Background and Problem
Our client, a North American rights management organization, supports 150,000+ songwriters, composers, music publishers, and visual artists, licensing the performance and reproduction of music and audio-visual art.
The client wanted to improve data quality and data integration across the 40 million master records siloed in their older, overly complex legacy system. The legacy system’s performance proved inadequate for the task of managing increasing volumes of new membership registrations over time, especially given the entertainment industry’s expanding deployment of new streaming and internet channels for music/video consumption.
In order to provide better service, the client wanted to significantly reduce the time required to calculate and deliver performance royalties to their members. In addition, they also wanted to map usage data (song play transactions) to non-usage data (core master data, such as composers, titles, publishers, etc.)
Project Requirements and Data Challenges
- Real-time data tracking across internet entertainment channels (i.e., Spotify, Amazon Music, YouTube, etc.), plus the ability to provide data quality checks more accurately and quickly across all data entities and attributes.
- Consolidating and interrelating three business domains with 25 million musical works, eight million AV works, and eight million interested parties (songwriters, composers, music publishers, visual artists, and licensees).
- Consolidating 38 tables, 2,563 attributes, and three levels of hierarchies in the initial phase (and adding more as the project continues), including a) people (creators, publishers, licensees,) b) things/assets (titles, running times, royalties splits), and c) royalties payment transaction data.
- Providing the backend power to manage and implement 1 billion+ rows/records created through consolidation and entities/attributes realignments.
Adastra built and deployed AstraMuse, our subscription-based music royalty management solution powered by Ataccama ONE technology. Our solution utilized the Ataccama ONE platform modules for Master Data Management and Data Quality Management (DQM). Ataccama ONE MDM is an advanced, scalable, highly available module for managing and consolidating data, and providing master and reference data management tasks. Ataccama ONE DQM is a powerful, business rule-driven platform module for complex data curation that enables you to transform, standardize, cleanse, validate, correct, and enrich your data, and prevent incorrect data from entering your systems.
AstraMuse’s MDM (Master Data Management) capability allowed us to hierarchically link and consolidate all relevant membership master data, including data representing musical works, audio-visual works, and their respective owners across the organization’s creative community.
This solution facilitated the ingestion, cleansing and processing of structured and unstructured data into a Master Data Repertoire (Repository). The Flattened Master Data could then be used for matching for royalty calculations. The Master Data Repertoire could be synced with other connected systems like CISAC, SUISA to enhance the matching and distribution process and could be used by data stewards to search or view records. Going forward, the solution will provide a backend engine powering real-time data quality checks.
With Ataccama’s capabilities, Adastra’s solution was able to:
- Create a canonical model for all attributes across the three domains to standardize all legacy system data formats.
- Build data flows into the MDM hub from 5 different input systems via batch and streaming interfaces, where some data is interchanged with the MS Azure messaging platform via JMS APIs.
- Expose golden records to consuming systems via web services, including both SOAP and RESTful formats.
- Create a master layer supporting upwards of 600 million records, including instance data from all sources, totalling 1.5 billion records in the MDM system.
Our solution also provided match explainability, making it easy to audit and reconfirm matches for royalty calculation verification. AstraMuse offered White Box matching based on business rules, and recorded match rules as well as resolutions made by data stewards for greater transparency.
The solution provided AI-assisted data profiling for data contents recognition and was able to automatically map standard data elements. Our matching rules were powered by Machine Learning, creating a self-improving machine engine capable of making learned matches, that could either be automated or steward-driven. The solution also incorporated AI & ML to assist data steward workflows, by suggesting resolutions and creating automated feedback loops which are invaluable during bulk data processing. This data was then prepared and ready to be provided to the client’s financial ERP solutions to calculate royalty distribution.
Where previously, the client’s staff needed to manually go through huge volumes of records and match them, with AstraMuse, they just need to search, review, and confirm AI generated matches for greater match accuracy. With our solution’s Intelligent Matching capabilities, the client was able to do away with manual matching and instead leverage the power of AI and Machine Learning to increase the speed and accuracy of record matching. The match accuracy was increased by 20%, reducing the need for human intervention and greatly increasing processing speed. This allowed our client to pay its members on a monthly, rather than a quarterly, basis.
We were also able to eliminate the performance issues they were facing with their legacy system and provide them with an efficient way of integrating data and managing data quality. Our rule-driven data quality solution streamlined ongoing data quality activities and improved the quality of their records and calculations.
With the matching time reduced from days to hours and manual intervention for matching limited to stewardship, our client witnessed a 40% increase in overall business efficiency. This increased productivity will provide them with a growth engine to acquire a bigger book of business and expand their operations.
Need a more efficient, accurate royalty processing approach? Speak with our experts to learn more about AstraMuse.
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