Research & Books for Everyone

The world Economic Forum in the 2022 report of a survey to 1000 experts and leaders indicated in their risk perception that the earth conditions for humans is a main concern in the next 10 years. This means environmental risks are a priority to study in a formal way, using the adequate methods and techniques, obtaining data and creating multidisciplinary groups doing data analytics applied to risk management. At the same time innovation risks are present in the mind of leaders, new knowledge brings new risks and the adaptation and adoption of risk knowledge is required to understand more what causes and effects can have the technological risks. Innovation is a means to progress and to create value in a society. Artificial intelligence and digital evolution in the society are opening wonderful opportunities for new generations. These opportunities in most of the cases require not only to adopt new ways to manage and control emerging processes for the society and businesses, but also to adapt organizations to changes and manage new risks. Risk Analytics is about the capacity to use data analytics/science before, during and after the risk events. In a current situation Covid 19 modified several aspects of the socio-economic world. The Ukraine crisis generate modifications in business and families. The Supply-Chain risk (See section 4.8 for exposure understanding of supply chain risk illustration) is converted into one of the fundamental risk to understand and to find the way to assess, mitigate and control.

In this book the study of strategic risk is not only for its control and mitigation using analytics and digital transformation in organizations, but also it is about the strategic risks that digital transformation can bring to organizations. Strategic risk control is one of the goals in creating intelligent organizations and at the same time it is part of the appetite for creating smarter organizations to support organizations’ development. Knowledge that is created by data analytics and the capacity to operationalize that knowledge through digital transformation can produce potential sustainable competitive advantages. The core of the book is connecting data analytics and AI, risk management and digitalization to create strategic intelligence as the capacity of adaptation that organizations need to compete and to succeed. Strategic Intelligence is a symbiotic work of artificial intelligence, business intelligence and competitive intelligence. Strategic risk is represented by the probability of having variations in the performance results of the organizations that can limit their capacity to maintain sustainable competitive advantages. There is an emphasis in the book about the conversion of models that support data analytics into actions to mitigate strategic risk based on digital transformation.

Analytics is a concept that has been used in management in the last years but in reality the term encloses concepts and activities that have been developed for more than 50 years in support of management. There are multiple definitions of analytics including factors that are key in analytics process. Davenport and Harris (2007) defines analytics as “The extensive use of data, statistical and quantitative analysis, exploratory and predictive models, and fact-based management to drive decisions and actions.” And Davenport (2010) continue saying “The goal for analytics is to make better decisions. However, while many organizations collect data and some organizations engage in analytics, few organizations link their analytics activities to their decision making.” Furthermore, some authors concentrate analytics on the concept of rationality as it is Saxena  and Srinivasan (2013) who pointed out “In our view, analytics is the rational way to get from ideas to execution.”

Nevertheless, analytics is related to the concept of creating intelligence in organizations. The word intelligence is part of the Artificial Intelligence development in the 1950s, the same as in the 1990s is  Business intelligence. Business analytics and Big Data Analytics have been introduced in the 2000s (Chen et al. 2012). The idea of these concepts has been to describe the emerging need to deal with data in many different formats, with high volume and that is created very fast in order to develop intelligence in organizations

In general analytics is evolving from being isolated and problem specific tasks to a discipline fully integrated to the strategy creation and strategy implementation support. This can be possible if people, technology, processes are aligned to strategy and the learning of working interdisciplinary and across the organizations grows in order to contribute to the strategy design and its implementation in a better way. The value of analytics is not only in the methods or capabilities but also it is in the development of a solid culture to solve problems and make decisions using what organizations have in term of minds, data, and tools (models, applications and so on).

In general data analytics process are not only for big companies or complex problems to solve, or concentrated on geographical regions or economic development. Data analytics is an opportunity to develop economies creating and using knowledge to add value to manufacturing, services, governments and any sector in a society. Data analytics is a process for any organization in any society, the adoption of the analytics process is a matter of changing mindsets to consider data as an asset that will create value in organizations, that is affordable and it is possible because of a good combination of people’s minds, data and machines. Value that is based on what people in organizations are doing to be smart, to improve strategic intelligence and to evolve sustainable competitive advantages development.