Strategy, Action Plan, and Approaches for Business Intelligence in Banking and Mining

Strategy, Action Plan, and Approaches for Business Intelligence in Banking and Mining

Hafeez Niazi (University of South Australia, Australia)
DOI: 10.4018/978-1-5225-5718-0.ch017

Abstract

This case study will analyze the critical success factors and key matters related to the deployment of BI deployment in different organizations. Different organizations have different approaches to making BI available for different business users, divisions, and departments. Data visualization is also one of the important factors which will provide user better reflection of data rather than make them confuse about organization data with too much information in the reports and dashboards. Data quality and diverse standards, which make BI famous in the different organizations, are also analyzed during the investigation of both organizations used in this case study. The case study analysis also shows how BI maturity, governance, and framework are key factors involved in the successful deployment of the BI in different organizations.
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Research Motivation

The BI approach enables organisations to make decisions at the right time to achieve a competitive advantage. Acknowledgement of CSFs is essential for organisations successfully implement BI (Naderinejad, Jafar & Poorebrahimi, 2014). The CSFs include communication, collaboration, innovation, adaptability and leadership. A lack of information and communication can result in costly failures where 60% of companies lose value after five years, 30% have no increase in value and only 10% increase in value (Rud, 2009). Success or failure is not necessarily associated with BI implementation, but instead is determined by organisational and environmental factors (Olbrich, Poppelbub & Niehaves, 2012). The revenue generated from BI platforms reached US$10.5 billion worldwide in 2010 and 80% of BI projects fail (Jafartarokha & Teymournejada, 2012). A BI system is not simply a combination of software and hardware; it requires suitable infrastructure and resources for the longer term. Organisations generate enormous amount of data from external and internal sources but need to present meaningful information to their business users. This information must be clean and based on relevant data because data quality issues alone cost United States businesses over US$600 billion a year (Isik, Jones & Sidorova, 2013).

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