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The 2022 World Economic Forum surveyed 1,000 experts and leaders
who indicated their risk perception that the earth’s conditions
for humans are a main concern in the next 10 years. This means
environmental risks are a priority to study in a formal way. At the
same time, innovation risks are present in theminds of leaders,
newknowledge brings new risk, and the adaptation and adoption of
risk knowledge is required to better understand the causes and
effects can have on technological risks. These opportunities
require not only adopting new ways of managing and controlling
emerging processes for society and business, but also adapting
organizations to changes and managing new risks. Risk Analytics:
Data-Driven Decisions Under Uncertainty introduces a way to analyze
and design a risk analytics system (RAS) that integrates multiple
approaches to risk analytics to deal with diverse types of data and
problems. A risk analytics system is a hybrid system where human
and artificial intelligence interact with a data gathering and
selection process that uses multiple sources to the delivery of
guidelines to make decisions that include humans and machines. The
RAS system is an integration of components, such as data
architecture with diverse data, and a risk analytics process and
modeling process to obtain knowledge and then determine actions
through the new knowledge that was obtained. The use of data
analytics is not only connected to risk modeling and its
implementation, but also to the development of the actionable
knowledge that can be represented by text in documents to define
and share explicit knowledge and guidelines in the organization for
strategy implementation. This book moves from a review of data to
the concepts of a RAS. It reviews RAS system components required to
support the creation of competitive advantage in organizations
through risk analytics. Written for executives, analytics
professionals, risk management professionals, strategy
professionals, and postgraduate students, this book shows a way to
implement the analytics process to develop a risk management
practice that creates an adaptive competitive advantage under
uncertainty.
The 2022 World Economic Forum surveyed 1,000 experts and leaders
who indicated their risk perception that the earth’s conditions
for humans are a main concern in the next 10 years. This means
environmental risks are a priority to study in a formal way. At the
same time, innovation risks are present in theminds of leaders,
newknowledge brings new risk, and the adaptation and adoption of
risk knowledge is required to better understand the causes and
effects can have on technological risks. These opportunities
require not only adopting new ways of managing and controlling
emerging processes for society and business, but also adapting
organizations to changes and managing new risks. Risk Analytics:
Data-Driven Decisions Under Uncertainty introduces a way to analyze
and design a risk analytics system (RAS) that integrates multiple
approaches to risk analytics to deal with diverse types of data and
problems. A risk analytics system is a hybrid system where human
and artificial intelligence interact with a data gathering and
selection process that uses multiple sources to the delivery of
guidelines to make decisions that include humans and machines. The
RAS system is an integration of components, such as data
architecture with diverse data, and a risk analytics process and
modeling process to obtain knowledge and then determine actions
through the new knowledge that was obtained. The use of data
analytics is not only connected to risk modeling and its
implementation, but also to the development of the actionable
knowledge that can be represented by text in documents to define
and share explicit knowledge and guidelines in the organization for
strategy implementation. This book moves from a review of data to
the concepts of a RAS. It reviews RAS system components required to
support the creation of competitive advantage in organizations
through risk analytics. Written for executives, analytics
professionals, risk management professionals, strategy
professionals, and postgraduate students, this book shows a way to
implement the analytics process to develop a risk management
practice that creates an adaptive competitive advantage under
uncertainty.
This book focuses on understanding the analytics knowledge
management process and its comprehensive application to various
socioeconomic sectors. Using cases from Latin America and other
emerging economies, it examines analytics knowledge applications
where a solution has been achieved. Written for business students
and professionals as well as researchers, the book is filled with
practical insight into applying concepts and implementing processes
and solutions. The eleven case studies presented in the book
incorporate the whole analytics process and are useful reference
examples for applying the analytics process for SME organizations
in both developing and developed economies. The cases also identify
multiple tacit factors to deal with during the implementation of
analytics knowledge management processes. These factors, which
include data cleaning, data gathering, and interpretation of
results, are not always easily identified by analytics
practitioners. This book promotes the understanding of analytics
methods and techniques. It guides readers through numerous
techniques and methods available to analytics practitioners by
explaining the strengths and weaknesses of these methods and
techniques.
