Business Intelligence for Small and Middle-Sized Entreprises

TL;DR

This paper reviews web-based and in-memory business intelligence approaches suitable for small and medium-sized enterprises, focusing on lightweight, cost-effective solutions for real-time data analysis.

Contribution

It provides a comprehensive review of existing web-based and in-memory BI tools tailored for small and middle-sized enterprises, highlighting their suitability and limitations.

Findings

Web-based BI tools are suitable for small enterprises.

In-memory processing offers real-time analysis with lower storage costs.

Most existing solutions are adapted from large-scale enterprise systems.

Abstract

Data warehouses are the core of decision support sys- tems, which nowadays are used by all kind of enter- prises in the entire world. Although many studies have been conducted on the need of decision support systems (DSSs) for small businesses, most of them adopt ex- isting solutions and approaches, which are appropriate for large-scaled enterprises, but are inadequate for small and middle-sized enterprises. Small enterprises require cheap, lightweight architec- tures and tools (hardware and software) providing on- line data analysis. In order to ensure these features, we review web-based business intelligence approaches. For real-time analysis, the traditional OLAP architecture is cumbersome and storage-costly; therefore, we also re- view in-memory processing. Consequently, this paper discusses the existing approa- ches and tools working in main memory and/or with web interfaces (including freeware tools), relevant for small and middle-sized enterprises in decision making.