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Situation

The client, a prominent university with a vast network of students, faculty, staff, and alumni, faced data gaps and differences due to the expansion of systems over the years. This hindered their transformation and growth efforts. A comprehensive analysis was conducted, revealing the following:

  • Analyzed 7 systems, including those for students, programs, alumni, HR, and billing.
  • Identified 9-21% duplications across all key data elements.
  • Inconsistency in key data elements impacted more than 70% of records.

The source of truth (main data repository) had missing records that were available in other systems. Data was not flowing efficiently between systems, resulting in data gaps, inconsistent customer experience, and potential revenue loss. Marketing and finance departments experienced significant operational inefficiencies due to data gaps and inconsistencies.

Solution

To address these challenges and enable data-driven transformation and growth, the following solutions were implemented:

  • Full-scale clean-up strategy and Routine integration: A comprehensive clean-up strategy was developed, encompassing data deduplication, data consolidation, and data standardization. Routine’s capabilities were leveraged for continuous evaluation and instant clean-up options, ensuring ongoing data quality improvement.
  • Process and system changes: Initiating process and system changes were crucial to prevent future inconsistencies. This involved revising data entry procedures, implementing data governance practices, and establishing data integration protocols to ensure smooth data flow between systems.
  • Continuous data monitoring: Routine’s AI-powered engine was deployed to continuously monitor data quality and identify any new gaps or inconsistencies. This allowed for proactive identification and resolution of issues, ensuring data integrity and reliability.
  • Scaling discovery and change implementation: The clean-up strategy and process improvements were extended to 13 additional systems beyond the initial seven. This comprehensive approach ensured that data gaps and inconsistencies were addressed across the university’s entire data ecosystem.

Results

The implementation of the solution led to significant improvements for the university:

  • Reduction of data duplications: By addressing the duplications in key data elements, the university achieved a more streamlined and accurate database, enhancing operational efficiency and data integrity.
  • Improved data consistency: Resolving inconsistencies in key data elements positively impacted more than 70% of records, leading to improved reporting accuracy, enhanced decision-making, and a better overall customer experience.
  • Enhanced operational efficiency: Marketing and finance departments experienced reduced operational inefficiencies as data gaps and inconsistencies were minimized. This led to improved workflows, increased productivity, and potential revenue optimization.
  • Future-proofed data systems: With process and system changes implemented, the university established a foundation for maintaining data consistency and preventing future inconsistencies as new systems and technologies are integrated.

By utilizing Routine’s capabilities and implementing a comprehensive clean-up strategy, the large North American university successfully addressed data gaps and inconsistencies, laying the groundwork for data-driven transformation and growth.

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