Design and Implementation of an ETL Approach in Business Intelligence Project

Prioritising data items for business analytics: Framework and application to human resources

“…Not surprisingly, multi-purpose data items tend to be judged as more relevant by practitioners in our experience and are therefore more frequently collected (see section 4.3). Second, the extract-transform-load (ETL) process of cleansing data items from legacy systems and external sources to transfer them to a new BI system typically accounts for more than 50% of the time and costs of a BI project (Davenport et al 2001, Shen et al 2012, Wang et al 2012. Third, in a study of small and medium enterprises (SMEs), which have previously setup or are in the process of setting up a BI system, 13 of 20 SMEs mentioned a lack of existing data items for business analytics as a major barrier for succeeding in their analytic strategy (Olszak & Ziemba 2012, see also Marx et al 2011).…”

Dimensions of AutomatedETL Management: A Contemporary Literature Review

“…In line with the definitions mentioned above and pragmatic application of software engineering, software engineering can be advocated as a systematic process that enables the developments right from the conceptual stage to the implementation stage for an application system development. While the process from the conceptual stage to implementation can be executed in any manner, adapting the right steps and procedural developments can lead to optimal utilization of resources, developing more comprehensive solutions, which can yield better results [5], [6]. Handling software engineering successfully comprises scores of data to be handled as integral to the system development and management.…”

“…Effective use of such data management is critical for software engineers. Such data can work as a potential and actionable for improvisation in the software engineering process and its accurate delivery [6]. In the other dimension, effective ways in which the organizations garner the data indicate how the teams can monitor the work progress, the quality, and the quantum outcome.…”

“…In general, KPIs and KPIs can be the highly resourceful value-driven mechanism for software engineering of the ETL frameworks. Unless the ETL tools are designed more in customization to the functional flow essential for the organization, the outcome can be challenging [6].…”

“…Thus, taking such factors into account, it is of paramount importance that the businesses focus on combining factors that lead to a quality outcome. Some of the earlier proposed models for ETL and currently in real-time practice, like the machine learning models, can help the project management teams adapt the best possible solutions for data handling, which can play a significant role in the enterprise systems [5], [6]. Thus, this recent research literature review focuses on applying the machine learning models in the ETL process, certain key elements of the software engineering process like the techniques, tools, systematic approaches, and wider implementation scope.…”