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- Herausgeber: John Wiley & Sons
- Kategorie: Wissenschaft und neue Technologien
- Sprache: Englisch
Businesses are becoming increasingly aware of the importance of data and information. As such, they are eager to develop ways to manage them, to enrich them and take advantage of them. Indeed, the recent explosion of a phenomenal amount of data, and the need to analyze it, brings to the forefront the well-known hierarchical model: Data, Information, Knowledge . Data this new intangible manna is produced in real time. It arrives in a continuous stream and comes from a multitude of sources that are generally heterogeneous. This accumulation of data of all kinds is generating new activities designed to analyze these huge amounts of information. It is therefore necessary to adapt and try new approaches, methods, new knowledge and new ways of working. This leads to new properties and new issues as a logical reference must be created and implemented. At the company level, this mass of data is difficult to manage; interpreting it is the predominant challenge.
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Table of Contents
Cover
Title Page
Copyright Page
Foreword
Acknowledgements
Introduction
1 From Data to Decision-Making: A Major Pathway
1.1. Background on economic intelligence
1.2. Strategic economic intelligence revisited
1.3. Conclusion
2 Data: An Indispensable Platform for Companies
2.1. The key figures of digital technology
2.2. The power of data: a major challenge
2.3. The Big Data revolution, “Mega Data”
2.4. Developing the culture of data sharing
2.5. Storage of data in databases
2.6. The appearance of buzzwords: Big, Open, Viz, etc.
2.7. Conclusion
3 From Data to Information: Essential Transformations
3.1. Value creation from data processing
3.2. Value creation and analysis of open databases
3.3. From data to information: the “DataViz” or data visualization
3.4. From data to information: statistical processing
3.5. Turning mass data into an opportunity for innovation
3.6. Development of company assets in the web of data
3.7. Conclusion
4 Information: Contextualized and Materialized Data
4.1. What is information?
4.2. Internal and external information
4.3. Formal and informal information
4.4.
Importance of information
4.5. Décodex set up by Le Monde
4.6. Conclusion
5 From Information to Knowledge: Valuing and Innovating
5.1. Innovation as a driving force of growth
5.2. Knowledge: the key to innovation
5.3. Building knowledge: economic intelligence
5.4. Data mining, Statistica and Tibco
5.5. Information an economic good?
5.6. What is data science?
5.7. Conclusion
6 From Knowledge to Strategic Business Intelligence: Decision-Making
6.1. Data valuation mechanisms
6.2. How do you value data
6.3. Data governance: a key factor in valuation
6.4. EI: protection and enhancement of digital heritage
6.5. Data analysis techniques: data mining/text mining
6.6. Conclusion
Conclusion
Glossary
References
Webography
Index
Other titles from ISTE in Innovation, Entrepreneurship and Management
End User License Agreement
List of Illustrations
Introduction
Figure I.1. Digital golden jobs in 2018. For a color version of this figure,...
Figure I.2. TIBCO's data mining and Statistica software. The research and de...
Figure I.3. Forecasting Big Data market size, based on revenues, 2011-2027 (...
Figure I.4. The Big Data turnover and market size (source: Statista). For a ...
Figure I.5. With what interest do you follow the news through the informatio...
Chapter 1
Figure 1.1. The three concepts (source: Monino and Lucato 2005)
Figure 1.2. Business intelligence model of data to decision-making (source: ...
Figure 1.3. From data to decision-making strategic intelligence (source: Mon...
Chapter 2
Figure 2.1. MOOC1 introduces you to the GDPR2. The CNIL offers professionals...
Figure 2.2. ANSSI and information security. Raising French people's awarenes...
Figure 2.3. Digital technology in the world and its growth (source: We Are S...
Figure 2.4. Types of subscriptions and e-commerce activities in France (sour...
Figure 2.5. Penetration rate of smartphones among mobile phone users in Fran...
Figure 2.6. Number of emails sent and received every day in the world from 2...
Figure 2.7. Comparison by age of the number of hours spent on the Internet p...
Figure 2.8. Value of the Cloud computing market for companies worldwide from...
