Computationally Supported Quantitative Risk Management for Information Systems

Many organizations are struggling with a vast amount of data in order to gain valuable insights and get support in their decision-making process. Decision-making quality depends increasingly on information and the systems that deliver this information. These services are vulnerable and risky from security aspects, and they have to satisfy several requirements, like transparency, availability, accessibility, convenience, and compliance. IT environments are more and more complex and fragmented, which means additional security risks. Business intelligence solutions provide assistance in these complex business situations. Their main goal is to assist organizations to make better decisions. Better decisions means that these solutions support the management of risks, and they have a key role in raising revenue and in reducing cost. The objectives of this chapter are to give an overview of the business intelligence field and its future trends, to demonstrate the most important business intelligence solutions, meanwhile highlighting their risks, business continuity challenges, and IT audit issues. In spite of the fact that this chapter focuses on the business intelligence solutions and their specialities, risk management and the related IT audit approach can be applied for other categories of information systems. IT audit guidelines, best practices, and standards are presented as well, because they give effective tools in controlling process of business intelligence systems.

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Risk and Security of Information Systems in the Portuguese Financial Sector

International Journal of Risk and Contingency Management ◽

10.4018/ijrcm.2019100102 ◽

2019 ◽

Vol 8 (4) ◽

pp. 18-38

Author(s):

Pedro Fernandes da Anunciação ◽

Alexandre Miguel Barão Rodrigues

Keyword(s):

Risk Management ◽

Information Systems ◽

Safety Management ◽

Central Banks ◽

Safety Standards ◽

Degree Of Protection ◽

Financial Activity ◽

Proposed Model ◽

Risk Management Process ◽

Definition Of

This work follows the need of safety standards, update ISO27002:2013, in the major central banks of several European countries. This need has been studied by establishing a focus group that integrated European experts from major central banks. The analysis carried out was supported in the current methodology of information risk management, used by central banks in the safety management of information systems. This methodology is used to analyze and evaluate the adequacy of practices to risk management in the financial activity. The main objective was to present a proposal, sufficiently comprehensive and consistent, to a new risk management process of Information Systems within the European System of Central Banks. And a definition of a practical guide to risk management throughout the different stages of the Information Systems Life Cycle. The proposed model provides a higher degree of protection systems, technologies and information, especially in Central Banks, taking as reference the Portuguese Central Bank.

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Big-Bang ERP Implementation at a Global Company

Cases on Information Technology Series - Cases on Information Technology Planning, Design and Implementation ◽

10.4018/978-1-59904-408-8.ch002 ◽

2006 ◽

pp. 27-40

Author(s):

Nava Pliskin ◽

Marta Zarotski

Keyword(s):

Risk Management ◽

Information Systems ◽

Big Bang ◽

Steering Committee ◽

Dead Sea ◽

Process Reengineering ◽

Erp Implementation ◽

Project Risk ◽

Set Up ◽

Global Company

Dead Sea Works is an international multi-firm producer of Potash and other chemicals whose sales for 1998 were about $500 million. In 1996, the Information Systems group convinced top management to pursue a big-bang ERP implementation of SAP R/3. To reduce project risk, risk management was practiced. First, only modules that matched the functionality of the then-existing systems were targeted, avoiding as much as possible software modifications and process reengineering. Second, a steering committee was set up to handle conflict resolution and set priorities throughout the project and top users were given responsibility with implementing modules within the irrespective functions. R/3 went into production on July 1, 1998, six months ahead of schedule and without exceeding the $4.95 million budget.

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