Zoekresultaten

Developing Data Driven Business Models for Interactive Media Companies

The growing availability of data offers plenty of opportunities for data driven innovation of business models for SMEs like interactive media companies. However, SMEs lack the knowledge and processes to translate data into attractive propositions and design viable data-driven business models. In this paper we develop and evaluate a practical method for designing data driven business models (DDBM) in the context of interactive media companies. The development follows a design science research approach. The main result is a step-by-step approach for designing DDBM, supported by pattern cards and game boards. Steps consider required data sources and data activities, actors and value network, revenue model and implementation aspects. Preliminary evaluation shows that the method works as a discussion tool to uncover assumptions and make assessments to create a substantiated data driven business model.

Cultivating a data-driven culture: exploring the role of data analytic capability, employee empowerment, and transformational leadership.

INTELLI 2016 The Fifth International Conference on Intelligent Systems and Applications

Conference proceedings International Symposium on Intelligent Manufacturing Environments

Aviation Data Analytics in MRO Operations: Prospects and Pitfalls

As every new generation of civil aircraft creates more on-wing data and fleets gradually become more connected with the ground, an increased number of opportunities can be identified for more effective Maintenance, Repair and Overhaul (MRO) operations. Data are becoming a valuable asset for aircraft operators. Sensors measure and record thousands of parameters in increased sampling rates. However, data do not serve any purpose per se. It is the analysis that unleashes their value. Data analytics methods can be simple, making use of visualizations, or more complex, with the use of sophisticated statistics and Artificial Intelligence algorithms. Every problem needs to be approached with the most suitable and less complex method. In MRO operations, two major categories of on-wing data analytics problems can be identified. The first one requires the identification of patterns, which enable the classification and optimization of different maintenance and overhaul processes. The second category of problems requires the identification of rare events, such as the unexpected failure of parts. This cluster of problems relies on the detection of meaningful outliers in large data sets. Different Machine Learning methods can be suggested here, such as Isolation Forest and Logistic Regression. In general, the use of data analytics for maintenance or failure prediction is a scientific field with a great potentiality. Due to its complex nature, the opportunities for aviation Data Analytics in MRO operations are numerous. As MRO services focus increasingly in long term contracts, maintenance organizations with the right forecasting methods will have an advantage. Data accessibility and data quality are two key-factors. At the same time, numerous technical developments related to data transfer and data processing can be promising for the future.

Data science in government agencies

Abstract Despite the numerous business benefits of data science, the number of data science models in production is limited. Data science model deployment presents many challenges and many organisations have little model deployment knowledge. This research studied five model deployments in a Dutch government organisation. The study revealed that as a result of model deployment a data science subprocess is added into the target business process, the model itself can be adapted, model maintenance is incorporated in the model development process and a feedback loop is established between the target business process and the model development process. These model deployment effects and the related deployment challenges are different in strategic and operational target business processes. Based on these findings, guidelines are formulated which can form a basis for future principles how to successfully deploy data science models. Organisations can use these guidelines as suggestions to solve their own model deployment challenges.

Finding your feet

IntroductionInternational placements challenge students to find the right level of participation, as local practices, language and time pressure may affect their engagement in patient-related tasks or team activities. This study sought to unpack the initiation process during international clinical placements with the ultimate aim to achieve active student participation.MethodsFollowing a constructivist grounded theory approach, we conducted two individual interviews with 15 undergraduate healthcare students (before departure and whilst on placement). To identify emerging themes, we applied an iterative process of data collection and constant comparative analysis. Several team discussions informed further analysis, allowing us to reach a more conceptual level of theory.ResultsFrom our findings we constructed a four-phase model of healthcare students’ initiation of international clinical placements, which brings into focus how the phases of ‘orientation’, ‘adjustment’ and ‘contribution to patient care’ build up towards a ‘sense of belonging’. We identified several factors that induced active student participation in practice, such as a favourable workplace setting, opportunities for learning and a local support network.DiscussionActive student participation is aimed at different goals, depending on the four phases of initiation that eventually lead to a sense of belonging and support workplace learning.