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The review helped in exploring SDPLC phases in the context of big data applications and performing a gap analysis of the phases that have yet to see detailed research efforts but deserve attention. Results: The search results helped in identifying data rich application projects that have the potential to utilize big data successfully.
#Jumpstart download for windows 8 2016 manual
A manual search covered papers returned by search engines resulting in approximately 2,000 papers being searched and 170 papers selected for review. Method: A literature survey was performed.
#Jumpstart download for windows 8 2016 software
Objective: To look at existing research on how software engineering concepts, namely the phases of the software development project life cycle (SDPLC), can help build better big data application projects. However, developing and maintaining stable and scalable big data applications is still a distant milestone. Researchers and major corporations are looking into big data applications to extract the maximum value from the data available to them.
As a result, this paper presents a business analytics ecosystem for organizations that contributes to the body of scholarly knowledge by identifying key business areas and functions to address to achieve this transformation.Ĭontext: Big data has become the new buzzword in the information and communication technology industry. Further, becoming data-driven is not merely a technical issue and demands that organizations firstly organize their business analytics departments to comprise business analysts, data scientists, and IT personnel, and secondly align that business analytics capability with their business strategy in order to tackle the analytics challenge in a systemic and joined-up way. The case studies reinforced the Delphi findings and highlighted several challenge focal areas: organizations need a clear data and analytics strategy, the right people to effect a data-driven cultural change, and to consider data and information ethics when using data for competitive advantage. 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.
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