Just compensation? The price of death and injury after the Rana Plaza garment factory collapse

The 2013 collapse of the Rana Plaza factory building in Dhaka, Bangladesh was the most deadly disaster in garment manufacturing history, with at least 1,134 people killed and hundreds injured. In 2015, injured workers and the families of those killed received compensation from global apparel brands through a $30 million voluntary initiative known as the Rana Plaza Arrangement. Overseen by the International Labour Organization (ILO), the Rana Plaza Arrangement awarded payments to survivors using a pricing formula developed by a diverse team of ‘stakeholders’ that included labour groups, multinational apparel companies, representatives of the Bangladesh government and local employers, and ILO actuaries. This article draws from anthropological scholarship on the ‘just price’ to explore how a formula for pricing death and injury became both the means and form of a fragile political settlement in the wake of a shocking and widely publicised industrial disaster. By unpacking the complicated ‘ethics of a formula’ (Ballestero 2015), I demonstrate how the project of creating a just price involves not two sets of values (ethical and financial) but rather multiple, competing values. This article argues for recognition of the persistence and power of these competing values, showing how they variously strengthen and undermine the claim that justice was served by the Rana Plaza Arrangement. This analysis reveals the deficiencies of counterposing ‘morality’ and ‘economy’ in the study of price by reflecting upon all elements of price as situated within political economy and history

Evaluation of data warehouse design methodologies in the context of big data

The data warehouse design methodologies require a novel approach in the Big Data context, because the methodologies have to provide solutions to face the issues related to the 5 Vs (Volume, Velocity, Variety, Veracity, and Value). So it is mandatory to support the designer through automatic techniques able to quickly produce a multidimensional schema using and integrating several data sources, which can be also unstructured and, therefore, need an ontology-based reasoning. Accordingly, the methodologies have to adopt agile techniques, in order to change the multidimensional schema as the business requirements change, without a complete design process. Furthermore, hybrid approaches must be used instead of the traditional data-driven or requirement-driven approaches, in order to avoid missing the adhesion to user requirements and to produce a valuable multidimensional schema compliant with data sources. In the paper, we perform a metric comparison among different methodologies, in order to demonstrate that methodologies classified as hybrid, ontology-based, automatic, and agile are tailored for the Big Data context