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Service delivery centers are extremely dynamic environments in which large numbers of globally distributed system administrators (SAs) manage a vast number of IT systems on behalf of customers. SAs are under significant time pressure to efficiently resolve incoming customer requests, and may fall far short of accurately capturing the intricacies of technical problems, affecting the quality of ticket data. At the same time, various data stores and warehouses aggregating business insights about operations are only as reliable as their sources. Verifying such large data sets is a laborious and expensive task. In this paper we propose system h-IQ, which embeds a grading schema and an active learning mechanism, to identify most uncertain samples of data, and most suitable human expert(s) to validate them. Expert qualification is established based on server access logs and past tickets completed. We present the system and discuss the results of ticket data assessment process.