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Magic 8 Multi-Database Analyzer 

 

ABSTRACT:

 

Business Intelligence, due to it's unique manifestation within each company, suffers from many types of failures and often is "iterated into the ground" (Hughes et al, 2008). BI spawned out of the Decision Support Systems of the early 1990's, and today, 97% of companies with revenues exceeding $100 million use some form of business intelligence (Golfarelli et al, 2004), (Chen, Chiang, Storey, 2012). Many companies start a data warehouse project without fully understanding their existing legacy systems, their architecture, company business processes, and the state of data within the systems. Much of the research that has been done within BI has consisted of different methodologies of development for the solution, (Kimball Model, ERD model), and very little to do with "preparation" or "readiness" (Fitriana, Eriyatno, Djatna, 2011). Of the available "packaged solutions" available for BI, most are built to fit a larger audience, and specific company customization's either become costly or unavailable. Ultimately, the literature points towards understanding what systems are in place, what business processes exist or should exist, and what data will make up the "information" comprised within the BI systems "dashboards".  Figure 1 showcases how current ETL processes drop "bad" data and do little to stop inconstent data from making into the Data Warehouse.  Through this process, error is induced (in the form of incompleteness - aggragations, summations, and transforms on limited data does not expose reality) and the BI solution loses effect.

 

 

 

                                                                                                                                        References:

 

Chen, H., Chiang, R., Storey, V. (2012). Business intelligence and analytics: from big data to big impact. MISQ, 36(4), pp 1165-1188. Retrieved from http://www.misq.org/skin/frontend/default/misq/pdf/V36I4/SI_ChenIntroduction.pdf

 

Fitriana, R., Djatna, T. (2012). Business intelligence design for decision support dairy agro industry medium scaled enterprise. International Journal of Engineering & Technology, 12(5), pp 1-9. Retrieved from http://www.ijens.org/Vol_12_I_05/120605-3434-IJET-IJENS.pdf

 

Golfarelli, M., Rizzi, S., Cella, I. (2004). Beyond data warehousing: what’s next in business intelligence? DOLAP ’04 Proceedings of the 7th ACM International Workshop on Data Warehousing and OLAP, pp. 1-6 Retrieved from http://student.bus.olemiss.edu/files/Conlon/Others/Others/BusinessIntelligence/ Beyond%20data%20warehousing_whats%20next%20in%20business%20intellige nce.pdf

 

Hughes, R. (2008). Agile data warehousing. Ceregenics, Inc. iUniverse, Bloomington, IN. Retrieved from http://books.google.com/books?hl=en&lr=&id=AFtxwlJpqZQC&oi=fnd&pg=PR7 &dq=%22business+intelligence+development%22&ots=UqaozzZTaA&sig=arxff 7xsJhEaIDl8aiNzXhIKY- o#v=onepage&q=%22business%20intelligence%20development%22&f=false

Figure 1

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