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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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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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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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.
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Expectations are high for digital technologies to address sustainability related challenges. While research into such applications and the twin transformation is growing rapidly, insights in the actual daily practices of digital sustainability within organizations is lacking. This is problematic as the contributions of digital tools to sustainability goals gain shape in organizational practices. To bridge this gap, we develop a theoretical perspective on digital sustainability practices based on practice theory, with an emphasis on the concept of sociomateriality. We argue that connecting meanings related to sustainability with digital technologies is essential to establish beneficial practices. Next, we contend that the meaning of sustainability is contextspecific, which calls for a local meaning making process. Based on our theoretical exploration we develop an empirical research agenda.
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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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Trustworthy data-driven prognostics in gas turbine engines are crucial for safety, cost-efficiency, and sustainability. Accurate predictions depend on data quality, model accuracy, uncertainty estimation, and practical implementation. This work discusses data quality attributes to build trust using anonymized real-world engine data, focusing on traceability, completeness, and representativeness. A significant challenge is handling missing data, which introduces bias and affects training and predictions. The study compares the accuracy of predictions using Exhaust Gas Temperature (EGT) margin, a key health indicator, by keeping missing values, using KNN-imputation, and employing a Generalized Additive Model (GAM). Preliminary results indicate that while KNN-imputation can be useful for identifying general trends, it may not be as effective for specific predictions compared to GAM, which considers the context of missing data. The choice of method depends on the study’s objective: broad trend forecasting or specific event prediction, each requiring different approaches to manage missing data.
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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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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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Consistency issues limit the sharing of horticultural data across multiple systems, resulting in challenges for users to analyze data effectively across various systems utilizing artificial intelligence technology. Introducing data governance principles can help standardize and unify data practices, making it easier for analysts to locate, comprehend, transfer and integrate data from diverse sources to enable data-driven horticulture. Implementing data governance and principles specific to horticulture can assist in standardizing the layout and format of data structures from different sources. This study aims to propose a new governance framework, Horti-IoT, based on the Data Management Body of Knowledge and several structured frameworks for the Internet of Things (IoT) governance that will lead to data-driven horticulture. This study is empirical in nature. The Dutch horticulture stakeholders are involved in this initiative, providing the data, knowledge, and experiences needed for this study. The data stream from various sources, including camera images, sap flow sensors, climate sensors and manually measured growth data. The key findings following the implementation of the Horti-IoT framework’s principles are reduced workload for data analysts, efficiency in plant monitoring, savings time in pre-processing, enhanced water resource management, reduced system administrator contacts and compliance with General Data Privacy Regulation. The new proposed Horti-IoT framework, compatible with Dutch horticulture, is presented. The data were obtained from the Lab greenhouse at the World Horti Centre in the Netherlands, in the framework of the Regionale SIA RAAK MKB call March 2022-September 2024 subsidy funds for project title ‘Gewasgroei Goed Gemeten (GeGoGe). This project is a collaboration between three educational institutions. Inholland University of Applied Science, the Hague University of Applied Science, Lentiz Vocational School, and stakeholders.
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