McGraw-Hill Education

Customer Case Study

McGraw-Hill Education believes that today’s learning extends beyond the classroom, beyond one format, beyond a singular style. Through analysis of massive volumes of student and educator engagement data McGraw-Hill delivers intuitive products that adapt to the individual and capture deep insights into how learning happens.

Industry

Media and Entertainment

Vertical Use Case

Improve user engagement and retention
Personalized learning

Technical Use Case

Ingest/ETL
Machine Learning

The Challenges

More effective data analytics: Product development workflows suffered from inefficient data analysis.
Improving time to market: Slow response times from data pipelines and launching of product initiatives were falling behind.
Engaging and adapting to learners: Student abandonment levels were unacceptably high and adaptive learning features were failing to improve the student experience.

The Solution

McGraw-Hill Education depends on Databricks Unified Analytics Platform as the foundation of their innovation in technology initiatives. Databricks has helped McGraw-Hill Education:

Gain deep insight: More than 10 million individual student interactions have been successfully captured and analyzed increasing student retention by 19%.
Deliver personalized learning: McGraw-Hill Education’s users machine learning to analyze students’ levels of comprehension and delivers recommendations ‑ improving student pass rates by 13%.
Accelerated time to value: With Databricks, they can deliver innovative solutions to over 5.5 million students more quickly and efficiently.
Reduced total cost of ownership: Moving to a fully managed platform in the cloud has reduced operational costs by 30%.

Having an agile innovation workflow is critical for McGraw-Hill Education. Databricks Unified Analytics Platform is at the center of our ecosystem and underpins our innovation pipeline and workflows.

Alfred Essa
VP of Research and Data Science, McGraw-Hill Education

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