Title:	METHODOLOGICAL GUIDE FOR A DATA WAREHOUSING PROCESS
Author(s):	Wilson Castillo-Rojas and Fernando Medina-Quispe
ISBN:	978-989-8533-69-2
Editors:	Pedro Isaías and Hans Weghorn
Year:	2017
Edition:	Single
Keywords:	Business intelligence, Data Warehouse, Data Warehousing, DataMart, ETL, OLAP
Type:	Full Paper
First Page:	221
Last Page:	229
Language:	English
Cover:
Full Contents:	click to dowload
Paper Abstract:	The article describes a methodological guide prepared for the development of a Data Warehousing Process (DWP) in a Chilean University. The goal of this DWP, is to generate indicators of academic productivity. For this, a methodological guide is elaborated, integrating diverse approaches and techniques, such as: specification of information requirements, relational modeling, combined development model of Kimball and Hefesto proposals, ETL process (Extraction, Transformation and Load) with a validation phase of indicators, and integrated and interactive visualizations, for the multidimensional analysis of the indicators with the use of dashboards. In this process, three elements incorporated that, in the opinion of the development team, favored the success and effectiveness of the development of the solution. Firstly, how to specify Key Performance Indicators (KPI) in detail, through the development of a template based on those used to specify information requirements in a software process. Second, to explain the incorporation of a validation phase of the KPI obtained in the ETL process, based on Structured Query Language queries (SQL), and the comparison with the data of the operational systems. Thirdly, integrated visualizations configured with an On-Line Analytical Processing (OLAP) tool, which interactively display the related indicators for the same activity under analysis. Because of this data warehousing process, you get a Business Intelligence (BI) platform based on a DataMart (DM) model with two schemes, one star and another snowflake. The first contains the indicators of teacher productivity, and the second, those of scientific productivity, which satisfy the specifications of the KPI, and defined in agreement with the end users. The methodological guide allowed, on the one hand, the complete development of the process in an appropriate time, and on the other hand, to ensure the quality of the indicators generated through an adequate validation phase.
	Go Back