Data Control. Jean-Louis Monino
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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 1 A call center is a center for processing incoming and/or outgoing calls.
2 2 Robert Half's study: “Les emplois en or du numérique en 2018” by Claire Jenik, Statistica, 11 Oct. 2017.
1 From 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