Data Lake: Strategic Challenges for Small and Medium Sized Enterprises

Abstract

The main purpose of the chapter is to discuss the nature of analytical systems based on data lake architecture and their abilities to support different business objectives. In software engineering, empirical methods play a significant role in order to reach research objectives (Wohlin et al. in Empirical methods and studies in software engineering. Springer, Berlin, 2013 [1]). We decided to examine the proposed ideas using a prototyping approach. Software prototypes can be perceived as design artefacts and they can support creativity, communication and early evaluation (Beaudouin-Lafon and Mackay in Human computer interaction handbook: fundamentals. CRC Press, 2007 [2]). Prototyping can be a good way to merge this process with agile principles (Böhmer et al. in Proceedings of the 21st international conference on engineering design (ICED 17). Design methods and tools. The Design Society, Vancouver, 2017 [3]; Tanvir et al. in International conference on engineering, computing & information technology (ICECIT 2017), pp. 50–54, 2017 [4]). This research method has been applied in the chapter, and the final prototype of a light data lake (LDL) is presented in the third section. Additionally, the authors present maturity level modeling as a supported process to implement LDL in SME organizations. The last section describes the maturity model that was achieved by the LDL prototype.