Figure 2.9. Statistical social networks8 (source: We Are Social/GlobalWebInde...
Figure 2.10. Number of companies analyzing Big Data using geo-location data ...
Figure 2.11. Categories and digital currency (source: We Are Social/GlobalWe...
Figure 2.12. Time spent and profile of the social media audience in France 2...
Figure 2.13. Information processing. Security and mass processing of C2i cer...
Figure 2.14. Percentage of companies purchasing Cloud computing services use...
Figure 2.15. History of open data (source: Monino and Sedkaoui). For a color...
Figure 2.16. History of communities that have opened their data14 (source: C...
Figure 2.17. Big Data and open data volumes (source: Statista). For a color ...
Chapter 3
Figure 3.1. Map of the divisions of the population. For a color version of t...
Figure 3.2. AUTOUR.CIM. For a color version of this figure, see www.iste.co....
Figure 3.3. Open data on the city of Paris. The site of the Open Data initia...
Figure 3.4. The data.gouv.fr website. In the same perspective and in order t...
Figure 3.5. Clouds of text or words. “The keyword cloud”: it is a visual rep...
Figure 3.6. Three-dimensional visualization. Animated visualization in space...
Figure 3.7. AFSM their main sources of income. For a color version of this f...
Figure 3.8. Ranking of the most attractive metropolises in the world. For a ...
Figure 3.9. The Intelligent City - Montpellier. Montpellier Méditerranée Mét...
Chapter 4
Figure 4.1. Advice, information, rumor. According to Agathe Dahyot/Le Monde,...
Figure 4.2. According to Jean-Pascal Perrein, the information flow is repres...
Figure 4.3. Several kinds of information (source: Monino 2016). For a color ...
Chapter 6
Figure 6.1. Data Centers worldwide. For a color version of this figure, see ...
Figure 6.2. A classification by data analysis concerning a group. For a colo...
Figure 6.3. Predictions using the neuron method. For a color version of this...
Figure 6.4. The stock market, chance, deterministic chaos
Figure 6.5. Example of educational resources put online by AUNEGE. Fora colo...
Figure 6.6. A model of strategic business intelligence, influence and inform...
Figure 6.7. Example screen of TIBCO's Statistica. For a color version of thi...
Conclusion
Figure C.1.The place of data in the digital society: a proposal for modelin...
Guide
Cover
Table of Contents
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Smart Innovation Setcoordinated byDimitri Uzunidis
Volume 29
Data Control
Major Challenge for the Digital Society
Jean-Louis Monino
First published 2020 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 Ltd27-37 St George’s RoadLondon SW19 4EUUKwww.iste.co.uk
John Wiley & Sons, Inc111 River StreetHoboken, NJ 07030USAwww.wiley.com
© ISTE Ltd 2020The rights of Jean-Louis Monino to be identified as the author of this work have been asserted by him in accordance with the Copyright, Designs and Patents Act 1988.
Library of Congress Control Number: 2020938410
British Library Cataloguing-in-Publication DataA CIP record for this book is available from the British LibraryISBN 978-1-78630-550-3
Foreword
In the fight against waste, one particular commodity, however eminently precious, is rarely mentioned: data.
While it is not a priority, it is first collected to serve as, or illustrate, a reasoning or an idea. The major challenge of the last two decades has refocused on another field: that of understanding and analysis. Offering a clear report facilitates the management and steering of an activity.
Data processing opens the door to strategic recommendation. Now that the value of data is known, its value can further improve, provided it is operational and ready to use.
The individual, the consumer and the voter are regularly solicited, sometimes against their will, often in ignorance of the risks involved. In the face of the abuses that have been noted, the law has had to be involved.
Before this stage of maturity, “just in case” storage is reassuring without really knowing why. This intuition leads companies to sometimes accumulate incomplete or unorganized data. Ignorance of its purpose has resulted in the accumulation of data in huge volumes, with the only limit being the technical constraints of the moment, the cost per byte.
