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From Data to Decision

Des données à la décision

FromD2D - © ISTE Ltd

Aims and scope

Objectifs de la revue

Collecting data is not enough. What stories do the data tell?


Data analytics and management is the process of turning data into meaningful information that humans can use to make effective decisions. Raw data is only the starting point. The data need to be analyzed, turned into information and made accessible to users, and the data also needs to meet varying needs and profiles, and be understandable to different audiences. The value of the data comes from the stories it tells. The journal From Data to Decision concerns all aspects related to managing, modeling, analyzing and exploiting data, in any context, as a basis for decision making.

Collecter la donnée ne suffit pas : que nous racontent les données ?


L’analyse et la gestion des données est un processus qui tend à transformer la donnée en lui donnant du sens, en une information sur laquelle l’humain peut se baser pour prendre des décisions effectives. Nous savons que la donnée n’est que le point de départ. Les données nécessitent d’être analysées, transformées en information et rendues accessibles à l’utilisateur. La donnée doit aussi répondre à des besoins variables et des profils, et être compréhensible par diverses audiences. La valeur de la donnée réside dans l’histoire qu’elle raconte. La revue Des données à la décision concerne tous les aspects liés à la gestion, la modélisation, l’analyse et l’exploration des données, dans tous les contextes, comme une base pour la prise de décision.

Journal issues


Volume 20- 3

Issue 1


Volume 18- 2

Issue 1


Volume 17- 1

Issue 1

Recent articles

From data processing to value creation: understanding professional practices of open data reusers
Valentyna Dymytrova, Françoise Paquienséguy

Based on a field survey conducted in France in 2017, this article identifies different forms of open data reuse and analyses the processing data chains on which they are relied. By analysing the chains and the tools used by three categories of professional reusers (developers, data scientists and data journalists), the authors discuss their links with the value creation chain. Professional practices and expectations are also discussed in terms of value generated by data, of economic model (informational brokerage) but also of innovative service creation. This article results partially of the research ANR OpenSensingCity.

Use of a Choquet Integral in an item-based recommendation system and a multicriteria active filtering, application to jeans recommendation and perspectives
Christophe Terrien

Though far from recent, the Choquet integral is at present the subject of an abundant literature in operational research. That tool opens perspectives of interesting applications in the marketing field, in particular in the implementation of recommendations systems based on multiple criteria. The integral of Choquet is interesting in the development of an attitude model because it makes it possible to take into account the relations of dependence between criteria. This paper proposes a practical use of the Choquet integral in an item-based recommendation system based on a multicriteria active filtering.

Using the quantization error from Self‐Organizing Map (SOM) output for fast detection of critical variations in image time series
Birgitta Dresp-Langley, John Mwangi Wandeto, Henry Okola Nyongesa

The quantization error (QE) from Self-Organizing Map (SOM) output after learning is exploited in these studies. SOM learning is applied on time series of spatial contrast images with variable relative amount of white and dark pixel contents, as in monochromatic medical images or satellite images. It is proven that the QE from the SOM output after learning provides a reliable indicator of potentially critical changes in images across time. The QE increases linearly with the variability in spatial contrast contents of images across time when contrast intensity is kept constant. The hitherto unsuspected capacity of this metric to capture even the smallest changes in large bodies of image time series after using ultra-fast SOM learning is illustrated on examples from SOM learning studies on computer generated images, MRI image time series, and satellite image time series. Linear trend analysis of the changes in QE as a function of the time an image of a given series was taken gives proof of the statistical reliability of this metric as an indicator of local change. It is shown that the QE is correlated with significant clinical, demographic, and environmental data from the same reference time period during which test image series were recorded. The findings show that the QE from SOM, which is easily implemented and requires computation times no longer than a few minutes for a given image series of 20 to 25, is useful for a fast analysis of whole series of image data when the goal is to provide an instant statistical decision relative to change/no change between images.

Editorial Board

Editor in Chief

Florence SEDES
​IRIT, Université Paul Sabatier, Toulouse


Laboratoire d’Electronique
Informatique et Image

Oscar DIAZ
University of Basque Country
San Sebastián

Université Grenoble Alpes

University of Zaragoza

Université du Sud Toulon-Var

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