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Scaling a Small Data Team with the Power of Machine Learning
Technology Category
- Analytics & Modeling - Machine Learning
- Analytics & Modeling - Predictive Analytics
- Analytics & Modeling - Natural Language Processing (NLP)
Applicable Functions
- Sales & Marketing
Use Cases
- Predictive Maintenance
Services
- Data Science Services
The Challenge
DAZN, a subscription sports streaming service, was looking to grow their business in existing and new markets. They wanted to enable their small data team to run predictive analytics and machine learning projects at scale. They also wanted to find a way to allow data analysts who were not necessarily technical or experienced in machine learning to contribute in meaningful ways to impactful data projects. The goal was to support an underlying data culture with advanced analytics and machine learning at the heart of the business.
About The Customer
DAZN is a subscription service owned by Perform Group dedicated to live and on-demand streaming of worldwide sporting events. It offers access to more than 8,000 sporting events a year across a wide range of devices to customers in Austria, Germany, Japan, Switzerland, and Canada, with more markets coming soon. In an effort to continue to grow their business in existing and new markets, DAZN wanted a fast, low-maintenance way to enable their small data team to run predictive analytics and machine learning projects at scale.
The Solution
DAZN turned to Amazon Web Services (AWS) and Dataiku Data Science Studio (DSS) for their simplicity in setup, connection, integration, and usability. They were able to get up and running in under one hour. With AWS and Dataiku, the small data team built and now manages more than 30 models in parallel, all without needing to do any coding so that the processes are completely accessible to non-technical team members. They use these models as the basis for a variety of critical processes throughout all areas of the business, including content attribution, advanced customer segmentation, propensity modeling, survival analysis, and natural language processing on social networks for market research.
Operational Impact
Quantitative Benefit
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