A Report and Reflection on an Application of Critical Systems Practice to Improve a Business Intelligence System's Business Requirements

2018 ◽  
Vol 35 (5) ◽  
pp. 548-563
Author(s):  
Carin Venter ◽  
Roelien Goede
2011 ◽  
Vol 2 (3) ◽  
pp. 64-77 ◽  
Author(s):  
Nayem Rahman ◽  
Dale Rutz ◽  
Shameem Akhter

Traditional data warehouse projects follow a waterfall development model in which the project goes through distinct phases such as requirements gathering, design, development, testing, deployment, and stabilization. However, both business requirements and technology are complex in nature and the waterfall model can take six to nine months to fully implement a solution; by then business as well as technology has often changed considerably. The result is disappointed stakeholders and frustrated development teams. Agile development implements projects in an iterative fashion. Also known as the sixty percent solution, the agile approach seeks to deliver more than half of the user requirements in the initial release, with refinements coming in a series of subsequent releases which are scheduled at regular intervals. An agile data warehousing approach greatly increases the likelihood of successful implementation on time and within budget. This article discusses agile development methodologies in data warehousing and business intelligence, implications of the agile methodology, managing changes in data warehouses given frequent change in business intelligence (BI) requirements, and demonstrates the impact of agility on the business.


Author(s):  
Muhammad Mazen Almustafa ◽  
Dania Alkhaldi

In this highly technology - dependent, knowledge- based economy, the causes for failure of most software development projects are related to rapid technology changes, in-flux business requirements, or failure to tackle risk. Accordingly, risk management plays significant and crucial role in organizations’ response to this rapidly changing economy. Risk management process is illustrated in four main steps: identify the risk, analyze the risk, treat the risk and monitor the risk. This chapter discusses and explores the role of business intelligence and agile methodology to manage risk effectively and efficiently. It explores the risk management traditional tools that are commonly used, the role of business intelligence in risk management, and the role of agile methodology in risk management.


2016 ◽  
Author(s):  
◽  
Jeanette Wendy Wing

Motivated by the literature regarding the need for further research on client participation in software development, a systemic framework for the understanding of client requirements in Information System development projects is developed. This systemic framework is particularly relevant for project contexts characterized by diversity of stakeholder values and complexity. To address this complexity, research led to the selection of methods from three systems methodologies and the conclusion for the need to mix them in the process of requirements understanding by clients. The mixing of methods from various methodologies is justified through the principles of Critical Systems Practice, and the process of their use is guided by Action Design Research. In spite of the strong research tradition associated with Soft Systems Methodology and the growing interest in the Work System Method, the level of use of these by practitioners is not high because complex project situations require harnessing of the strengths of more than one methodology. The proposed framework also includes a third system methodology Critical Systems Heuristics. This study demonstrated how the meta-methodology Critical Systems Practice is applied in justifying the selection and the mix of methods from the above three methodologies in the proposed framework. The principles of design science were applied, where the framework is the design artifact that is developed. Action Research was used to guide evaluation of the framework in the pilot study. The framework was applied in a pilot study to the understanding of the management of a Wellness Centre which operates within the Kenneth Gardens Housing Estate, through action research. As a result of the pilot study some modifications were made to the framework and the process of its implementation. The modified framework was applied in a further main study concerning the management of the Kenneth Gardens Housing Estate which has a broader context than the pilot study. The contribution of this research to the field of Information Systems is both theoretical and practical. One theoretical contribution is provision of a framework for clearer understanding of software requirements by clients. The second theoretical contribution is that Action Design Research is enhanced by adding proper justification for the methods included in the framework through the application of Critical Systems Thinking and Critical Systems Practice. The practical contribution is through the demonstration of Action Design Research being applied to a real-world problem in both the pilot and the main study.


Author(s):  
Nayem Rahman ◽  
Dale Rutz ◽  
Shameem Akhter

Traditional data warehouse projects follow a waterfall development model in which the project goes through distinct phases such as requirements gathering, design, development, testing, deployment, and stabilization. However, both business requirements and technology are complex in nature and the waterfall model can take six to nine months to fully implement a solution; by then business as well as technology has often changed considerably. The result is disappointed stakeholders and frustrated development teams. Agile development implements projects in an iterative fashion. Also known as the sixty percent solution, the agile approach seeks to deliver more than half of the user requirements in the initial release, with refinements coming in a series of subsequent releases which are scheduled at regular intervals. An agile data warehousing approach greatly increases the likelihood of successful implementation on time and within budget. This article discusses agile development methodologies in data warehousing and business intelligence, implications of the agile methodology, managing changes in data warehouses given frequent change in business intelligence (BI) requirements, and demonstrates the impact of agility on the business.


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