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.
LINK
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.
MULTIFILE
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.
MULTIFILE
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.
MULTIFILE
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.
DOCUMENT
DOCUMENT
IntroductionThe growing availability of data offers plenty of opportunities for data-driven innovation of business models. This certainly applies to interactive mediacompanies. Interactive media companies are engaged in the development, provisioning, and exploitation of interactive media services and applications.Through the service interactions, they may collect large amounts of data which can be used to enhance applications or even define new propositions and business models. According to Lippell (2016), media companies can publish content in more sophisticated ways. They can build a deeper and more engaging customer relationship based on a deeper understanding of their users. Indeed, research from Weill & Woerner (2015) suggests that companies involved in the digitalecosystem that better understand their customers than their average competitor have significantly higher profit margins than their industry averages. Moreover, the same research suggests that businesses need to think more broadly about their position in the ecosystem. Open innovation and collaboration are essential for new growth, for example combining data within and across industries (Parmar et al., 2014). However, according to (Mathis and Köbler, 2016), these opportunities remain largely untapped as especially SMEs lack the knowledge and processes to translate data into attractive propositions and design viable data driven business models (DDBM). In this paper, we investigate how interactive media companies can structurally gain more insight and value from data and how they can develop DDBM. We define a DDBM as a business model relying on data as a key resource (Hartmann et al., 2016).
DOCUMENT
In projects concerning big data, ethical questions need to be answered during the design process. In this paper the Value Sensitive Design method is applied in the context of data-driven health services aimed at disease prevention. It shows how Value Sensitive Design, with the use of a moral dialogue and an ethical matrix, can support the identifcation and operationalization of moral values that are at stake in the design of such services. It also shows that using this method can support meeting the requirements of the General Data Protection Regulation.
LINK
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
MULTIFILE
The number of Electric Vehicles (EVs) is expected to increase exponentially in the coming years. The growing presence of charging points generates a multitude of interactions between EV users, particularly in metropolitan areas where a charging infrastructure is largely part of the public domain. There is a current knowledge gap as to how current decisions on charging infrastructure deployment affect both current and future infrastructure performance. In the thesis an attempt is made to bridge this knowledge gap by creating a deeper understanding of the relation between charging behavior, charging infrastructure deployment, and performance.The results demonstrate shown how both strategic and demand-drive deployment strategies have an effect on performance metrics. In a case study in the Netherlands it was found that during the initial deployment phase, strategic Charging Points (CPs) facilitate EV users better than demand driven deployment. As EV user adoption increased, demand-driven CPs show to outperform strategic CPs.This thesis further shows that there are 9 EV user types each with distinct difference distinct behavior in terms of charging frequency and mean energy uptake, both of which relate to aggregate CP performance and that user type composition, interactions between users and battery size play an important role in explaining performance of charging infrastructure.A validated data-driven agent-based model was developed to explore effects of interactions in the EV system and how they influence performance. The simulation results demonstrate that there is a non-linear relation between system utilization and inconvenience even at the base case scenario. Also, a significant rise of EV user population will lead to an occupancy of non-habitual charging at the expense of habitual EV users, which leads to an expected decline of occupancy for habitual EV users.Additional simulations studies support the hypothesis that several Complex Systems properties are currently present and affecting the relation between performance and occupation.
DOCUMENT
