A data management platform in which the data can no longer be used for agile and value-added analytics, for example due to a lack of data quality, intelligibility, or comprehensibility.
Published in Chapter:
Enterprise Data Lake Management in Business Intelligence and Analytics: Challenges and Research Gaps in Analytics Practices and Integration
Mohammad Kamel Daradkeh (Yarmouk University, Irbid, Jordan)
Copyright: © 2021
|Pages: 22
DOI: 10.4018/978-1-7998-5781-5.ch005
Abstract
The data lake has recently emerged as a scalable architecture for storing, integrating, and analyzing massive data volumes characterized by diverse data types, structures, and sources. While the data lake plays a key role in unifying business intelligence, analytics, and data mining in an enterprise, effective implementation of an enterprise-wide data lake for business intelligence and analytics integration is associated with a variety of practical challenges. In this chapter, concrete analytics projects of a globally industrial enterprise are used to identify existing practical challenges and drive requirements for enterprise data lakes. These requirements are compared with the extant literature on data lake technologies and management to identify research gaps in analytics practice. The comparison shows that there are five major research gaps: 1) unclear data modelling methods, 2) missing data lake reference architecture, 3) incomplete metadata management strategy, 4) incomplete data lake governance strategy, and 5) missing holistic implementation and integration strategy.