So far, it has been impossible to use all this data due to lack of time and resources. Therefore, it is not possible to educate the overall population. Using a sample is imperative, with representativeness being a key concept.
Technological progress has made it possible to overcome this constraint of representativeness. The entire dataset is now vulnerable to attack even though the size is discouraging. These masses of data, veritable “assembled pieces”, have whetted the appetite of the greedy known as Big Data, machine learning, artificial intelligence and other “datavores”, reinventing a 2.0 statistic: data science.
At the same time, the proliferation of equipment (tools, applications) has democratized the use of data, making connection and leading to automatic and endemic branches. The variety of media (computers, tablets, smartphones), the explosion of networks and access providers facilitate the exchange, sharing and processing of information. Mobile consumption has become instantaneous.
This continuous feeding creates a new addiction, a demand of a different nature. In order to respond before it becomes obsolete, it is necessary to move quickly, even if it means returning the information to its original role as simple data. In this sense, pre-selections are made, and the data is delivered, sorted and “ready to read”, not necessarily ready to use. The preferences and tastes of the “target” are identified; its supposedly known expectations are identified in relation to its history. Trespassing?
However, the absence of filters or segmentation leads to fears of saturation. The data is ubiquitous despite its limited lifespan. It is refreshed and constantly updated. The required reactivity sometimes weighs on its quality. The corollary concession is the “average” information, which is difficult to exploit. It is against this risk of drowning or submersion that analysts, true lifeguards, fight. Their responsibilities are cleaning, reducing and synthesizing data; detailing or refocusing it if necessary; and using it in the delegated question. At the heart of the process, this human presence guarantees the surpassing of a simple mechanical treatment and therefore the creation of value. The mission of the latter consists, after all the stages of data processing, of restoring the true dimension of the data while making it profitable.
Here, Professor Monino's book brings the main tools that are indispensable to his mission, which, in the course of time, will become more and more indispensable. Unfortunately, not all companies have the necessary resources (time, budget, analyst) to extract or exploit this wealth. However, the use of specialists such as E2S-Conseils allows us to make the most of this information to help companies develop and flourish.
I hope that you enjoy reading and learning from this book.
Philippe RIBIEREChief Executive OfficerE2S-Conseils
Acknowledgements
I wish to dedicate this book to my sister who unfortunately was unable to read my work on data.
Also, all the members of the “Réseau de Recherche sur l'Innovation”, RRI, and in particular, Dimitri Uzunidis, who encouraged me to publish this work and I thank him warmly for his encouragement.
This book is the culmination of many years of research dedicated to data processing, to statistics and to econometrics in the TRIS laboratory (Traitement et Recherche de l'Information et de la Statistique). It is the fruit of various work carried out within the framework of R&D (Research & Development) for several start-ups in the Languedoc-Roussillon of France and large private and public groups.
Thank you to all those who have supported me during difficult times and have helped transform an individual intellectual adventure into a collective one. In particular I offer my thanks to Philippe RIBIERE, President of E2S-Conseils with whom I have written several R&D books over the years.
Thank you also to the team of researchers at the “Montpellier Recherche en Economie” laboratory who demonstrated their trust in my research and granted me my emeritus status.
I would also like to thank my children: Christine, Laurent, Caroline and Daniel, as well as their partners: Laurent, Alexandra, Guillaume and Louise who have all supported and surrounded me during the difficult times I had to face.
To my grandchildren: Lily-Rose, Jean-Baptiste, Alice and Gabriel.
To Anne-Marie, my wife.
Jean-Louis MONINO
Introduction
The world has become digital, and technological advances have increased the number of different ways for accessing, processing, and distributing data. New technologies are now reaching a certain maturity.