This book is about the process of using analytics and the
capabilities of analytics in today's organizations. Cutting through
the buzz surrounding the term analytics and the overloaded
expectations about using analytics, the book demystifies analytics
with an in-depth examination of concepts grounded in operations
research and management science. Analytics as a set of tools and
processes is only as effective as: The data with which it is
working The human judgment applying the processes and understanding
the output of these processes. For this reason, the book focuses on
the analytics process. What is intrinsic to analytics' real
organizational impact are the careful application of tools and the
thoughtful application of their outcomes. This work emphasizes
analytics as part of a process that supports decision-making within
organizations. It wants to debunk overblown expectations that
somehow analytics outputs or analytics as applied to other
concepts, such as Big Data, are the be-all and end-all of the
analytics process. They are, instead, only a step within a holistic
and critical approach to management thinking that can create real
value for an organization. To develop this holistic approach, the
book is divided into two sections that examine concepts and
applications. The first section makes the case for executive
management taking a holistic approach to analytics. It draws on
rich research in operations and management science that form the
context in which analytics tools are to be applied. There is a
strong emphasis on knowledge management concepts and techniques, as
well as risk management concepts and techniques. The second section
focuses on both the use of the analytics process and organizational
issues that are required to make the analytics process relevant and
impactful.
This book focuses on understanding the analytics knowledge
management process and its comprehensive application to various
socioeconomic sectors. Using cases from Latin America and other
emerging economies, it examines analytics knowledge applications
where a solution has been achieved. Written for business students
and professionals as well as researchers, the book is filled with
practical insight into applying concepts and implementing processes
and solutions. The eleven case studies presented in the book
incorporate the whole analytics process and are useful reference
examples for applying the analytics process for SME organizations
in both developing and developed economies. The cases also identify
multiple tacit factors to deal with during the implementation of
analytics knowledge management processes. These factors, which
include data cleaning, data gathering, and interpretation of
results, are not always easily identified by analytics
practitioners. This book promotes the understanding of analytics
methods and techniques. It guides readers through numerous
techniques and methods available to analytics practitioners by
explaining the strengths and weaknesses of these methods and
techniques.
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 volume is connecting data analytics and artificial
intelligence, 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.This book reviews the steps that
organizations have taken in using technology that connects the data
analytics modeling process and digital operations, such as the
shift from the use of statistical learning and machine learning for
data analytics to the improvement and use of new technologies. The
digitalization process is a potential opportunity for organizations
however the results are not necessarily good for everyone. Hence,
organizations implement strategic risk control in cloud computing,
blockchain, artificial intelligence and create digital networks
that are connected internally and externally to deal with internal
and external customers, with suppliers and buyers, and with
competitors and substitutes. The new risks appear once new
knowledge emerges and is in use, but at the same time the new
knowledge supports the initiatives to deal with risks arising from
novel ways of competing and collaborating.
Mexico moved from an almost closed-to-trade economy to a very open
economy and one of the least guided by public sector forces.
However, the presence of wide disparities in social development and
economic growth across the Mexican states may be an obstacle to the
further integration of the national economy to the global economy.
In this context, the main focus of Mexican public policies has been
growth at the national level, while regional policies are merely
national policies with territorial implications. This book studies
factors that may affect the regional pattern of growth and
contribute to the debate around the need for regional polices in
Mexico. The aim of this book is to examine for Mexico the regional
distribution of, and effects on regional growth of, three of the
main factors that have been highlighted in the standard economic
literature on growth: public investment, human capital, and science
and technology, and how they can be fit into a regional policy to
foster development.
La Soledad y el Silencio "Un d a cre que desfallec a el resplandor
de la vida y que no hab a m s que sombras a mi alrededor.
Entristecido, mir hacia el oc ano inmenso de la vida tratando de
hallar la causa y vi entonces, que la vida era amable y que el
error se hallaba simplemente en m " Georges Bernanos Que cada amor
sea el ltimo --------------------- Dr. Eduardo Rodriguez Caldera M
dico Cirujano especialista en medicina interna, endocrinolog a,
metabolismo, diabetes y nutrici n. Postgrado en la Universidad de
Gante en B lgica.
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