Data Development Innovation Entrepreneurship Management 1016 pdf pdf

  Big Data, Open Data and Data Development

  Smart Innovation Set coordinated by Dimitri Uzunidis

  Volume 3 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.

  

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  ISTE Ltd John Wiley & Sons, Inc. 27-37 St George’s Road 111 River Street London SW19 4EU Hoboken, NJ 07030

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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 1. The Big Data Revolution . . . . . . . . . . . . . . . . . .

  1 1.1. Understanding the Big Data universe . . . . . . . . . . . . . . . .

  2 1.2. What changes have occurred in data analysis? . . . . . . . . . .

  8

  1.3. From Big Data to Smart Data: making data warehouses intelligent . . . . . . . . . . . . . . . . . . . . . . . . . . . .

  12

  1.4. High-quality information extraction and the emergence of a new profession: data scientists . . . . . . . . . . . . .

  16 1.5. Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

  21 Chapter 2. Open Data: A New Challenge . . . . . . . . . . . . . . .

  23 2.1. Why Open Data? . . . . . . . . . . . . . . . . . . . . . . . . . . .

  23 2.2. A universe of open and reusable data . . . . . . . . . . . . . . . .

  28 2.3. Open Data and the Big Data universe . . . . . . . . . . . . . . . .

  33 2.4. Data development and reuse . . . . . . . . . . . . . . . . . . . . .

  38 2.5. Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

  41

Chapter 3. Data Development Mechanisms . . . . . . . . . . . .

  43 3.1. How do we develop data? . . . . . . . . . . . . . . . . . . . . .

  44

  3.2. Data governance: a key factor for data valorization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

  54

  3.3. CI: protection and valuation of digital assets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

  60

  3.4. Techniques of data analysis: data mining/text mining . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

  65 3.5. Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

  72 Chapter 4. Creating Value from Data Processing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

  73

  4.1. Transforming the mass of data into innovation opportunities . . . . . . . . . . . . . . . . . . . . . . . . . .

  74

  4.2. Creation of value and analysis of open databases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

  82

  4.3. Value creation of business assets in web data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

  87

  4.4. Transformation of data into information or “DataViz” . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

  94

  4.5. Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 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 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.

  ONINO

  Jean-Louis M 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 S EDKAOUI Foreword

  The world has become a digitalized place, and technological advancements have multiplied the ways of accessing, processing and disseminating data. Today, new technologies have reached a point of maturity. Data is available to everyone throughout the planet. In 2014, the number of Internet users in the world was 2.9 billion, which is 41% of the world population. The thirst for knowledge can be perceived in the drive to seize this wealth of data. There is a need to inquire, inform and develop data on a massive scale. The boom in networking technologies – including the advent of the Internet, social networks and cloud computing (digital factories) – has greatly increased the volume of data available. As individuals, we create, consume and use digital information: each second, more than 3.4 million emails are sent throughout the world. That is the equivalent of 107,000 billion emails per year, with over 14,600 per person per year, although more than 70% of them are junk mail. Millions of links are shared on social networks, such as Facebook, with over 2.46 million shares every minute. The average time spent on the Internet is over 4.8 hours per day on a computer and 2.1 hours on a cellphone. The new immaterial substance of “data” is produced in real-time. It arrives in a continuous stream flowing from a variety of generally heterogeneous sources. This shared pool of all kinds of data (audio, video, files, photos, etc.) is the site of new activities aimed at analyzing the enormous mass of information. It thus becomes necessary to adapt and develop new approaches, methods, forms of knowledge and ways of working, all of which involve new paradigms and stakes as a new ordering system of knowledge must be created and put into place. For most companies, it is difficult to manage this massive amount of data. The greatest challenge is interpreting it. This is especially a challenge for those companies that have to use and implement this massive volume of data, since it requires a specific kind of infrastructure for the creation, storage, treatment, analysis and recovery of the same. The greatest challenge resides in “developing” the available data in terms of quality, diversity and access speed.

