Document Processing · Enterprise Knowledge Search · Risk Management
For NDUS to compete in the fierce higher education market, each institution must work together; however, its data teams lacked the infrastructure to collaborate and scale its use cases. Lacking a modernized data and AI infrastructure, NDUS spent time on manual tasks rather than capitalizing on innovation for speed, ease and cost-cutting across all departments.
Reported impact
North Dakota University System (NDUS) achieved measurable business impact on Databricks, including 10–20x Faster using a comprehensive LLM for policy search and response; 2x Decrease in time to market, from 1 year to 6 months; 100% Reduced procurement time and costs leveraging Databricks on Azure.
The North Dakota University System (NDUS) includes 11 institutions, all governed by the State Board of Higher Education (SBHE). Critical to mitigating risk and remaining in good standing, NDUS relies on thousands of internal policies and state laws. Without a data and AI platform, the NDUS data teams spent considerable time wading through pages, references, codes and contracts to ensure compliance with regulations. NDUS needed to modernize its data architecture to accelerate this process for faster time to insights and market. Already an Azure customer, NDUS took advantage of the Databricks Data + AI Platform integration to introduce generative AI for rapid policy search and response.
Unstructured documents and reference data lands in Databricks and is transformed into curated datasets
100% Reduced procurement time and costs leveraging Databricks on Azure
10–20x Faster using a comprehensive LLM for policy search and response
2x Decrease in time to market, from 1 year to 6 months
The North Dakota University System (NDUS) has five community colleges, four regional universities and two research universities with approximately 80,000 students, faculty and staff.
The North Dakota University System (NDUS) includes 11 institutions, all governed by the State Board of Higher Education (SBHE).
Review how this reference was scored and trace each normalized claim back to its documented source.
Scoring methodology
Customer Reference Value measures usefulness as a customer reference. Evidence Confidence measures the quality and traceability of available evidence, not independent verification.
Traceability
Field coverage shows which facts each source supports. Dates are shown only when documented by the source.
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