Today, data comes from all sides: geolocation sensors, smartphones, social networks where we share files, videos, photos, etc., Internet shopping transactions by customers, banking transactions through credit cards and so on. In France, out of 65 million people, 83% are Internet users; 42% are registered on Facebook, or 28 million members. More than 72 million phones are activated, and the French spend on average more than 4 hours a day surfing the Internet. French mobile users spend 58 minutes or more there; 68% of the population is registered on social networks. French people spend more than 1 hour and 30 minutes a day on social networks. The development of these masses of data and their access represents what is called “Big Data”. These immaterial data arrive continuously; their processing poses problems, particularly in knowledge extraction. Thus, new methods of automatic information extraction are implemented: for example, “data mining” or “text mining”. They underlie profound changes that affect the economy, marketing, research and even politics. The amount of data will increase sharply with the arrival on the market of connected objects that will gradually come into use. Elements of our daily life are already connected: the car, the television and some household appliances. They are or will be equipped with a chip to collect and transmit data to their users via a computer, tablet or smartphone. The most important thing is that these items will also be able to trade with each other! This will allow us to remotely manipulate the equipment in our home, in our vehicles by connecting to them from our home and outside our home, using smartphones or any other equipment. This is called “the Internet of Things”.
This phenomenon is now of interest to operational decision-makers (marketing managers, financial managers, etc.) when it comes to analyzing the immense potential of data held by companies in real-time. In order to meet the challenge of “Big Data”, measures should be taken that include all the tools that allow data to be processed in a more restrictive way, as well as all the actors who analyze these data. This will only be possible through an awareness of the gains offered by “data enhancement”. The data in the organized, reorganized bases, processed by statistical methods or econometric modeling, become knowledge.
For a company it is essential to have more and more data about the environment in which it operates or will operate. We will no longer work on classes of behaviors but on individual analysis. It is easy to understand that this revolution is leading to the creation of so-called “start-up” companies whose aim is the automatic processing of data that make up what is known as “Big Data”. This is certainly one of the components of what some call the “new industrial revolution”. The Internet, digital and connected objects have opened up new horizons in a multitude of fields.
As an example, access to data allows quantitative and qualitative analyses to be enriched. Customer contacts can be analyzed with data collected by a call center1; this kind of product can also be offered in limited numbers, as E2S-Conseils does. This needs to be developed by exploring the content of emails, voice calls, and mixing this information with website navigation. Or, study the messages exchanged on social networks (Facebook, Twitter, Linkedin, etc.) to identify interesting new products or to know which products are most talked about.
For data to reach its full value potential, it must be easily accessible to all interested parties without additional barriers and at economically bearable costs. If the data are open to users (Matouk - MTRL review), other specific data processing companies may be created. This activity will meet the needs of users without the need for them to build models and equations themselves. 1
In addition to its potential in economic terms and for the creation of new activities, the opening up of data is also a matter of philosophical or ethical choice. It quantifies collective human behavior and, therefore, belongs, in fact, to those whose behavior has been measured. The culture of this phenomenon is based on the availability of data towards a communication orientation.
To limit itself to the economic benefits of open data, a study carried out for the European Commission estimated the total public sector information market in 2008 in the EU at 28 billion euros. According to this study, the overall economic benefits of more open public sector data would amount to about 40 billion euros per year for the EU. For the EU economy as a whole, the total direct and indirect economic gains from the use of ISPs (Internet Service Providers) and applications based on these data would be in the order of 140 billion euros per year.
The Internet age has massively increased the search for information. Companies are overwhelmed by the flood of data that results from simply surfing the Internet. In other words, they are forced to acquire the relevant information to develop their high value-added strategies in order to master the ever-changing environment. Now, the management of industrial strategies is largely based on the ability of companies to access strategic information to better develop their environment. This information can therefore be the source of new knowledge (knowledge pyramid).
The process of collecting, processing and interpreting information is a practice that goes beyond the definition of ideas to the realization of those ideas to ensure a better production of knowledge that generates innovations. It is economic intelligence that enables each company to optimize its offer quantitatively and qualitatively and also to optimize its production technologies.
In addition to the advent of ICT, the speed of production, dissemination and data processing capacity, another element has become important in recent years: time. This temporal element implies a notion of speed of information flow. This calls for a rethinking of corporate strategy, beyond the difficulties posed by the processing of large volumes of data. The value of a data item increases over time and depends on the multiple uses that are made of it.