  Alain I OZZINIO E-PROSPECTS Manager

  January 2016 Key Concepts

  Before launching into the main text of this book, we have found it pertinent to recall the definitions of some key concepts. Needless to say, the following list is not exhaustive:

• – Big Data: The term Big Data is used when the amount of data that an organization has to manage reaches a critical volume that requires new technological approaches in terms of storage, processing, and usage. Volume, speed, and variety are usually the three criteria used to qualify a database as “Big Data”.
• – Cloud computing: This term designates a set of processes that use computational and/or storage capacities from remote servers connected through a network, usually the Internet. This model allows access to the network on demand. Resources are shared and computational power is configured according to requirements.
• – Competitive intelligence: It is the set of coordinated information gathering, processing and dissemination activities useful for economic actors. According to the Marte Report, competitive intelligence can be defined as the set of coordinated information research, processing and dissemination actions aimed at exploiting it for the purpose of economic actors. This diverse set of actions is carried out legally with all data protection guarantees necessary to preserve the company’s assets, with the highest regard to quality, deadlines and cost. Useful information is needed at the company or partnership’s different decision-making levels in order to design and put into place

defined objectives and improving its position in the competitive environment in which it operates. These kind of actions take place in an uninterrupted cycle that generates a shared vision of company objectives.

• – Data: This term comprises facts, observations and raw information. Data itself has little meaning if it is not processed.
• – Data analysis: This is a class of statistical methods that makes it possible to process a very large volume of data and identify the most interesting aspects of its structure. Some methods help to extract relations between different sets of data and thus draw statistical information that makes it possible describe the most important information contained in the data in the most succinct manner possible. Other techniques make it possible to group data in order to identify its common denominators clearly, and thereby understand them better.
• – Data governance: It constitutes a framework of quality control for management and key information resource protection within a company. Its mission is to ensure that the data is managed in accordance with the company’s values and convictions, to oversee its quality and to put mechanisms into place that monitor and maintain that quality. Data governance includes data management, oversight, quality evaluation, coherence, integrity and IT resource security within a company.
• – Data journalism: The term designates a new form of journalism based on data analysis and (often) on its visual representation. The journalist uses databases as his or her sources and deduces knowledge, meaningful relationships or intuitions from them that would not be accessible through traditional research methods. Even when the article itself stands as the main component of the work, illustrating ideas through graphs, diagrams, maps, etc., is becoming more important day by day.
• – Data mining: Also referred to as knowledge discovery from data, is intended for the extraction of knowledge from large amounts of data using automatic or semi-automatic methods. Data mining uses algorithms drawn from disciplines as diverse as statistics, artificial

data; that is, in order to find interesting structures or recurrent themes according to criteria determined beforehand and to extract the largest possible amount of knowledge useful to companies. It groups together all technologies capable of analyzing database information in order to find useful information and possible significant and useful relationships within the data.

• – Data reuse: This practice consists of taking a dataset in order to visualize it, merge it to other datasets, use it in an application, modify it, correct it, comment it, etc.
• – Data science: It is a new discipline that combines elements of mathematics, statistics, computer science and data visualization. The objective is to extract information from data sources. In this sense, data science is devoted to database exploration and analysis. This discipline has recently received much attention due to the growing interest in Big Data.
• – Data visualization: Also known as “data viz”, it deals with data visualization technology, methods and tools. It can take the form of graphs, pie-charts, diagrams, mappers, timelines or even original graphic representations. Presenting data through illustrations makes it easier to read and understand.
• – data.gouv.fr: The French government’s official website for public data, which was launched on December 5th 2011 by Mission Etalab. In December 2013, data.gouv.fr was transformed deeply through a change in both the site’s structure and its philosophy. It has, without doubt, become a collaborative platform oriented towards the community, which has resulted in better reuse of public data.
• – Dataset: Structured and documented collection of data on which reusers rely.
• – Etalab: This is a project proposed in the November 2010 Riester Report and put into place in 2011 which is responsible for implementing the French government’s open data policy, as well as for establishing an almanac of French public data: data.gouv.fr.
• – Hadoop: Big Data software infrastructure that includes a storage system and a distributed processing tool.

