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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.


This research aims to define an autonomous method to “scan” database systems, determine their data validity, and intelligently match/connect the datasystems through use of an autonomous Entity-Relationship diagram, in effect to provide a company with the needed actionable items to either (1) clean up data systems, and or (2) move forward with the ER model and further develop their BI solution.











































































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


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


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 Beyond%20data%20warehousing_whats%20next%20in%20business%20intellige nce.pdf


Hughes, R. (2008). Agile data warehousing. Ceregenics, Inc. iUniverse, Bloomington, IN. Retrieved from &dq=%22business+intelligence+development%22&ots=UqaozzZTaA&sig=arxff 7xsJhEaIDl8aiNzXhIKY- o#v=onepage&q=%22business%20intelligence%20development%22&f=false

Until January 1, 2015, we will be conducting research focused on helping a company understand the quality of their source data through development of a software solution that exposes weaknesses and strengths, dynamically, within a companies data structure.


We are seeking participants (organizations or companies) that run on Microsoft SQL and that would like to analyze their data sets (multiple data systems at once).  The software tool will run in memory, and will not perform any actions within the production data systems beyond select statements. The tool will effectively detail the consistency and quality of the data.  No company data will be retained and the tool will be provided free of charge. 


The concept is to: analyze existing data, find relationships between data systems, and provide an understanding of data quality that a company can then (1) clean up data entry rules/reqs, and (2) proceed into business intelligence design with a visual "roadmap" and a better understanding of "truth" that will evolve from the BI systems.



Thank You.


American Code & Development LLC

Figure 1

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