'Datafication': Making sense of (big) data in a complex world

This is a pre-print of an article published in European Journal of Information Systems. The definitive publisher-authenticated version is available at the link below. Copyright @ 2013 Operational Research Society Ltd.No abstract available (Editorial

Predictive Analytics im Human Capital Management : Status Quo und Potentiale

First Online: 23 December 2015 Erworben im Rahmen der Schweizer Nationallizenzen (http://www.nationallizenzen.ch)Unternehmen verfügen mittlerweile über fortgeschrittene analytische Informationssysteme, die es erlauben die wachsenden Datenmengen nahezu automatisiert auszuwerten und Aussagen über zukünftige Entwicklungen zu treffen. Predictive Analytics befinden sie sich im Human Capital Management noch in den Anfängen. Datengetriebene Unternehmen wie Google oder Hewlett-Packard nutzen Predictive Analytics bereits, um Personalbeschaffung und -erhaltung zu verbessern. Obwohl jedoch die Personalbereiche über umfangreiche Daten verfügen, beschränkt sich deren Nutzung nach unserer Erfahrung und einer von uns durchgeführten Befragung in den meisten Fällen immer noch auf reaktives Excel-Reporting und einfachste Prognosen z. B. zur Personalanzahl. Data Mining-Verfahren werden hingegen selten genutzt, obwohl sich daraus für das Human Capital Management und andere Unternehmensbereiche Vorteile ergeben könnten. In diesem Beitrag stellen wir anhand von Praxisbeispielen und ausgewählter Fachliteratur Potentiale von Predictive Analytics im Human Capital Management vor, untersuchen die Verbreitung sowie die Einsatzmöglichkeiten von personalbezogenen Analysen und gehen auch auf die spezifischen Herausforderungen der Nutzung von Personaldaten ein

Learning frequent behaviors patterns in intelligent environments for attentiveness level

Nowadays, when it comes to achieving goals in business environments or educational environments, the performance successfully has an important role in performing a task. However, this performance can be affected by several factors. One of the most common is the lack of attention. The individual’s attention in performing a task can be determinant for the final quality or even at the task’s conclusion. In this paper is intended to design a solution that can reduce or even eliminate the lack of attention on performing a task. The idea consist on develop an architecture that capture the user behavior through the mouse and keyboard usage. Furthermore, the system will analyze how the devices are used.This work has been supported by COMPETE: POCI-01-0145-FEDER-007043 and FCT – Fundação para a Ciência e Tecnologia within the Project Scope: UID/CEC/00319/2013.info:eu-repo/semantics/publishedVersio

A people-oriented paradigm for smart cities

Most works in the literature agree on considering the Internet of Things (IoT) as the base technology to collect information related to smart cities. This information is usually offered as open data for its analysis, and to elaborate statistics or provide services which improve the management of the city, making it more efficient and more comfortable to live in. However, it is not possible to actually improve the quality of life of smart cities’ inhabitants if there is no direct information about them and their experiences. To address this problem, we propose using a social and mobile computation model, called the Internet of People (IoP) which empowers smartphones to recollect information about their users, analyze it to obtain knowledge about their habits, and provide this knowledge as a service creating a collaborative information network. Combining IoT and IoP, we allow the smart city to dynamically adapt its services to the needs of its citizens, promoting their welfare as the main objective of the city.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Uncovering the hidden costs of offshoring: The interplay of complexity, organizational design, and experience

This study investigates estimation errors due to hidden costs—the costs of implementation that are neglected in strategic decision-making processes—in the context of services offshoring. Based on data from the Offshoring Research Network, we find that decision makers are more likely to make cost-estimation errors given increasing configuration and task complexity in captive offshoring and offshore outsourcing, respectively. Moreover, we show that experience and a strong orientation toward organizational design in the offshoring strategy reduce the cost-estimation errors that follow from complexity. Our findings contribute to research on the effectiveness of sourcing and global strategies by stressing the importance of organizational design and experience in dealing with increasing complexity