• – Information: It consists of interpreted data and has discernible meaning. It describes and answers questions like “who?”, “what?”, “when?” and “how many?”.
• – Innovation: It is recognized as a source of growth and competitiveness. The Oslo Manual distinguishes between four types of innovation:
• Product innovation: Introduction of a new product. This definition includes significant improvements to technical conditions, components or materials, embedded software, user friendliness or other functional characteristics.
• Process innovation: Establishing a new production or distribution method, or significantly improving an existing one. This notion involves significant changes in techniques, material and/or software.
• Marketing innovations: Establishing a new marketing method involving significant changes in a product’s design, conditioning, placement, promotion or pricing.
• Organizational innovation: Establishing a new organizational method in practices, workplace organization or company public relations.
>– Interoperability: This term designates the capacity of a product or system with well-known interfaces to function in sync with other existing or future products or systems, without access or execution restrictions.
• – Knowledge: It is a type of know-how that makes it possible to transform information into instructions. Knowledge can either be obtained through transmission from those who possess it, or by extraction from experience.
• – Linked Open Data (LOD): This term designates a web-approach proposed by supporters of the “Semantic Web”, which describes all data in a way such that computers can scan it, and which links to it by describing its relationships, or by making it easier for the data to be related. Open public data is arranged in a “Semantic Web” format,

such that its items have a unique identifier and datasets are linked together by those identifiers.

• – Open innovation: It is defined as increased use of information and knowledge sources external to the company, as well as the multiplication of marketing channels for intangible assets with the purpose of accelerating innovation.
• – Open knowledge foundation network: A British non-profit association that advocates for open data. It has most famously developed CKAN (open source data portal software), a powerful data management system that makes data accessible.
• – Open data: This term refers to the principle according to which public data (gathered, maintained and used by government bodies) should be made available to be accessed and reused by citizens and companies.
• – Semantic Web: This term designates a set of technologies seeking to make all web resources available, understandable and usable by software programs and agents by using a metadata system. Machines will be able to process, link and combine a certain amount of data automatically. The semantic web is a set of standards developed and promoted by W3C in order to allow the representation and manipulation of knowledge by web tools (browsers, search engines, or dedicated agents). Among the most important, we can cite:
• RDF: a conceptual model that makes it possible to describe any dataset in the form of a graph in order to create knowledge bases;
• RDF Schema: language that makes it possible to create vocabularies, a set of terms used to describe things;
• OWL: A language that makes it possible to create ontologies and more complex vocabularies that serve as support for logical processing (interfaces, automatic classification, etc.);
• SPARQL: A query language for obtaining information from RDF graphs.
• – Semi-structured information: It is worth noting that the boundary between structured information and unstructured information is rather

one category or the other. In such a case, one is no doubt dealing with semi-structured information.

• – Smart Data: The flood of data encountered by ordinary users and economic actors will bring about changes in behavior, as well as the development of new services and value creation. This data must be processed and developed in order to become “Smart Data”. Smart Data is the result of analysis and interpretation of raw data, which makes it possible to effectively draw value from it. It is, therefore, important to know how to work with the existing data in order to create value.
• – Structured information: It can be found, for example, in databases or in programming languages. It can thus be recognized by the fact that it is arranged in a way such that it can be processed automatically and efficiently by a computer, but not necessarily by a human. According to Alain Garnier, the author of the book

  

Unstructured Information in Companies, “information is structured

  when it is presentable, systematic, and calculable”. Some examples include forms, bills, pay slips, text documents, etc.

• – Text mining: This is a technique that makes it possible to automate processing of large volumes of text content to extract the main tendencies and statistically assess the different subjects they deal with.
• – Tim Berners-Lee: He is the co-inventor of the Semantic Web. He is very active and engaged in data.gov.uk. In particular, he has defined a five star ranking system to measure the Semantic Web openness level for putting a dataset online.
• – Unstructured information: Unlike structured information, unstructured information constitutes the set of information for which it is impossible to find a predefined structure. It is always intended for humans, and is therefore composed mainly of text and multimedia documents, like letters, books, reports, video and image collections, patents, satellite images, service offers, resumes, calls for tenders, etc. The list is long.
• – Web 1.0: This term refers to the part of the Internet that makes it possible to access sites composed of web pages connected by

hyperlinks. This Web was created at the beginning of the 1990s. It creates a relationship between an edited site that publishes content or services and Internet users who visit it and who surf from site to site.

