Big Data and Strategy: A research Framework

With big data growing rapidly in importance over the past few years’, academics and practitioners have been considering the means through which they can incorporate the shifts these technologies bring into their competitive strategies. To date, there has been an emphasis on the technical aspects of big data with limited attention on the organizational changes they entail and how they should be leveraged strategically. As with any novel technology, it is important to understand the mechanisms and processes through which big data can add business value to companies and have a clear picture of the different elements and their interdependencies. To this end, the present paper aims to provide a theoretical discussion leading up to a research framework that can help explain the mechanisms through which big data lead to competitive performance gains. The research framework is grounded on past empirical work on IT-business, and builds on the resource-based view (RBV) and dynamic capabilities view (DCV) of the firm. By identifying the main areas of focus for big data and explaining the mechanisms through which they should be leveraged, this paper attempts to add to literature on how big data should be examined as a source of a competitive advantage

A Semiotic Approach to Investigate Quality Issues of Open Big Data Ecosystems

Part 2: Organisational Semiotics and ApplicationsInternational audienceThe quality of data models has been investigated since the mid-nineties. In another strand of research, data and information quality has been investigated even longer. Data can also be looked upon as a type of model (on the instance level), as illustrated e.g. in the product models in CAD-systems. We have earlier presented a specialization of the general SEQUAL-framework to be able to evaluate the combined quality of data models and data. In this paper we look in particular on the identified issues of ‘Big Data’. We find on the one hand that the characteristics of quality of big data can be looked upon in the light of the quality levels of the SEQUAL-framework as it is specialized for data quality, and that there are aspects in this framework that are not covered by the existing work on big data. On the other hand, the exercise has resulted in a useful deepening of the generic framework for data quality, and has in this way improved the practical applicability of the SEQUAL-framework when applied to discussing and assessing quality of big data