Hoe meer data er beschikbaar komt, des te meer de beslissing verbeterd kan worden. Hoe beter (slimmer) de beslissing wordt gemaakt, des te meer waarde de beslissing heeft voor uw organisatie. Dit wordt het data-netwerk-effect genoemd. Vaak wordt het data-netwerk-effect gerealiseerd door het gebruik van data van onbewuste data-donoren. In dit artikel wordt een ander soort data-donor belicht: de bewuste data-donor.
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A study of the improvement of the quality of student teachers’ lessons in interactive (story)book reading through the use of data-feedback on observed lessons. Variables regarding the optimal time use, the quality of instruction and the student teachers’ pedagogical relation with pupils were included in a one group pre-test post-test design.
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Abstract The Government of the Netherlands wants to be energy neutral by 2050 (Rijksoverheid, sd). A transition towards non-fossil energy sources also affects transport, which is one of the industries significantly contributing to CO2 emission (Centraal Bureau Statistiek, 2019). Road authorities at municipalities and provinces want a shift from fossil fuel-consuming to zero-emission transport choices by their inhabitants. For this the Province of Utrecht has data available. However, they struggle how to deploy data to positively influence inhabitants' mobility behavior. A problem analysis scoped the research and a survey revealed the gap between the province's current data-item approach that is infrastructure oriented and the required approach that adopts traveler’s personas to successfully stimulate cycling. For this more precisely defined captured data is needed and the focus should shift from already motivated cyclists to non-cyclers.
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Data is widely recognized as a potent catalyst for advancing healthcare effectiveness, increasing worker satisfaction, and mitigating healthcarecosts. The ongoing digital transformation within the healthcare sector promises to usher in a new era of flexible patient care, seamless inter-provider communication, and data-informed healthcare practices through the application of data science. However, more often than not data lacks interoperability across different healthcare institutions andare not readily available for analysis. This inability to share data leads to a higher administrative burden for healthcare providers and introduces risks when data is missing or when delays occur. Moreover, medical researchers face similar challenges in accessing medical data due to thedifficulty of extracting data from applications, a lack of standardization, and the required data transformations before it can be used for analysis. To address these complexities, a paradigm shift towards a data-centricapplication landscape is essential, where data serves as the bedrock of the healthcare infrastructure and is application agnostic.In short, a modern way to think about data in general is to go from an application driven landscape to a data driven landscape, which willallow for better interoperability and innovative healthcare solutions.
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During the past two decades the implementation and adoption of information technology has rapidly increased. As a consequence the way businesses operate has changed dramatically. For example, the amount of data has grown exponentially. Companies are looking for ways to use this data to add value to their business. This has implications for the manner in which (financial) governance needs to be organized. The main purpose of this study is to obtain insight in the changing role of controllers in order to add value to the business by means of data analytics. To answer the research question a literature study was performed to establish a theoretical foundation concerning data analytics and its potential use. Second, nineteen interviews were conducted with controllers, data scientists and academics in the financial domain. Thirdly, a focus group with experts was organized in which additional data were gathered. Based on the literature study and the participants responses it is clear that the challenge of the data explosion consist of converting data into information, knowledge and meaningful insights to support decision-making processes. Performing data analyses enables the controller to support rational decision making to complement the intuitive decision making by (senior) management. In this way, the controller has the opportunity to be in the lead of the information provision within an organization. However, controllers need to have more advanced data science and statistic competences to be able to provide management with effective analysis. Specifically, we found that an important skill regarding statistics is the visualization and communication of statistical analysis. This is needed for controllers in order to grow in their role as business partner..
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Productivity in construction is relatively low compared to other industries. This is particularly true for labour productivity. Problems that contribute to low labour productivity are often related to unorganised workspace, and inefficient organisation of work, materials and equipment. In terms of time use, site workers spend time on various activities including installing, waiting, walking etc. In lean production terms time use should be value adding and not wasteful or non-value adding. The study reported in this paper has endeavoured to measure the time use and movement applying an automated data system. The case study reflected a limited application to a specific kind of activity, namely doors installation. The study investigated time use and movements based on interviews and on automated detection of workforce. The interviews gave insights in the time build-up of work and value-added time use per day. The automated tracking indicated time intervals and uninterrupted presence of site workers on work locations giving indications of value adding time. The time measurements of the study enable comparison of time use categories of site workers. The study showed the data system calculated the same amounts of productive and value adding time one would expect based on the organisation and characteristics of the work. However, the discussion of the results underlined that the particular characteristics of individual projects and types of team work organisation may well have an impact on productivity levels of workers. More application and comparative studies of projects and further development and extension of the automated data system should be helpful.
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Recent years have seen a massive growth in ethical and legal frameworks to govern data science practices. Yet one of the core questions associated with ethical and legal frameworks is the extent to which they are implemented in practice. A particularly interesting case in this context comes to public officials, for whom higher standards typically exist. We are thus trying to understand how ethical and legal frameworks influence the everyday practices on data and algorithms of public sector data professionals. The following paper looks at two cases: public sector data professionals (1) at municipalities in the Netherlands and (2) at the Netherlands Police. We compare these two cases based on an analytical research framework we develop in this article to help understanding of everyday professional practices. We conclude that there is a wide gap between legal and ethical governance rules and the everyday practices.
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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.
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In this study data-feedback in a cyclic model of data-driven teaching was used to enhance the teaching behavior of students registered in a master course for teachers.
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In dit rapport wordt verslag uitgebracht van het onderzoek naar de resultaten van de toepassing van een cyclisch model voor observatie en data-gestuurde feedback.
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