• – Web 2.0: This term designates the set of techniques, functions and uses of the World Wide Web that have followed the original format of the Web. It concerns, in particular, interfaces that allow users with little technical training to appropriate new Web functions. Internet users can contribute to information exchanges and interact (share, exchange, etc.) in a simple manner.
• – Web 3.0: (also known as the Semantic Web). This is a network that allows machines to understand semantics, which is to say the meaning of information published online. It expands the network of Web pages understandable by humans by adding metadata that is understandable by a machine and that creates links between content and different pages, which in turns allows automatic agents to access the Web in a more intelligent manner and to carry out some tasks in the place of users.

  Introduction

  Today data comes from everywhere: GPS tracking, smartphones, social networks where we can share files, videos and photos, as well as online client transactions made possible through the intermediary of credit cards. Of the 65 million people in France, 83% are Internet users and 42% (or 28 million) are on Facebook. More than 72 million telephones are active, and the French people spend a daily average of 4 hours online. Mobile phone users spend over 58 minutes online, and 86% of the population is on a social network. The French people spend over 1.5 hours per day on social networks.

  Developing this massive amount of data and making access to it possible is known as “Big Data”. This intangible data comes in a constant stream and its processing is especially challenging in terms of knowledge extraction. This is why new automatic information extraction methods are put into place such as, for example, “data mining” and “text mining”. These are the sorts of processes behind radical transformations in the economy, marketing and even politics. The amount of available data will increase greatly with the appearance of new connected objects on the market that are going to be used more and more.

  Some objects we use in our daily lives are already connected: cars, television sets and even some household appliances. These objects have (or will one day have) a chip designed to collect and transfer data to their users through a computer, a tablet or a smartphone. More importantly, these objects will also be able to communicate with one another! We will be able to control equipment in our homes and in our car by simply logging onto our smartphone or some other device. This phenomenon is known as the “Internet of Things”.

  

The American economist, Jeremy Rifkin, predicted the development of a new

society of wealth and abundance brought about by technology, especially by

the Internet of Things and 3D printing. New technologies would modify

socio-economic relationships to the point of significantly reducing profits for

capitalist enterprises. In the world of the Internet, the advent of the zero

marginal cost society has already taken place. As information has become

dematerialized and as it has become possible to reproduce and distribute it

with near-zero marginal costs, radical changes have come about in these

industries’ business models.

  

Box I.1. Zero marginal cost society

  This phenomenon has attracted the interest of operational decision- makers (marketing managers, finance chiefs, etc.) seeking to benefit from the immense potential involved in analyzing data hosted by companies in real-time. In order to meet the Big Data challenge, measures must be taken, including incorporating tools that make more restrictive data processing possible and actors capable of analyzing that data. This will only be possible if people become more aware of the benefits of “data development”. When databases are organized, reorganized and processed by statistical methods or econometric modeling, they become knowledge.

  For a company, it is essential to have access to more and more data about the environment in which it operates. This will make it possible to scrutinize not classes of behavior, but individual cases. This explains why this revolution has brought with it the emergence of so- called “start-up” companies whose objective is to process the data known as Big Data automatically. We certainly find ourselves in front of one of the elements of what some people are calling the “new industrial revolution”. The Internet and the digital and connected objects have opened up new horizons in a wide array of fields.

  

Data access makes it possible to enrich quantitative and qualitative analyses.

Client contacts can be analyzed through data collected by a call center. This

kind of product can also be offered in a limited quantity, as does e-prospects. It

is necessary to develop that data by exploring the content of emails and voice

calls, and to match that information with browsing activities on the company

website. Beyond that, it is also possible to study messages exchanged on social

networks (Facebook, Twitter, LinkedIn, etc.) in order to identify new trends or

to identify the products that are being most talked about.

