In-memory Business Intelligence: Verifying its Benefits against Conventional Approaches
Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Business intelligence project failures in organizations derive from various causes. Technological aspects regarding the use of business intelligence tools expose the problem of too complicated tool for operational users, lack of system scalability, dissatisfied software performance, and hard coded business requirements on the tools. This study was conducted in order to validate in-memory business intelligence advantages towards functionality, flexibility, performance, ease of use, and ease of development criteria. A case study research method had been applied to achieve the goals in this thesis. Primarily, a pilot study was carried out to collect the data both from literatures and interviews. Therefore, the design of test case had been developed. Types of testing can be divided into 2 categories: BI functionality test and performance test. The test results reveal that in-memory business intelligence enhances conventional business intelligence performance by improving the software’s loading time and response time. At the meantime, it was proved to be flexible than rule-based, query-based, and OLAP tools, whereas its functionality and ease of development were justified to be better than query-based system. Moreover, in-memory business intelligence provides a better ease of use over query-based and rule-based business intelligence tools. Pair wise comparisons and analyses between selected in-memory business intelligence tool, QlikView, and conventional business intelligence software, Cognos, SAS, and STB Reporter, from 3 banks were made in this study based on the aforementioned test results.
Place, publisher, year, edition, pages
2013. , 106 p.
In-memory business intelligence, Associative business intelligence, QlikView, in-memory, business intelligence tool, business intelligence
Engineering and Technology
IdentifiersURN: urn:nbn:se:kth:diva-128449OAI: oai:DiVA.org:kth-128449DiVA: diva2:647569
Master of Science - Engineering and Management of Information Systems
Johannesson, Paul, Professor