Growing data, slowed decision making
Previously, Notre Dame struggled with disparate data, spread across the university. Departments had a difficult time getting data out of the enterprise data warehouse. Even when they succeeded in accessing the data, they struggled to interpret it. Standard reports took about two months to create.
“We knew the data existed, but we were struggling to get to it,” said Chris Frederick, Business Intelligence Manager, Notre Dame.
Each department worked in silos with their own data definitions, leading to inconsistent reporting across campus. If a decision maker had a question like, “How many students do we have?” each department had a different answer based on a small subset of data.
Understanding alumni interaction trends and finding new ways to keep alumni connected are crucial to Notre Dame. The university built its first data warehouse with a legacy warehouse vendor eight years ago. But the system couldn’t scale to Notre Dame’s thriving volume and velocity of data—a result of new, faster fundraising efforts. Queries could often take 30-90 minutes to complete.
“We decided to look into new solutions that could help improve overall performance,” said Frederick.
To keep pace with growing business demands, Notre Dame needed a flexible, scalable, and user-friendly data warehouse and a business intelligence (BI) platform that could keep pace with big data. Faster query performance and support for both concurrency and scalability were top priorities, along with reduced maintenance and overhead.