to the drawing board. Among respondents who engaged actively with Web3 in their data operations, general experimentation was reported as their primary goal in using the technology, as companies continue to search for sustainable use cases.
A notable use case that has fallen is tokenizing music copyrights. In 2023, 83% of music-tech companies surveyed that were using Web3 were leveraging it for this use case. This year, only 37% reported doing so.
What are your company's primary goals when engaging with Web3 & blockchain technology?
N=84
Ironically, Web3 was once touted as a solution to many of the fan data issues that still plague the industry today.
However, its promise remains largely unfulfilled, in an industry still grappling with longstanding data challenges. Contributing factors include:
- Regulatory uncertainty — the most-cited challenge with Web3 in our sample
- Technical complexity in integrating Web3 with existing company operations
- Declining market interest, making it difficult for companies to find and retain talent with Web3 expertise
- Resource competition with other areas like AI and traditional data analytics
What are the challenges your company faces in adopting and implementing Web3 and blockchain technologies?
N=84
Similar to AI, we asked respondents about how they envision Web3 and blockchain technologies might change their company's operations in the future. Responses tended to be more mixed than AI, with unclear consensus on whether it holds transformative potential for the industry.
When it comes to data applications, commonly cited use cases for Web3 included decentralized databases and metadata standardization.
"Blockchain adoption for master, publishing, and artists could provide more accurate access to information than current CWR and DDEX pushes."
"…use NFTs & blockchain to hold all metadata on ownership, licensing, splits, etc. on each recording & composition so all that info is in a single centralized location…"
"…my music tech start up is creating a blockchain public ledger solution in an effort to create more transparency around copyright records and discrepancies between all of the privatized databases…"
"…ideally a well-considered blockchain database could solve many issues around business rules/rights-holder compensation… length of use, global/territory restrictions, rights to remix/augment, publishing/writing splits…"
08 Conclusion: What comes next?
The majority of respondents (59%) expected their company to increase data budgets in the next year, with only 1% saying they expect some sort of decrease. This trend could be driven by several factors — from greater attention being paid to back catalogs, to the need for operational efficiency at labels as revenue growth lags behind release volume.
Companies are investing in improvements across all aspects of data strategy and operations, with data analysis & visualization, machine learning, data quality management, and data acquisition cited as top priorities.
How do you expect your company's data analytics budget to change over the next 12 months?
N=600
Which of the following subsets of data analytics do you expect your company to invest in over the next 12 months?
N=600
Our recommendations
As music data budgets continue to rise, it's clear that strategic adaptation and thoughtful implementation will be key to long-term success.
Based on our survey findings, we offer the following recommendations for music professionals seeking to improve their data strategies:
1. Embrace industry standards while remaining flexible
Our research reveals clear leaders in certain areas of the music data ecosystem, such as AWS and Google Cloud for hosting, Chartmetric for market intelligence, and Spotify for Artists for platform-specific analytics.
While leveraging these widely-adopted solutions can provide a solid foundation, it's crucial to remain adaptable. The music data landscape is diverse and evolving, particularly in areas like CRM where no clear industry leader has emerged. Regularly evaluate new tools, and be open to adopting emerging solutions that could become tomorrow's standards.
2. Enhance data ROI transparency and measurement
With over 40% of respondents uncertain about their data investments' ROI, there's a clear need for better measurement and communication around the value of data. Consider implementing these strategies:
- • Develop and share internal case studies showcasing data-driven successes
- • Document how data informs key strategic decisions
- • Gather feedback from clients and artists on the impact of data-driven initiatives
- • Run experiments to understand the causal impact of data-driven strategies and efforts
- • Compare performance metrics before and after implementing data-driven approaches
By more clearly quantifying and communicating the value of data investments, one can justify increased budgets and drive broader adoption within an organization.
3. Navigate data democratization thoughtfully
While the industry has traditionally defaulted to data silos, there's growing recognition of the value in democratizing access. At the same time, this process requires careful consideration, as there is no one-size-fits-all solution. Consider these steps:
- • Assess your organization's specific needs and culture around data access
- • Start with incremental steps, such as providing more employees access to key dashboards or reports
- • Invest in data literacy training to ensure team members can effectively utilize the data they're given
- • Regularly review and adjust your data sharing policies as your organization evolves
4. Prepare for an AI-driven future
As AI continues to reshape the music industry — and society at large — it's crucial to start laying the groundwork for its integration into your data operations:
- • Identify potential use cases for AI within your existing workflows
- • Invest in building or acquiring the necessary technical expertise
- • Address data quality issues to ensure in-house AI models have reliable training data
- • Develop clear ethical guidelines for AI use in your organization
Future research ideas
While our survey provides a broad overview of the current state of data in the music industry, there are numerous opportunities for deeper, more targeted research.
Here are key areas we believe warrant further investigation:
Deeper industry cohort analysis
More granular analysis is needed on how music data impacts specific industry verticals, especially in live events and publishing. While underrepresented in our survey sample, these comprise two of the most hyped verticals in the music business today, and were two of the top categories mentioned in our industry “data wishlist.”
A follow-up report focused on fast-growing international markets like Asia, Africa, and Latin America would also be valuable, as it could shed light on region-specific challenges, data collection methods, and tooling preferences that differ significantly from North American and European markets.
The role of non-music data analytics
While this report focused on music data, non-music data analytics dashboards (e.g. Google Analytics, Meta Business Suite) were the second most popular tooling category among our respondents.
A future study could examine how music companies integrate non-music solutions — across social media, ecommerce, and general business intelligence — into their daily operations and strategic decision-making.
This comparative approach could potentially address current tooling limitations in streaming analytics platforms and royalty collection systems, and provide more actionable insights on how to optimize music analytics processes in more holistic ways.
The impact of generative AI on data workflows
As generative AI rapidly evolves, it will be critical to understand its potential to automate and augment data tasks in music, as well as its long-term impact on skill development and career trajectories across the industry.
Future research could focus on:
- • AI’s role in processing, analyzing, and contextualizing large music datasets
- • The potential of AI to democratize data analysis for non-technical staff
- • Ethical considerations and best practices for implementing AI in music data workflows
Thank you for reading!
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