Data development innovation entrepreneurship management 1016

Big Data, Open Data and Data Development  Smart Innovation Set coordinated by Dimitri Uzunidis Volume Big Data, Open Data and Data Development Jean-Louis Monino Soraya Sedkaoui First published 2016 in Great Britain and the United States by ISTE Ltd and John Wiley & Sons, Inc Apart from any fair dealing for the purposes of research or private study, or criticism or review, as permitted under the Copyright, Designs and Patents Act 1988, this publication may only be reproduced, stored or transmitted, in any form or by any means, with the prior permission in writing of the publishers, or in the case of reprographic reproduction in accordance with the terms and licenses issued by the CLA Enquiries concerning reproduction outside these terms should be sent to the publishers at the undermentioned address: ISTE Ltd 27-37 St George’s Road London SW19 4EU UK John Wiley & Sons, Inc 111 River Street Hoboken, NJ 07030 USA www.iste.co.uk www.wiley.com © ISTE Ltd 2016 The rights of Jean-Louis Monino and Soraya Sedkaoui to be identified as the authors of this work have been asserted by them in accordance with the Copyright, Designs and Patents Act 1988 Library of Congress Control Number: 2016931678 British Library Cataloguing-in-Publication Data A CIP record for this book is available from the British Library ISBN 978-1-84821-880-2 Contents Acknowledgements vii Foreword ix Key Concepts xi Introduction xix Chapter The Big Data Revolution 1.1 Understanding the Big Data universe 1.2 What changes have occurred in data analysis? 1.3 From Big Data to Smart Data: making data warehouses intelligent 1.4 High-quality information extraction and the emergence of a new profession: data scientists 1.5 Conclusion 12 16 21 Chapter Open Data: A New Challenge 23 2.1 Why Open Data? 2.2 A universe of open and reusable data 2.3 Open Data and the Big Data universe 2.4 Data development and reuse 2.5 Conclusion 23 28 33 38 41 vi Big Data, Open Data and Data Development Chapter Data Development Mechanisms 3.1 How we develop data? 3.2 Data governance: a key factor for data valorization 3.3 CI: protection and valuation of digital assets 3.4 Techniques of data analysis: data mining/text mining 3.5 Conclusion 43 44 54 60 65 72 Chapter Creating Value from Data Processing 73 4.1 Transforming the mass of data into innovation opportunities 4.2 Creation of value and analysis of open databases 4.3 Value creation of business assets in web data 4.4 Transformation of data into information or “DataViz” 4.5 Conclusion 74 82 87 94 100 Conclusion 101 Bibliography 109 Index 121 Acknowledgements This book is the product of several years of research devoted to data processing, statistics and econometrics in the TRIS (traitement et recherche de l’information et de la statistique) laboratory It is the fruit of several projects carried out within the framework of research and development for several startups within the Languedoc-Roussillon region and large private and public groups I would like to thank all of the members of the RRI (réseau de recherche sur l’innovation), and more particularly, Dimitri Uzunidis, its president, for his attentive and careful reading of the first version, and who encouraged us to publish this book Thanks also to M Bernard Marques, who had the difficult task of proofreading the manuscript and who had many important notes to help with the understanding of the book I would also like to thank my teacher and friend Jean Matouk, who was the cause of this publication, thank you for his encouragement and unfailing support over the years Many thanks to all the researchers at the laboratory for their help and support and most especially to Soraya Sedkaoui; without her this book would never have seen the light of day Thanks to all those who have supported me through difficult times and who have transformed an individual intellectual adventure into a viii Big Data, Open Data and Data Development collective one, in particular Alain Iozzino, director of the startup E-prospects, with whom we have carried out many research and development projects over the years Finally, I must express my special gratitude to those dear to me, my family, and most of all to my wife, who has had to put up with my moods over the last few years Jean-Louis MONINO This book was a work of adaptation, updating and rewriting in order to adapt all of the work of the TRIS laboratory Its creation was fed by exchanges and discussions with my teacher Jean-Louis Monino, without whom this book would never have seen the light of day I am infinitely grateful to him for having included me in this adventure It would not have been possible to produce this book without my family who have always encouraged and supported me throughout all my ideas and projects, no matter how far away they have sometimes been Special mention must go to my mother, there are no words to express how important she is and how much she has done in making me what I am today Finally, I would like to thank Hans-Werner Gottinger, Mohamed Kasmi and Mustapha Ghachem for their unfailing support and for the interest that they have always shown in what I am doing Soraya SEDKAOUI Bibliography 113 [FAY 96] FAYYAD U.M., PIATETSKY-SHAPIRO G., SMYTH P et al., Advances in Knowledge Discovery & Data Mining, MIT Press, Cambridge, MA, 1996 [FIO 10] FIORETTI M., SANT’ANNA S., Open Data Open Society, Dynamics of Institutions and Markets in Europe, available at: http://www.lem.sssup.it/WPLem/odos/odos.html, 2010 [FLI 08] FLICI A., A systematic evaluation of business intelligence tools: an investigation into potential tangible/intangible value, PhD Thesis, Brunel University, 2008 [FOR 00] FORAY D., L’économie de la connaissance, La Découverte, Paris, 2000 [FRA 97] FRANCO J.