Customer Case Study: McGraw-Hill Education - Databricks

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

McGraw Hill is focused on using data to understand how students and instructors leverage their content and learning management solutions in order to improve the education experience. Core to this approach is building advance analytics on top of their millions of anonymized student records. Unfortunately, they faced a number of challenges:

  • Need for more effective data analytics: Product development workflows suffered from inefficient data analysis tools built on top of silo’ed data sources.
  • Improving time to market: Slow response times from data pipelines prevented new product initiatives from launching on time.
  • 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 Data Analytics Platform as the foundation for building data driven products and innovations.

  • Reduced Infrastructure Complexity: Databricks provides a fully managed service on AWS that greatly simplifies infrastructure management and reduces total cost of ownership.
  • Interactive Workspace: The data science team is able to collaborate on the data and models via the interactive notebooks.
  • Cluster Management: Significantly simplified the provisioning of Apache Spark clusters with powerful features like Autoscaling, improving operational efficiency.
  • Automated Workflows: Using analytic workflow automation, McGraw Hill is easily able to build reliable and fast data pipelines that allow them to build powerful models that improve the student learning experience.

The Results

Databricks has helped McGraw-Hill Education:

  • Gain deep insight: More than 10 million individual student interactions have been successfully captured, aggregated and analyzed enabling McGraw-Hill to increase student retention by over 19%.
  • Deliver personalized learning: McGraw-Hill uses machine learning to analyze students’ levels of comprehension and deliver 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