Big, Open and Linked Data

This book examines the recent evolution of the concept of data as an economic and managerial phenomenon. The author first describes and discusses open data and then introduces the concept of linked data, with a focus on assets for reuse. Furthermore, he addresses the main challenges of big data. Value is identified as the main incentive for the adoption of linked data; accordingly, the next two chapters study sources of data value from a macroeconomic and micro economic perspective, respectively. This contributes to the systematization of important issues at the crossroads of enterprise data and data sharing: data ownership, personal data, and data privacy. In turn, the book reveals the role of innovation as a main vehicle for creating value by unifying big, open, and linked data. It studies the ways in which value can be created, transferred, and captured in the form of business models, before the closing chapter verifies the data unification model by combining open and linked geographical data with big data from a major telecom company.

Offers a detailed analysis of the impact of open data from a macro and micro perspective Reviews business models relevant for new enterprises working mainly with digital assets Presents a method for geographical profiling from a major telecom company

Auteur

Krzysztof Wecel is an Associate Professor at the Poznan University of Economics and Business (Poland). In May 2020, he obtained a habilitation degree from the University of Potsdam (Germany) at the Faculty of Economics and Social Sciences. His main research interest is the intersection of semantic web technologies, open data, and machine learning. He has also a significant experience in big data and business intelligence technologies, ranging from data management through data warehousing to data mining and anomaly detection.

Contenu
Introduction.- Open Data as an Economic, Political, and Technical Phenomenon.- Linked Data for Enrichment of Data Assets.- Big Data Organization Challenge.- Macroeconomic Aspects of Data Value.- Microeconomic Aspects of Data Value.- Business Models for Data.- Geographical Profiling with Linked Data.