Agile Methodologies for Business Intelligence

Agile Methodologies for Business Intelligence

Deanne Larson (Larson & Associates, LLC, USA)
DOI: 10.4018/978-1-61350-050-7.ch005
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Agile methodologies were introduced in 2001. Since this time, practitioners have tried to create and apply Agile methodologies to many delivery disciplines. This chapter will explore the application of Agile methodologies and principles to business intelligence delivery. The practice of business intelligence delivery with an Agile methodology has yet be proven to the point of maturity and stability; this chapter will outline Agile principles and practices that have emerged as best practices and formulate a framework to outline how an Agile methodology could be applied to business intelligence delivery.
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Business Intelligence (BI) is defined by literature and scholars in similar ways. Noble (2006) defines BI as the ability to provide the business an information advantage; business doing what it has always done, but more efficient. Singer (2001) described BI as the value proposition that helps organizations tap into decision-making information that regular reporting does not provide. Singer outlined that BI requires tools, applications, and technologies focused on enhanced decision-making and is commonly used in supply chain, sales, finance, and marketing. Negash and Gray (2008) outlined BI more comprehensively. BI is a data driven process that combines data storage and gathering with knowledge management to provide input into the business decision making process. BI enables organizations to enhance the decision making process and requires processes, skills, technology, and data.

Being able to deliver BI in a manner that enables business collaboration, data to become information, and ease of use of information are the challenges. Delivery of BI is accomplished via a methodology. Creswell (2003) outlined that a methodology is set of processes, methods, and rules applied within a discipline. Successful BI methodology should focus on the information value chain and less on the development of software as is the focus of traditional information technology (IT) development. Research has demonstrated that waterfall lifecycles and traditional software development practices are not successful in BI. Software and hardware do not provide organizations value pertaining to BI; it is the use of information (Larson, 2009).

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