  

Box I.2. A data access example

₁ E-prospects has developed an innovative service based on a community platform that makes it possible to buy and sell _{Contact and appointment} contacts and _buyers meetings through _{• Request contacts and appointments in their sectors • Professionals} a range of products and _{Purchase • • Demand} countries. _{Demand Supply} Research and _{Possibility of buying individually • Choice between various providers Production}

^•

^•

development with _{Different selling/purchasing models Neutral and legitimate information on the provider Contact and appointment} the University of _{W eb agencies • Call centers TRIS laboratory . Telemarketing companies providers} Montpellier’s ₂

_•

_•

_{Leads professionals}

^•

The program is supported by the

  Languedoc- Roussillon region and by the “Transferts L-R” ₃ program .

  

Example I.1. The startup E-PROSPECTS

1 http://www.e-prospects.biz.

  

2 TRIS stands for “Information and Statistics Development and Research” in French.

  

3 Transfert LR stands for “Regional Languedoc-Roussillon Innovation Agency” in

  In order to get the full potential out of data, it must be available to all interested parties with no additional obstacles and at reasonably accessible costs. If data is open to users [MAT 14], other specialized data processing companies can be created. This activity will meet the needs of users without them having to develop models and equations themselves.

  Open Data, beyond its economic and innovative potential, involves

  4

  a philosophical or ethical choice . Data describes collective human behavior, and therefore, belongs to those whose behaviors it measures. The cultivation of these phenomena depends on the availability of data that can be communicated easily.

  The Internet Age has detonated a boom in information research. Companies are flooded by the wealth of data that results from simple Internet browsing. In other words, they are forced to purchase pertinent information to develop high added value strategies that allows them to succeed in the face of incessant changes in their business environment. Industrial strategies now rely strongly on the capacity of companies to access strategic information to better navigate their environment. This information can, thus, become the source of knew knowledge (knowledge pyramid).

  The process of gathering, processing and interpreting information is not limited to defining ideas, but also consists of materializing them in order to ensure improved knowledge production that leads to innovation. Competitive intelligence allows each company to optimize its service offerings in qualitative and quantitative terms, as well as to optimize its production technology.

  

4 To speak just of the economic advantages of open data, a study carried out for the

European Commission estimated that the total market for public sector information in

2008 in the EU reached 28 billion euros. According to this study, the global economic

advantages resulting from more open public sector data would represent about 40

billion euros per year for the EU [VIC 11]. For the entire EU economy, the total direct

and indirect gains from ISPs (Internet Service Providers) and applications based on

  In the last few years, several initiatives to verify the authenticity of diplomas have been launched. In this regard, it is worth noting that University of Montpellier and, in particular, its research laboratory TRIS have been pioneers in the fight against diploma falsification since 2011.

  

Example I.2. Information processing C2i certificate

security and massive processing ⁵ by QRCode ⁶ Beyond the advent of ICT and of increased data production,

  dissemination and processing speeds, another element has recently become critically important: time. The importance of time carries with it a notion of information circulation speed. This prompts companies to rethink their strategies beyond the challenges involved in processing large volumes of data. The value of a given piece of data increases in time and depends on the variety of uses it is given.

  In this sense, companies must possess the capacity to absorb the entirety of data available, which allows them to assimilate and reproduce knowledge. This capacity requires specific skills that make

  

5 C2i: IT and Internet Certification Established in 2004 by the French National

Ministry of Education and Research. One of the websites devoted to the certification:

http://www.portices.fr

  

6 A QR code is a type of two dimensional barcode (or datamatrix code) composed of

black squares aranged in a larger squeare with a white background. The distribution of

these points defines the information contained in the code. QR (an abbreviation for

quick response) means that the code’s content can be quickly decoded when read by a ^{EXAMPLE (enlarged )} it possible to use that knowledge. Training “data scientists” is, therefore, indispensable in order to be able to identify useful approaches for new opportunities, or for internal data exploitation, and in order to quantify their benefits in terms of innovation and competitiveness. However, Big Data is just a single element in the new set of technical tools known as “data science”.

  Data scientists have the task of extracting knowledge from company data. They hold a strategic function within the firm, and to that end, must be in command of the necessary tools. They must also be able to learn on the go and increase their understanding of data mining regularly, as the volume of data requires increasing skills and techniques.

  E-prospects’s research and development project, carried out in collaboration with TRIS, has been developed by using Statistica’s data mining.

