Although I have not worked on a traditional big-data project, I have worked with a large institutional dataset for academic program audits. The data included program recognition files, course descriptions, syllabi, learning outcomes, faculty qualifications, instructor records, and multi-year administrative documents. It was obtained from university departments and different internal systems.

Three of the four defining features were particularly present. Volume was evident in the large number of programs, courses, instructors, and documents reviewed. Variety was present because the information came in different formats, including Word documents, PDFs, spreadsheets, and scanned files. Veracity was a major concern because the data sometimes contained missing information, duplicate records, outdated documents, or inconsistencies between sources. Velocity was less significant, although updated files and corrections were received regularly throughout the review process.