This article aims to improve learning on the process of unstructured data use. To develop the principal objective this study explores the relationship between big data (Volume, Velocity, Variety, Veracity and Value) and the DIKW pyramid (Data, Information, Knowledge, Wisdom). This article related two theoretical bases (Big Data and DIKW pyramid) to improve the learning on unstructured data. The reseach propositions are: The toughest aspects of unstructured data are data capture and data treatment; The knowledge generation using unstructured data depends of preliminary tacit knowledge. A multiple case study methodology was used to develop the article. A guide research was developed to investigate the research propositions in two companies. The article improve learning on the process of unstructured data. A multiple case study confirm the research propositions: The toughest aspects of unstructured data are data capture and data treatment; The knowledge generation using unstructured data depends of preliminary tacit knowledge. This article contribute improving the knowledge on unstrutured data use. Through two case study the reseach propositions were confirmed: Capture and data treatment are the toughest aspect and the knowledge Generation depends of preliminar tacit knowledge. This article explore two case study to identify through aspects of unstructured data use and a dependecy of tacit knowledge. The factors identified helps to learn about unstructured data use and identify factors that must be considered in knowledge management.