Professional Sports League
Data Platform Modernization for Commercial & Operational Analytics
Project Details
Background
Our client is evolving into one of the most innovative sports leagues in North America. A modern cloud data platform was needed to unify data across all teams in the league to answer larger and more complex questions across all business practices. The goal was to stand-up a net new AWS environment centred around Redshift to build league-level data models on all commercial (e.g., ticket and merchandise sales) and operational (e.g., player performance and partnerships) data, unlocking new analytics capabilities both for head office and individual teams. Other objectives included:
Simplify data management and recurring data collection from teams
Upskill the internal technology team to leverage the new platform for existing and new reporting and analytics
Move away from manual data entry and reporting in Excel and provide a foundation for Tableau reporting and future AI/ML use cases
Challenge
Weeks were spent internally each month manually collecting and collating several data sets submitted by 60+ teams across the league.
Years of historical data across 25 data sources was complex to manage and prone to data errors, delaying the length of time to turn around accurate reporting and dashboards.
A lack of existing data unification model and warehouse prohibited the team from supporting scalable dashboards and analytics, and there was an inability to progress to predictive and ML and AI use cases without a core data estate to provide accessible, trustworth, and secure data.
Outcomes
Data Elephant assembled a small team (2 resources) to work with the client team in collaboratively designing a future State AWS Architecture that would not only integrate well with their existing environment, but support their vision for future modernization around analytics. A Hierarchical Data Model was built to unify all team and league data in Redshift for analytics, and over 25 flat file sources were migrated to the new DW. Ingestion pipelines were designed and built for ongoing data collection and ingestion, and training and enablement was provided for the internal Data Management team to build on and sustain in the future.
Why Data Elephant? Data Elephant was recommended to the client by an existing AWS customer, StellarAlgo, for our expertise in designing and building AWS data platforms, collaborative and flexible engagement model, and ability to provide ongoing management and enhancement to the new data platform over a multi-year term.
Why AWS? AWS provided the best solutions to support the initial journey into the cloud as an enterprise. The platform enabled the client to start small on the initial build of their data platform and provide a robust and extensible solution with an attractive pricing model.
AWS Technologies S3, Glue ETL, Glue Data Catalog, Redshift Serverless, Athena, Step Functions, IAM and Cloud Platform Admin Services