-M., Le Data Warehouse et le Data Mining, Eyrolles, Paris, 1997 [GAR 99] GARDARIN G., Internet/intranet et bases de données, Eyrolles, Paris, 1999 [GAR 14] GARTNER, Big data needs a data-centric security focus, available at: www.gartner.com/document/2691917, 2014 [GAU 12] GAULTER D., Le Big data: un cadean empoisonné pour les data miners? 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Index A, B, C D, E, F accessibility, 27, 83 architecture of the semantic web, 89 assist decisionmaking, 66 association, 48, 69 AUNEGE, 59 automatic classification, 48, 71 autonomy, 27 big challenge, 14 Big Data 4v, 54 7v, 54 Bootstrap, 52 business intelligence, 8, 70 Canal-U, 59 cloud, 2, 11, 21, 46, 65, 81, 98 computing, 11, 46, 65 collaboration, 29, 40–42, 93 collective intelligence, 35, 51, 57, 61, 64 competitiveness, 53, 62–64, 73, 74 data analysis, 8–12, 14, 21, 33, 43, 56, 65, 70, 75, 83, 95, 96 analyst, 17, 19 gouv.fr, 24, 84, 87 governance, 54–60, 72 journalism, 30 management, 10, 15, 57, 72, 74, 95 marketplace, 45 mining, 8, 16, 43, 61, 65–71, 75 public, 24, 27, 29, 34–37, 39, 83, 87 quality, 14, 15, 21, 27, 57, 67, 96 reuse, 27, 29, 35, 38 science, 17–19, 46, 60 science, 17–19, 46, 60 scientist, 2, 15–21, 51, 67, 87, 94, 95 scientists, 2, 16–21, 67, 94, 95 statistica, 70 visualization, 74, 95–97, 100 Big Data, Open Data and Data Development, First Edition Jean-Louis Monino and Soraya Sedkaoui © ISTE Ltd 2016 Published by ISTE Ltd and John Wiley & Sons, Inc 122 Big Data, Open Data and Data Development warehouse, 10, 23, 66 web, 74, 90 dataset, 8, 10, 39, 40, 51, 67, 86 DataViz, 94–100 decisions trees, 48 defensive, 58 description, 68, 89 descriptive techniques, 48 development of information and communication technologies (ICTS), 82 discriminate analysis, 48 discrimination, 48 e-prospects, 70 economy, 23, 29, 30, 41, 58, 60, 74 ecosystem, 7, 33, 34, 39, 69, 80, 87 enterprise, 81 estimation, 69 etalab, 36 evaluation of a model, 67 exchange office, 74 exploitation of data, 32 freedom Act, 73 FUN, 59 G, H, I geolocalization data, 62 Google Car, 79 government data, 41 hadoop, 19, 20 handimap, 39 harvesting phasec, 86 heterogeneity, 69, 81 hurst, 52 hyperlink, 89 identifying sets, 90 improvement, 16, 19, 24, 40 input, 75 internet of things, 45 interoperability, 27, 53, 89 K, L, M knowledge pyramid, 75 linear regression, 49 linked data, 31, 88, 91 lobbying, 58 LOD, 90 log analysis, 56 logistic regression, 48 machine learning, 4, 16, 81 managerial approaches, 82 MapReduce, 19 Martre, 64 mash-ups, 40 mass data, 77 mediation, 64 metadata, 89, 92 model general linear, 49 N, O, P Neuronal method, 50 new economic value, 10 offensive, 58 open innovation, 81 knowledge foundation network, 84 page rank, 92 piloting activity, 56 PORTICES, 59 power of data, 56 powerset, 92 predictive techniques, 48 preparation of data, 67, 96 Index product lifecycle analysis, 14 productivity, 2, 11, 14, 41, 45, 53, 75 public actors, 29 Q, R, S quantified self, 87 raw data, 27, 34, 37–39, 53, 87 RDF, 89, 90, 92 reporting, 10, 72 scientific research, 24, 29 segmentation, 48, 69 semantic approach, 71 web, 69, 88–93 smart city, 80 Smart Data, 12–16, 54 social innovation, 39 123 stage deployment, 96 modeling, 96 startups, 33, 36, 87 statistical approach, 71 stock, 1, 31, 43 T, U, V, W Text mining, 65–71 Tim Berners-Lee, 27, 42, 87–90, 93 top management, 2, 19, 94 TRIS laboratory, 49 trust rank, 92 unstructured data, 10, 14, 21, 35, 56, 69, 73, 77 variability, W3C, 88, 90 Web 3.0, 87 Other titles from in Innovation, Entrepreneurship and Management 2015 CASADELLA Vanessa, LIU Zeting, DIMITRI Uzunidis Innovation Capabilities and Economic Development in Open Economies (Smart Innovation Set – Volume 1) CORSI Patrick, NEAU Erwan Innovation Capability Maturity Model CORSI Patrick, MORIN Dominique Sequencing Apple’s DNA FAIVRE-TAVIGNOT Bénédicte Social Business and Base of the Pyramid GODÉ Cécile Team Coordination in Extreme Environments MAILLARD Pierre Competitive Quality and Innovation MASSOTTE Pierre, CORSI Patrick Sustainability Calling MASSOTTE Pierre, CORSI Patrick Operationalizing Sustainability 2014 DUBÉ Jean, LEGROS Diègo Spatial Econometrics Using Microdata LESCA Humbert, LESCA Nicolas Strategic Decisions and Weak Signals 2013 HABART-CORLOSQUET Marine, JANSSEN Jacques, MANCA Raimondo VaR Methodology for Non-Gaussian Finance 2012 DAL PONT 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and Data Development  Smart Innovation Set coordinated by Dimitri Uzunidis Volume Big Data, Open Data and Data Development Jean-Louis Monino Soraya... 28 33 38 41 vi Big Data, Open Data and Data Development Chapter Data Development Mechanisms 3.1 How we develop data? 3.2 Data governance: a key factor for data valorization ... Chapter The Big Data Revolution 1.1 Understanding the Big Data universe 1.2 What changes have occurred in data analysis? 1.3 From Big Data to Smart Data: making data warehouses