Strategic Risk, Intelligence and Digital Transformation

The following sections are included:

• Dedication
• About the Author
• Contents
• Introduction

This chapter is a theoretical presentation of risk concepts and it introduces the concept of risk, utility theory, risk aversion, and risk indicators construction. The general framework of risk management (RM) used in the book is aimed at managing the variability of the possible results of the company, especially in terms of developing data analytics, AI, and digitalization capabilities to mitigate and control risk. This chapter presents the concept of risk under a holistic view of the organization where it is possible to identify what strategic risk is and how to coordinate efforts to mitigate those identified strategic risks. The risk management processes are the roads to follow to obtain benefits of the risk efforts. In the coordination of efforts, utility theory helps to define where to find value. For example, this chapter presents how the coverage with insurance can be optimized, but at same time explains that there are more aspects to consider for protection– mitigation of the organization’s risks. Risk management process development needs analytics tools, principles, and methods/techniques to deal with risk, to organize, and to align risk strategies with the business strategy. Strategic risk is presented in a joint explanation of what the organization needs to evaluate in its performance and how risk exists and is embedded in all organizations’ activities. The end of the chapter presents examples on RM experiences and business process alignment.

This chapter is about the integration of concepts and paradigms in organizations that lead to use of strategic intelligence development and digital support to mitigate strategic risks. The chapter covers basic concepts related to the structure of strategic intelligence based on ideas and tools that organizations use in strengthening data analytics and artificial intelligence capabilities and capacity. Organizations need to clarify the understanding of the meaning of strategic intelligence and data analytics, given the misinterpretation of having technology as a goal and not as means to achieve what organizations are looking for. A good example is the use of big data and the confusion of big data with the concept of big data analytics. Big data are another kind of data that require management under certain conditions; big data are a type of data to use in information system analysis and design. This means big data form an information systems problem to solve; this could be from the point of view of storage, access, or related to what is possible for transforming or using data in applications. Meanwhile, Big Data Analytics (BDA) is about the application of data analytics techniques and methods to obtain value from big data, to create actionable knowledge; that means if big data exist without analytics, big data are probably not going to give the organization the value that is expected…

This chapter describes the data analytics process and the steps to obtain digital solutions and potential digital transformation means for organizations. The chapter is divided into four sections: Data Analytics Process from Data to Valuable Actions; Data Analytics Integration to Digital Transformation; Models, Prototypes, and Solutions; and Case Studies of Strategic Risks. The aim of this chapter is to guide the reader to the comparison of the data analytics process and the data mining process, and how they are integrated into the general business strategies and AI and digital transformations. The last section introduces an example to explain required stages, after data are organized, for connecting models, prototypes, and solutions. It is important to keep in mind that agile principles are behind the concepts presented; however, the agile principles are suggested to work on data analytics and AI, but agile development cycle is not the suggested for AI and digital transformation because of the following reasons: Data Analytics and AI processes require continuous feedback in each step of the analytics process where there is an ongoing loop of data, model, and solution; modeling that can be from mathematical development, computing and conceptual sources, inductive (data mining) and deductive (mathematical models) processes; review of the good relationship among data, model, possible implementation, understanding and adoption. In AI and digital transformation, the processes integrate the stages and discovery of inputs and outputs based on the partial outcomes that are created in each step.

In the three previous chapters, the sequence of creating knowledge and intelligence using data analytics has been presented to mitigate risk and deal with strategic risk using digital transformation. The common idea in these chapters has been to identify how a data analytics process moves step by step to achieve intelligence creation that is integrated to digital transformation in organizations. In this chapter, the emphasis is on how to create actions with regard to the tools used and support the adoption of technologies, to successfully compete in the world.

This chapter is fundamentally a review of how humans and machines coexist and create value. The main points to cover are as follows:

• (1) The purpose of digital transformation and AI is to support and create benefit for humans.
• (2) Use and adoption of technologies to mitigate strategic risks possibly creates more risks or increases the severity of existing ones.
• (3) Human and machine interaction can occur only under ethical conditions, respecting and managing diversity, and modifying misuse and threats in society.

This chapter has five sections: the first one presents interactions between humans and machines, the second section is about challenges in evaluating talent and machines, the third one is related to the ethical issues, the fourth section is on how to deal with operational risk in a world of strategic risks, and the last section presents study cases of what happened in organizations when they dealt with strategic risks, increasing intelligence, and digital capabilities.

The following sections are included:

• References
• Index