Remuneration in the digital professions rose sharply in 2018, according to Robert Half's remuneration study2. However, HR directors admit that it is often difficult to find the rare pearl: the average recruitment time for an executive manager is four months.
Figure I.1.Digital golden jobs in 2018. For a color version of this figure, see www.iste.co.uk/monino/control.zip
For some professions, the jump will be very high. According to Robert Half, the “golden job” in the digital world will be that of SEO Manager with 5-10 years of experience: the remuneration linked to this job should increase by 27% compared to 2017 and reach between 65,000 and 90,000 euros per year. Data scientists, even with little experience, will also see their salaries increase by 22%.
Figure I.2.TIBCO's data mining and Statistica software. The research and development project of E2S-Conseils in collaboration with the TRIS laboratory has been developed using Tibco's Statistica Data Mining for health research (MONINO L., TIBCO's data mining and Statistica software. The research and development project of E2S-Conseils in collaboration with the TRIS laboratory has been developed using Tibco's Statistica Data Mining for health research, Thesis, 2019). For a color version of this figure, see www.iste.co.uk/monino/control.zip
Big Data includes the processing of these large masses of data, from their collection and storage to their visualization and analysis. This data thus becomes the fuel of the digital economy. They constitute the raw material indispensable to the queen activity of the new century: “data intelligence”. This book shows that the stakes of data are focused on the integration and development in the company. It deals with the issue of data control and valuation in a competitive context.
Faced with this multiplication of data, companies have to mobilize sophisticated processing techniques. In fact, the mastery of processing techniques is now becoming a real strategic and useful question for the competitive differentiation of companies (Bughin and Chui 2010). The processing of these masses of data plays a key role in tomorrow's society, with applications in fields as diverse as science, marketing, customer services, sustainable development, transportation, health and even education.
Figure I.3.Forecasting Big Data market size, based on revenues, 2011-2027 (source: Statista). For a color version of this figure, see www.iste.co.uk/monino/control.zip
In this context, companies must have the capacity to absorb all available data, enabling them to assimilate and reproduce knowledge. This capacity presupposes the existence of specific skills that enable the use of this knowledge. The training of “data scientists” is therefore essential in order to be able to identify useful approaches to opening up or internal exploitation of data and to quantify the benefits in terms of innovation and competitiveness, since Big Data is only one element of a new set of tools and techniques called “data science”.
The Data Scientist's mission is to extract knowledge from company data. They will be called upon to perform strategic functions within the Commission. To do so, they must master the necessary tools. They must also be more pedagogical and increase their command of data mining, because the volume of data requires an increase in the range of techniques to be mastered.
Big Data includes the processing of these large masses of data, their collection, storage, visualization and analysis. This data thus becomes the driving force of the digital economy. They constitute the raw material indispensable to the queen activity of the new century: “data intelligence”. This book shows that the stakes of Big Data are focused on the integration and the development of data in the company. It deals with the issue of data valuation in a context of strong competition.
More precisely, it represents a research subject that involves several fields (Big Data, open data, data processing, innovation, economic intelligence, etc.). This multidisciplinarity allows us to bring a considerable enrichment to studies and research on the development of data warehouses in its entirety.
Information on the French population was published by various news media in 2019, for example, that published by Paul Manuel Godoy Hilario on 16 May 2019. This statistic displays an interest in the French population in the new data through the different information media in 2019. Approximately half of the respondents said they were quite interested in this news. In 2018, nearly 35% of French people turned first to generalist television channels to further develop information they had received.
Figure I.4.The Big Data turnover and market size (source: Statista). For a color version of this figure, see www.iste.co.uk/monino/control.zip
Figure I.5.With what interest do you follow the news through the information media (press, radio, television)? (source: Statista). For a color version of this figure, see www.iste.co.uk/monino/control.zip
This book is part of the continuation of the book Big Data, Open Data and Data Development published by ISTE and Wiley in their Smart Innovation set of books - coordinated by Dimitri Uzunidis - Innovation Research Network (Monino and Sedkaoui 2016). This book brings together all of my research work on economic intelligence over the last 20 years. Its objective is to show that the stakes of the “data revolution” era are focused on the integration and enhancement of data in the enterprise. It is part of a theme related to the rise of the intangible economy mobilizing knowledge and know-how, and highlights the importance of data. The contribution of information allows the generation of knowledge useful for decision-making, within the framework of the various activities specific to the development of the company. To do this, we must clarify what is more precisely a “Data” to be able to understand and formulate the expression “Big Data” issues.
