Data Control

Оглавление

Jean-Louis Monino. Data Control

Table of Contents

List of Illustrations

Guide

Pages

Data Control. Major Challenge for the Digital Society

Foreword

Acknowledgements

Introduction

Notes

1 From Data to Decision-Making: A Major Pathway

1.1. Background on economic intelligence

1.2. Strategic economic intelligence revisited

1.2.1. The three major steps for decision support

1.2.2. Modeling the concept of strategic business intelligence

1.2.2.1. Data

1.2.2.2. From data to information: monitoring

1.2.2.3. From intelligence to economic intelligence

1.2.2.4. Information

1.2.2.5. From information to knowledge: economic intelligence

1.2.2.6. Knowledge

1.2.2.7. From knowledge to decision-making: strategic business intelligence

1.3. Conclusion

Notes

2 Data: An Indispensable Platform for Companies

2.1. The key figures of digital technology

Box 2.1.The platform of the international agency We Are Social

2.1.1. Figures on social networks

2.1.2. Numbers: Big Data

Box 2.2.“Zero Marginal Cost Economy”

2.1.3. Key figures: the Internet of Things

Box 2.3.An example of data access

2.2. The power of data: a major challenge

Box 2.4.How to organize and use data

2.3. The Big Data revolution, “Mega Data”

2.3.1. Understanding the world of Big Data

2.3.1.1. What changes in data analysis?

2.3.1.2. Challenges around Big Data

Box 2.5.Study on mastery of data processing techniques

2.3.1.3. Making data warehouses intelligent

Box 2.6.The McKinsey Global Institute Report on Big Data and Smart Data

Box 2.7.The data scientist

2.3.2. Open data: a new challenge

2.3.2.1. Why open data?

Box 2.8.Definition of open data

Box 2.9.Three criteria for open data

2.3.2.2. Making the data reusable

Box 2.10.Producers and re-users, types of actors

Box 2.11. The benefits of opening up data

2.3.2.3. Open data and Big Data

Box 2.12.Proposed approach

2.3.2.4. Valuation and reuse of data

2.4. Developing the culture of data sharing

Box 2.13.Open data and job creation

2.5. Storage of data in databases

Box 2.14.Different types of databases. From an article on the LeBigData platform (Bastien 2019)

2.6. The appearance of buzzwords: Big, Open, Viz, etc

2.7. Conclusion

Box 2.15.Economic domain in three categories

Notes

3 From Data to Information: Essential Transformations. 3.1. Value creation from data processing1

Box 3.1.INSEE and the grid pattern

Box 3.2.A start-up population tracking application AUTOUR.CIM

Box 3.3.The coloring model proposed by Monino-Boya

3.2. Value creation and analysis of open databases

Box 3.4.The OECD identifies five areas of potential value creation

Box 3.5.The eight principles of data accessibility

3.3. From data to information: the “DataViz” or data visualization

3.4. From data to information: statistical processing

3.4.1. Phases of data processing

3.4.2. Processing the data

3.4.2.1. Processing of non-digitized data

3.4.2.2. Processing of digitized data

3.4.2.3. Statistical processing of data

Box 3.6.Data visualization

3.5. Turning mass data into an opportunity for innovation

Box 3.7.The 10 most innovative firms

Box 3.8.Ranking of the world's most attractive cities

3.6. Development of company assets in the web of data

3.7. Conclusion

Notes

4 Information: Contextualized and Materialized Data

4.1. What is information?

4.1.1. How can we define information?3

Box 4.1.Different meanings of information

Box 4.2.Overall approach to information governance

4.2. Internal and external information

4.2.1. Internal information

4.2.2. External information

4.3. Formal and informal information

4.3.1. Formal information

4.3.2. Informal information

4.4. Importance of information

4.4.1. White information

4.4.2. Gray information

4.4.3. Black information

4.5. Décodex5 set up by Le Monde

Box 4.3.The DÉCODEX set up by Le Monde

4.6. Conclusion

Notes

5 From Information to Knowledge: Valuing and Innovating

5.1. Innovation as a driving force of growth

5.1.1. Innovation and the intangible economy

5.2. Knowledge: the key to innovation

Box 5.1.Knowledge and new knowledge

5.3. Building knowledge: economic intelligence

5.3.1. The EI process and the transition from information to knowledge

5.3.2. Managing the data warehouse to extract knowledge and insight

5.4. Data mining, Statistica and Tibco1

5.5. Information an economic good?

5.5.1. Innovation as a driving force of growth

5.5.2. Strategic business intelligence

Box 5.2.Business intelligence and new risks for the 21st Century4

Box 5.3.McKinsey and Company

5.6. What is data science?

5.7. Conclusion

Note

6. From Knowledge to Strategic Business Intelligence: Decision-Making

6.1. Data valuation mechanisms

6.2. How do you value data

Box 6.1.Digital data centers or factories and number of data centers worldwide by country in 2018 (source: Statista)

Box 6.2.An example of data processing for the start-up 123PRESTA in 2010

Box 6.3.Time series forecasting with neural networks. Turnover of mass retail stores

Box 6.4.Chaos, Hurst and bootstrap exhibitors. An example applied to the Paris Stock Exchange (Matouk and Monino 2005)

6.3. Data governance: a key factor in valuation

Box 6.5.The challenges of multiple sources

Box 6.6.El short presentation videos

6.4. EI: protection and enhancement of digital heritage

Box 6.7.The place of geolocation data

6.5. Data analysis techniques: data mining/text mining

Box 6.8.An example of data processing a database for a start-up with TIBCO's Statistica

6.6. Conclusion

Notes

Conclusion

Box C.1.Creating value from data - suggestions

Note

Glossary

References

Webography

Index. A, B, C

D, E, F

G, H, I

K, M, N, P

Q, S, T,V

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Smart Innovation Set coordinated by Dimitri Uzunidis

Volume 29

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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.

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