Data Warehouse in Research

Data warehouse

A central repository of structured data, integrated from multiple operational sources, modelled for analytical querying (typically with a star or snowflake schema), and optimised for read-heavy workloads supporting reporting and decision-making.

ByCASRAI Editorial Board

· Last updated 5 Sept 2026

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A data warehouse is a central repository of structured data, integrated from multiple operational source systems, modelled for analytical querying (typically a star or snowflake schema) and optimised for read-heavy reporting rather than day-to-day transactions. The pattern dates to Bill Inmon’s and Ralph Kimball’s competing-but-convergent methodologies of the 1980s-90s and remains the dominant approach to organisational reporting today, whether built on-premises or on a modern cloud platform (Snowflake, BigQuery, Redshift, Synapse) that blurs the older lake/warehouse distinction by combining cheap storage with on-demand SQL compute.

Scope note: data warehousing is a generic information-systems discipline with its own large, well-established body of practice — this entry is not an attempt to cover that ground, which is best learned from the Inmon/Kimball literature directly. The reason it appears in CASRAI’s dictionary at all is narrow: in a research-administration context, “data warehouse” most often refers to an institutional research-analytics warehouse that aggregates a CRIS, HR, finance, and student-records data — or a national funder’s warehouse of award spend and outputs — for institutional or funder-level analytics and reporting, distinct from any single operational system’s own database.

References

Inmon W.H., ‘Building the Data Warehouse’, Wiley, 4th edition, 2005.
Kimball R., Ross M., ‘The Data Warehouse Toolkit’, Wiley, 3rd edition, 2013.

Use in your systems

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