We are going to follow this conception of EI (economic intelligence) (Monino and Boussetta 2013) step by step, based on the basic model proposed in 2005 by Monino and Lucato during the “mornings of the city” meetings at the Montpellier Chamber of Commerce and Industry. Data, information, knowledge and wisdom (decision) are all essential elements in preparing for a good decision and we will follow this in the following chapters of this book.
Notes
1
A call center is a center for processing incoming and/or outgoing calls.
2
Robert Half's study: “Les emplois en or du numérique en 2018” by Claire Jenik, Statistica, 11 Oct. 2017.
1From Data to Decision-Making: A Major Pathway
While information is at the heart of economic intelligence, data are essential elements required to build knowledge in order to make a good decision. This is something which at the moment may seem optimal in the possible fields of its knowledge. It is the use of data that gives power. As companies become increasingly aware of the importance of data and information, they are rushing to think about how to “manage”, enrich and leverage it.
Thus, the explosion of a phenomenal amount of data, and the need to analyze them, brings to the fore the well-known hierarchical model: “Data, Information and Knowledge”. This model is often exploited in the literature on information and knowledge management. Several studies claim that the first appearance of the hierarchy of knowledge can be found in T.S. Elliot's poem “The Rock” in 1934. In recent literature, several authors cite R.L. Ackoff's 1989 publication “From data to wisdom” as a source of the hierarchy of knowledge. Indeed, this hierarchical model highlights three words: “Data”, “Information” and “Knowledge”. The relationship between these three words can be represented in the above form where knowledge is given the highest place to emphasize the fact that a great deal of data is necessary for the acquisition of knowledge.
This hierarchical model is often exploited in the literature on information and knowledge management. It can also be exploited as an approach to the concept of business intelligence.
1.1. Background on economic intelligence
In the United States, it was the work of academics in the 1960s that revealed the importance and necessity of conceiving economic intelligence as a branch of the economy. Harold Wilensky's book “Organizational Intelligence” - 1967. It defines business intelligence as the activity of producing knowledge serving the economic and strategic goals of an organization, collected and produced in a legal context and from open sources.
Stevan Dedijer in the late 1960s conceptualized “intelligence” as an economic matter, and gave a broad definition: “Intelligence is the information itself, and its processing, and the organization that deals with it, while it obtains, evaluates and uses it under more or less secret, competitive or cooperative conditions, for the purposes of conducting any social system and about the nature, capacities, intentions, actual or potential operations, of internal or external opponents.”
Klaus Knorr was one of the first to advocate a wide dissemination of Business Intelligence, starting from the university space; for him it is “the operation to obtain and process information about the external environment in which an organization wants to maximize the achievement of its different goals”. Business intelligence is becoming a major component of corporate strategy and is based on information that is the foundation of any decision-making process. Information as such had received a fundamental scientific treatment without which economic intelligence could not have developed.
In France, the contribution of a group of experts from the Commissariat Général du Plan was published in 1994 under the name of the Martre Report. This work on the “Economic Intelligence and Company Strategy” proposes a definition in the introduction to the report: “Economic intelligence can be defined as all the coordinated actions of research, processing and distribution, with a view to its exploitation, of information useful to economic players. These various actions are carried out legally with all the necessary guarantees of protection to preserve the company's assets, in the best conditions of quality, time and cost. Useful information is that which is needed by the various levels of decision-making in the company or community to develop and consistently implement the strategy and tactics necessary to achieve the objectives defined by the company in
order to improve its position in its competitive environment. These actions, within the company, are organized in an uninterrupted cycle, generating a shared vision of the objectives to be achieved ”