  

Example I.3. Data mining and Statistica software

  When confronted with this multiplicity of data, companies are driven to apply sophisticated processing techniques. In fact, technical competence in data processing is today a genuine strategic and useful stake for companies’ competitive differentiation [BUG 11]. Processing this mass of data plays a key role for tomorrow’s society because it can be applied in fields as varied as science, marketing, customer services, sustainable development, transportation, health and even education. Big Data groups together both processing, collection, storage and even visualization of these large volumes of data. This data, thus, becomes the fuel of the digital economy. It is the indispensable raw material of one of the new century’s most important activities: data intelligence. This book shows that the main challenges for Big Data revolve around data integration and development within companies. It explores data development processes within a context of strong competition.

  More specifically, this book’s research brings together several different fields (Big Data, Open Data, data processing, innovation, competitive intelligence, etc.). Its interdisciplinary nature allows it to contribute considerable value to research on the development of data pools in general.

I.1. The power of data

  Companies are very conscious of the importance of knowledge and even more so of the way it is “managed”, enriched and capitalized. Beyond all (financial, technical and other) factors, the knowledge that a company has access to is an important survival tool, whether it is market knowledge, or legal, technological and regulatory information.

  Knowledge is an extension of information to which value has been added because it is underpinned by an intention. It is a specifically- human phenomenon that concerns thought in rational, active and contextual frameworks. It represents an acquisition of information translated by a human element and which requires analysis and synthesis in order to assimilate, integrate, criticize and admit new knowledge.

  Information corresponds to data that has been registered, classified, organized, connected and interpreted within the framework of a specific study. Exploiting collected data requires: sorting, verification, processing, analysis and synthesis. It is through this process that raw data collected during research is transformed into information. Data processing provides information that can be accessed in decision- making moments.

  Lesca [LES 08] explores the problems behind interpreting data to transform it into strategic information or knowledge. Interpretation systems, which are at the heart of competitive intelligence, are defined as “meaning attribution systems”, since they assign meaning to information that companies receive, manipulate and store [BAU 98].

  According to Taylor [TAY 80], the value of information begins with data, which takes on value throughout its evolution until it achieves its objective and specifies an action to take during a decision. Information is a message with a higher level of meaning. It is raw data that a subject in turns transforms into knowledge through a cognitive or intellectual operation.

  This implies that in the information cycle, collected data must be optimized in order to immediately identify needs and address them as soon as possible. This will, in turn enhance interactions between a diversity of actors (decision-makers, analysts, consultants, technicians, etc.) within a group dynamic favoring knowledge complementarity, one which would be aimed at improving the understanding, situational analyses and information production necessary for action. Indeed, “operational knowledge production quality depends on the human element’s interpretation and analysis skills when it is located in a collective problem solving environment” [BUI 06, BUI 07].

  Everyone produces data, sometimes consciously, sometimes unconsciously: humans, machines, connected objects and companies. The data we produce, as well as other data we accumulate, constitutes a constant source of knowledge. Data is, therefore, a form of wealth, and exploiting it results in an essential competitive advantage for an ever-tougher market.

  The need to exploit, analyze and visualize a vast amount of data confirms the well-known hierarchical model: “data, information and knowledge”. This model is often cited in the literature concerning information and knowledge management. Several studies show that the first mention of the hierarchy of knowledge dates back to T.S. Elliot’s 1934 poem “The Rock”. The poem contains the following verses:

• – where is the wisdom we have lost in knowledge; – where is the knowledge we have lost in information. In a more recent context, several authors have cited “From Data to Wisdom” [ACK 89] as being the source of the knowledge hierarchy. Indeed, the hierarchic model highlights three terms: “data”,

  7

  “information” and “knowledge” . The relationship between the three terms can be represented in the following figure, where knowledge is found at the top to highlight the fact that large amounts of data are necessary in order to achieve knowledge.

  Knowledge Knowledge Information

  Information Data

  Data

Figure I.1. Relationship between data, information

and knowledge [MON 06]

  

7 This hierarchic model was taken up by Monino and Lucato [MON 06] in order to

demonstrate the importance of information in the business intelligence process (see

  Wisdom accumulation (intelligence) is not truth. Wisdom emerges when the fundamental elements giving rise to a body of knowledge are understood. For Elliot, wisdom, is hence, the last phase of his poem. In our presentation of the concept of competitive intelligence, we have made decision-making the equivalent of wisdom (see Figure I.2).

