During the COVID-19 pandemic, the bidirectional relationship between policy and data reliability has been a challenge for researchers of the local municipal health services. Policy decisions on population specific test locations and selective registration of negative test results led to population differences in data quality. This hampered the calculation of reliable population specific infection rates needed to develop proper data driven public health policy. https://doi.org/10.1007/s12508-023-00377-y
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Full tekst beschikbaar voor gebruikers van Linkedin. Driven by technological innovations such as cloud and mobile computing, big data, artificial intelligence, sensors, intelligent manufacturing, robots and drones, the foundations of organizations and sectors are changing rapidly. Many organizations do not yet have the skills needed to generate insights from data and to use data effectively. The success of analytics in an organization is not only determined by data scientists, but by cross-functional teams consisting of data engineers, data architects, data visualization experts, and ("perhaps most important"), Analytics Translators.
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The report from Inholland University is dedicated to the impacts of data-driven practices on non-journalistic media production and creative industries. It explores trends, showcases advancements, and highlights opportunities and threats in this dynamic landscape. Examining various stakeholders' perspectives provides actionable insights for navigating challenges and leveraging opportunities. Through curated showcases and analyses, the report underscores the transformative potential of data-driven work while addressing concerns such as copyright issues and AI's role in replacing human artists. The findings culminate in a comprehensive overview that guides informed decision-making in the creative industry.
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ackground and aim – Driven by new technologies and societal challenges, futureproof facility managers must enable sustainable housing by combining bricks and bytes into future-proof business support and workplace concepts. The Hague University of Applied Sciences (THUAS) acknowledges the urgency of educating students about this new reality. As part of a large-scale two-year study into sustainable business operations, a living lab has been created as a creative space on the campus of THUAS where (novel) business activities and future-proof workplace concepts are tested. The aim is to gain a better understanding amongst students, lecturers, and the university housing department of bricks, bytes, behavior, and business support. Results – Based on different focal points the outcomes of this research present guidelines for facility managers how data-driven facility management creates value and a better understanding of sustainable business operations. In addition, this practice based research presents how higher education in terms of taking the next step in creating digitized skilled facility professionals can add value to their curriculum. Practical or social implications – The facility management profession has an important role to play in the mitigation of sustainable and digitized business operations. However, implementing high-end technology within the workplace can help to create a sustainable work environment and better use of the workplace. These developments will result in a better understanding of sustainable business operations and future-proof capabilities. A living lab is the opportunity to teach students to work with big data and provides a playground for them to test their circular workplace, business support designs, and smart building technologies.
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Current research on data in policy has primarily focused on street-level bureaucrats, neglecting the changes in the work of policy advisors. This research fills this gap by presenting an explorative theoretical understanding of the integration of data, local knowledge and professional expertise in the work of policy advisors. The theoretical perspective we develop builds upon Vickers’s (1995, The Art of Judgment: A Study of Policy Making, Centenary Edition, SAGE) judgments in policymaking. Empirically, we present a case study of a Dutch law enforcement network for preventing and reducing organized crime. Based on interviews, observations, and documents collected in a 13-month ethnographic fieldwork period, we study how policy advisors within this network make their judgments. In contrast with the idea of data as a rationalizing force, our study reveals that how data sources are selected and analyzed for judgments is very much shaped by the existing local and expert knowledge of policy advisors. The weight given to data is highly situational: we found that policy advisors welcome data in scoping the policy issue, but for judgments more closely connected to actual policy interventions, data are given limited value.
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From the article: Though organizations are increasingly aware that the huge amounts of digital data that are being generated, both inside and outside the organization, offer many opportunities for service innovation, realizing the promise of big data is often not straightforward. Organizations are faced with many challenges, such as regulatory requirements, data collection issues, data analysis issues, and even ideation. In practice, many approaches can be used to develop new datadriven services. In this paper we present a first step in defining a process for assembling data-driven service development methods and techniques that are tuned to the context in which the service is developed. Our approach is based on the situational method engineering approach, tuning it to the context of datadriven service development. Published in: Reinhartz-Berger I., Zdravkovic J., Gulden J., Schmidt R. (eds) Enterprise, Business-Process and Information Systems Modeling. BPMDS 2019, EMMSAD 2019. Lecture Notes in Business Information Processing, vol 352. Springer. The final authenticated version of this paper is available online at https://doi.org/10.1007/978-3-030-20618-5_11.
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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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Urban experimentation has gained traction with (supra-)national and local politics as a method for catalyzing change in urban systems and practices. Yet, with experiments becoming more commonly driven by established actors, concerns persist about their potential to sidestep political issues of power, exclusion and conflict fundamental to societal change. This paper seeks to unpack what exactly is at stake when the political is ignored or neutralized during an urban experiment. Using theories on the political as an analytical lens, the paper presents a case study of an urban experiment in Amsterdam, dissecting the ways in which (de)politicization operates in the experiment. The findings demonstrate that ignoring the political in urban experimentation risks excluding certain voices and options from being considered, which ultimately leads to stagnation. The paper concludes by outlining future challenges for research and practice that addresses (de)politicization in urban experiments.
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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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