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Empirical research comprised a mixed methods approach using (1) a Delphi study with practitioners through various forums and (2) interviews with business analytics managers in three case organizations. This paper investigates the challenges faced by organizational managers seeking to become more data and information-driven in order to create value. However, while the literature acknowledges the importance of these topics little work has addressed them from the organization's point of view.

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The popularity of big data and business analytics has increased tremendously in the last decade and a key challenge for organizations is in understanding how to leverage them to create business value. This paper addresses how Agile principles and practices have evolved with business intelligence, as well as its challenges and future directions. New trends such as fast analytics and data science have emerged as part of business intelligence. The Big Data phenomenon, the volume, variety, and velocity of data, has impacted business intelligence and the use of information. The practice of business intelligence delivery with an Agile methodology has matured however, business intelligence has evolved altering the use of Agile principles and practices. Business intelligence has evolved because the amount of data generated through the internet and smart devices has grown exponentially altering how organizations and individuals use information. This article explores the application of Agile methodologies and principles to business intelligence delivery and how Agile has changed with the evolution of business intelligence. Since this time, practitioners have applied Agile methodologies to many delivery disciplines. The value of this framework is that it forces us to ask questions about pre-existing normative and procedural responses in a way that reveals future problems, instead of bounded discussions of the application to and adaptability of existing systems which does not.Īgile methodologies were introduced in 2001. We elaborate this theoretical framework by analysing problems posed by generative 'deep-fake' technology, in the context of the criminal justice system in England and Wales. A unifying framework will facilitate a holistic appreciation of why AI systems might destabilise criminal justice systems and suggest appropriate responses depending on the type of criminal legal disruption at its root.

As platform technologies that support a myriad of potential applications, the impact of AI systems upon criminal justice will be amplificatory, divergent, and simultaneous. This paper attempts to provide a unifying conceptual framework to interpret the perils and promises of artificial intelligence (AI) applications in criminal justice systems. The results corroborate the importance of these factors by explaining a substantial percentage of the variance in decision modelling effectiveness and support several insights on a few success factors corporate managers should focus on. Using stepwise multivariate regression analysis, this convenience sample with a response rate of 32% was used to test hypotheses regarding the importance of some factors proposed in the literature as likely to improve the success of BDA applications. This study used a two-respondent survey of decision makers using BDA applications fully operational for between 1-3 years and the corresponding CDOs in 282 organizations in various industry sectors.

Some authors have proposed that BDA projects differ from IT projects by requiring extra analytical capabilities, special software, and more specialized application developers to enhance decision modelling effectiveness. To be successful, BDA applications must be capable of producing actionable information useful to support managerial decisions throughout the organization.