Jay Liebowitz – författare

Successes and Failures of Knowledge Management highlights examples from across multiple industries, demonstrating where the practice has been implemented well-and not so well-so others can learn from these cases during their knowledge management journey.

Knowledge management deals with how best to leverage knowledge both internally and externally in organizations to improve decision-making and facilitate knowledge capture and sharing. It is a critical part of an organization's fabric, and can be used to increase innovation, improve organizational internal and external effectiveness, build the institutional memory, and enhance organizational agility.

Starting by establishing KM processes, measures, and metrics, the book highlights ways to be successful in knowledge management institutionalization through learning from sample mistakes and successes. Whether an organization is already implementing KM or has been reluctant to do so, the ideas presented will stimulate the application of knowledge management as part of a human capital strategy in any organization.

In this age of Big Data and analytics, knowledge gained through experiential learning and intuition may be taking a back seat to analytics. However, the use of intuition should not be underestimated and should play an important role in the decision process.

How Well Do Executives Trust Their Intuition covers the Fulbright research study conducted by this international team of editors. The main question of their investigation is: How well do executives trust their intuition? In other words, do they typically prefer intuition over analysis and analytics. And equally importantly, what types of intuition may be most favorable looking at different variables? The research utilizes survey and biometrics approaches with C-level executives from Canada, U.S., Poland, and Italy.

In addition, the book contains chapters from leading executives in industry, academia, and government. Their insights provide examples of how their intuition enabled key decisions that they made.

This book covers such topics as:

The book explores how executives can use intuition to guide decision making. It also explains how to trust intuition-based decisions. How Well Do Executives Trust Their Intuition is a timely and prescient reminder in this age of data-driven analytics that human insight, instinct, and intuition should also play key roles.

In this age of Big Data and analytics, knowledge gained through experiential learning and intuition may be taking a back seat to analytics. However, the use of intuition should not be underestimated and should play an important role in the decision process.

How Well Do Executives Trust Their Intuition covers the Fulbright research study conducted by this international team of editors. The main question of their investigation is: How well do executives trust their intuition? In other words, do they typically prefer intuition over analysis and analytics. And equally importantly, what types of intuition may be most favorable looking at different variables? The research utilizes survey and biometrics approaches with C-level executives from Canada, U.S., Poland, and Italy.

In addition, the book contains chapters from leading executives in industry, academia, and government. Their insights provide examples of how their intuition enabled key decisions that they made.

This book covers such topics as:

The book explores how executives can use intuition to guide decision making. It also explains how to trust intuition-based decisions. How Well Do Executives Trust Their Intuition is a timely and prescient reminder in this age of data-driven analytics that human insight, instinct, and intuition should also play key roles.

This book examines how to develop the main traits that are necessary to become an “informed intuitant”. Case studies and examples of successful “informed intuitants” are a major component of the book. “Intuitant” is someone who has the intuitive awareness to be successful. “Informed intuitant” indicates that the individual/decision maker not only applies his/her intuition but also verifies it through using data-driven approaches (such as data analytics). Some of this work resulted from research examining how well do executives trust their intuition.

This book examines how to develop the main traits that are necessary to become an “informed intuitant”. Case studies and examples of successful “informed intuitants” are a major component of the book. “Intuitant” is someone who has the intuitive awareness to be successful. “Informed intuitant” indicates that the individual/decision maker not only applies his/her intuition but also verifies it through using data-driven approaches (such as data analytics). Some of this work resulted from research examining how well do executives trust their intuition.

Analytics and artificial intelligence (AI), what are they good for? The bandwagon keeps answering, absolutely everything! Analytics and artificial intelligence have captured the attention of everyone from top executives to the person in the street. While these disciplines have a relatively long history, within the last ten or so years they have exploded into corporate business and public consciousness. Organizations have rushed to embrace data-driven decision making. Companies everywhere are turning out products boasting that "artificial intelligence is included." We are indeed living in exciting times. The question we need to ask is, do we really know how to get business value from these exciting tools?

Unfortunately, both the analytics and AI communities have not done a great job in collaborating and communicating with each other to build the necessary synergies. This book bridges the gap between these two critical fields. The book begins by explaining the commonalities and differences in the fields of data science, artificial intelligence, and autonomy by giving a historical perspective for each of these fields, followed by exploration of common technologies and current trends in each field. The book also readers introduces to applications of deep learning in industry with an overview of deep learning and its key architectures, as well as a survey and discussion of the main applications of deep learning. The book also presents case studies to illustrate applications of AI and analytics. These include a case study from the healthcare industry and an investigation of a digital transformation enabled by AI and analytics transforming a product-oriented company into one delivering solutions and services. The book concludes with a proposed AI-informed data analytics life cycle to be applied to unstructured data.