Data Pipeline Management in Practice: Challenges and Opportunities

Data pipelines involve a complex chain of interconnected activities that starts with a data source and ends in a data sink. Data pipelines are important for data-driven organizations since a data pipeline can process data in multiple formats from distributed data sources with minimal human intervention, accelerate data life cycle activities, and enhance productivity in data-driven enterprises. However, there are challenges and opportunities in implementing data pipelines but practical industry experiences are seldom reported. The findings of this study are derived by conducting a qualitative multiple-case study and interviews with the representatives of three companies. The challenges include data quality issues, infrastructure maintenance problems, and organizational barriers. On the other hand, data pipelines are implemented to enable traceability, fault-tolerance, and reduce human errors through maximizing automation thereby producing high-quality data. Based on multiple-case study research with five use cases from three case companies, this paper identifies the key challenges and benefits associated with the implementation and use of data pipelines

Using Big Data to manage safety-related risk in the upstream oil & gas industry: a research agenda

Despite considerable effort and a broad range of new approaches to safety management over the years, the upstream oil & gas industry has been frustrated by the sector’s stubbornly high rate of injuries and fatalities. This short communication points out, however, that the industry may be in a position to make considerable progress by applying ‘‘Big Data’’ analytical tools to the large volumes of safety-related data that have been collected by these organizations. Toward making this case, we examine existing safety-related information management practices in the upstream oil & gas industry, and specifically note that data in this sector often tends to be highly customized, difficult to analyze using conventional quantitative tools, and frequently ignored. We then contend that the application of new Big Data kinds of analytical techniques could potentially reveal patterns and trends that have been hidden or unknown thus far, and argue that these tools could help the upstream oil & gas sector to improve its injury and fatality statistics. Finally, we offer a research agenda toward accelerating the rate at which Big Data and new analytical capabilities could play a material role in helping the industry to improve its health and safety performance

Big Data Analytics. Analyse der prädiktiven Fähigkeit von Twitter-Sentiments auf die Entwicklung des Börsenkurses von Technologieunternehmen

Die Datenmengen vervielfachen sich in der heutigen Zeit konstant, was zum Begriff Big Data geführt hat. Durch diese Datenmengen entsteht ein neues Potenzial, Fragen zu beantworten. Eine dieser Fragestellungen, welche mithilfe von Big Data untersucht werden kann, ist, inwiefern die Social-Media-Daten die Veränderung von Börsenkursen voraussagen können. Diese Studie untersucht die prädiktive Fähigkeit von Twitter-Nachrichten im Zusammenhang mit einem Technologieunternehmen und dessen Börsenkurs anhand von zwei Anwendungsfällen. Konkret wird anhand der Twitter-Nachrichten mithilfe einer Sentimentanalyse die Stimmung der Twitter-Nutzer mit den Veränderungen des Börsenkurses verglichen. Diese Analyse wird anhand der Technologieunternehmen Facebook und Amazon vorgenommen. In einem ersten Schritt wird untersucht, ob eine Beziehung zwischen den Twitter-Sentiments und dem Börsenkurs besteht. In einem zweiten Schritt, ob die Twitter-Sentiments eine Voraussagekraft für die Veränderung des Börsenkurses haben. Die Auswertung zeigt bei beiden Unternehmen eine positive Korrelation der Twitter-Sentiments und des Börsenkurses auf. Weiter konnte mithilfe der Granger-Analyse eine signifikante Voraussagekraft der Twitter-Sentiments für die Börsenkurse beider Unternehmen ermittelt werden. Die Twitter-Sentiments können die Börsenkurse 13 h voraussagen

Understanding the adoption of business analytics and intelligence

Cruz-Jesus, F., Oliveira, T., & Naranjo, M. (2018). Understanding the adoption of business analytics and intelligence. In Á. Rocha, H. Adeli, L. P. Reis, & S. Costanzo (Eds.), Trends and Advances in Information Systems and Technologies, pp. 1094-1103. (Advances in Intelligent Systems and Computing; Vol. 745). Springer Verlag. DOI: 10.1007/978-3-319-77703-0_106Our work addresses the factors that influence the adoption of business analytics and intelligence (BAI) among firms. Grounded on some of the most prominent adoption models for technological innovations, we developed a conceptual model especially suited for BAI. Based on this we propose an instrument in which relevant hypotheses will be derived and tested by means of statistical analysis. We hope that the findings derived from our analysis may offer important insights for practitioners and researchers regarding the drivers that lead to BAI adoption in firms. Although other studies have already focused on the adoption of technological innovations by firms, research on BAI is scarce, hence the relevancy of our research.authorsversionpublishe