McGraw-Hill Education

Customer Case Study

McGraw-Hill Education

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.

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