  FINANCIAL _{OBSERVATION AWARENESS}

INTELLIGENCE DECISION- MAKING

  _{Define search Analyze the criteria information and Collect and process} Orientate Share _{Search for the Validate and spread} integrate it into the Change _{data information} decision-making process

  Improve

Figure I.2. The hierarchic model: data, information,

₈ and knowledge [MON 06]

  The boom in the number of available data sources, mostly coming from the Internet, coupled with the amount of data managed within these sources, has made it indispensable to develop systems capable of extracting knowledge from the data that would otherwise be hidden by its complexity. This complexity is mostly due to the diversity, dispersion and great volume of data.

  

8 Jean Louis Monino, is a professor and director of the TRIS laboratory at the

University of Montpellier. Gilles Lucato is the technological council in charge of

“digital data acquisition, processing and visualization” at Transfert LR, the How wever, the vo olume will co ontinue to inc crease at an e exponential r ate in years s to come. It t will, therefo ore, become necessary fo or companies s to address s challenges to position th hemselves on n the market t, retain mark ket share an nd maintain a competitiv ve advantage. . The greates st challenge h has to do w with the capa acity to comp pare the large est possible a amount of da ata in order r to extract th he best decisi ion-making e elements.

  Data conc cerning RV parking in n the Bouches- du-Rhône department wa as compared with tourist data in ord der to make RV

  V travelers’ j journeys easier r. In Rennes s, it was possibl le to develop p an app making g handicapp ped persons’ trips easie er. The same idea was t taken up by the city of Mo ontpellier. Website: http://www w.handimap.org g/

  Example I.4

4. An example from France’s s Bouches-du u-Rhône

  

Administrat tive Departme ent and from th he city of Mont tpellier

  Exp ploited data g generates bil llions of dol lars, accordi ing to differe ent reports . See, for e example, the e McKinsey y Institute’s report, whi ich recently y showed th hat making “Open Data a” available to the pub blic would allow the Un nited States to save 230 billion dolla ars by 2020 by allowin ng startups t to develop i innovative s ervices aime ed at reduci ing

  Another exam mple that recently y made headlines: th he arrival of the e American co ompany “Netflix” in France. It sells “vide eo on demand” in t the form of film ms and TV series, competing w with classic TV operators (e.

  g. Orange) at a a lower price. How d does it decide what content to provide, g going as far as to mak ke personalized d offers? By making a a progressively y enriched ana alysis of our purchase es and consumption n habits that ultimate ely defines our t tastes with a high d degree of precision.

  

Example I.5. Netflix

McKinsey & C Company is a c consulting firm advising top m management per rsonnel

that was foun nded 1926 in C Chicago by Jam mes Oscar McK Kinsey, a well-k known

professor at th he University o of Chicago. Mc Kinsey set up t the Global Inst titute a

few blocks aw way from the W White House i n Washington DC with the a aim of

studying mac croeconomic te endencies. Som me of their re esearch is pub lished,

especially in th he McKinsey Q Quarterly journa al.

  Box I

I.3. McKinsey & Company

  The challe enge with B ig Data has to do with h studying th he large vo olume of a available da ata and con nstructing m models capa able of producing analyses compatible with the businesses’ requirements. This data can in turn be used as explaining variables in the “models” on which a variety of users rely to make predictions or, more precisely, to draw relations based on past events that may serve to make future projections.

  If data is collected and stored it is because it holds great commercial value for those who possess it. It allows to target services and products to a more and more precise set of customers as determined by the data. It is indeed at the heart of “intelligence” in the most general sense of the word.

I.2. The rise of buzzwords related to “data” (Big, Open, Viz)

  In recent years, economic articles that do not make some mention or other of terms related to “data” (such as “Big Data”, “Open Data”, or “data mining”) when talking about companies have become few and far between. This tendency has led some scholars to believe that these concepts are emerging as new challenges that companies can profit from. We will chart the emergence of the data boom, especially of Big Data and Open Data, by studying search tendencies on Google (Google Trends).