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AbstractTraditional approaches to monitoring road safety have primarily focused on measuring outcomes such as the number of fatalities and injuries. While effective in capturing overall trends, this macroscopic approach often overlooks the underlying causes of unsafe conditions. Recognizing these limitations, many countries now embrace a safe system-based approach, which emphasizes a holistic view of road safety, considering various elements and their interactions. In response to this shift, this study introduces a five-step framework designed to provide comprehensive coverage and tailored assistance in selecting and utilizing appropriate Road Safety Indicators (RSIs) for more effective performance monitoring. The framework integrates a novel RSIs taxonomy aligned with critical elements of the safe system. It also incorporates an MCDA-based approach to account for decision-makers’ preferences when selecting suitable RSIs. A case study demonstrates the practical application of the proposed steps, including the identification, classification, selection, and development of descriptive sheets for each selected RSI, as well as the continuous updating of the RSIs set. The findings offered valuable insights into the commonly used indicators in international road safety reports, while also revealing the limitations of currents metrics and data in fully capturing critical elements and hierarchical level within the road safety management system. [ABSTRACT FROM AUTHOR]

Data-driven decision-making is a common approach for identifying child maltreatment. However, such strategies must be guided by ethical, equitable, and evaluative frameworks due to their potential for bias and error. This study analyzes the key features, potential challenges, and research evidence of a data-driven strategy known as birth match. Interviews with key informants across four states indicate that programs share features and objectives but that they differ regarding match criteria, data integrity processes, and responses to identified cases. Further, little outcome and equity evidence exists. Results emphasize the need for additional implementation and evaluation infrastructure to ensure transparency and effectiveness. [ABSTRACT FROM AUTHOR]

This paper interrogates the fundamental logic of data-driven decision-making (DDDM) as it has taken hold in education and argues for a critical analysis of data-driven education via an attitude of historical ontology. Though influenced by Foucault's understanding of this concept, I center Colin Koopman's recent analysis of the 'informational person' to point attention to the ways in which the very formatting of data may be understood as historically contingent and, thus, more contestable. After examining the background of DDDM and relevant critiques of it, I suggest that investigating the socio-historical formations of data itself are important for indicating its potential dangers within education. Such a critical analysis identifies the doubled politics of data formatting, with its ability to tether subjectivity to data, while potentially baking modes of power into a variety of technological data apparatuses. To conclude, I consider Koopman's work in terms of how it may extend critical studies of educational datafication. [ABSTRACT FROM AUTHOR]

The objective is to conceptualise data-driven and fact-based product portfolio management (PPM). The study examines how PPM process is internalised in companies and proposes a concept that covers all PPM performance management areas (strategic fit, value maximisation, and portfolio balance) to transform profitability analysis from company-level to product-level. The study is founded by focusing on PPM process and other key business processes, data-driven decision-making, company data assets, and business information technology (IT). The findings highlight how the strategic role of PPM process and related targets and key performance indicators must be internalised before adjusting business IT to utilise data assets for data-driven, fact-based PPM. The means for a data-driven approach are provided by the effective connection of the PPM process, company-widely governed data assets, and business IT systems to realise their full potential for fact-based decision-making over lifecycle. New contribution relates to introducing a technology-independent concept for data-driven, fact-based PPM. [ABSTRACT FROM AUTHOR]

Data-driven decision support for maintenance management is necessary for modern digitalized production systems. The data-driven approach enables analyzing the dynamic production system in real-time. Common problems within maintenance management are that maintenance decisions are experience-driven, narrow-focussed and static. Specifically, machine criticality assessment is a tool that is used in manufacturing companies to plan and prioritize maintenance activities. The maintenance problems are well exemplified by this tool in industrial practice. The tool is not trustworthy, seldom updated and focuses on individual machines. Therefore, this paper aims at the development and validation of a framework for a data-driven machine criticality assessment tool. The tool supports prioritization and planning of maintenance decisions with a clear goal of increasing productivity. Four empirical cases were studied by employing a multiple case study methodology. The framework provides guidelines for maintenance decision-making by combining the Manufacturing Execution System (MES) and Computerized Maintenance Management System (CMMS) data with a systems perspective. The results show that by employing data-driven decision support within the maintenance organization, it can truly enable modern digitalized production systems to achieve higher levels of productivity. [ABSTRACT FROM AUTHOR]

This research tests a hypothesized model that links collaborative data-use professional development experiences and successful use of student data to aid instruction. A serial, regression-based mediation model is tested (from professional development to self-efficacy, to positive affect, to successful use of data). Survey data from over 200 K–12 educators from across the United States were collected; the sample reflected a purposeful quota based on age and gender. The hypothesized model was supported. In all, the analyses demonstrate the importance of collaborative learning in professional development experiences related to use of student data. Professional development providers can use this knowledge to design more effective experiences. Similarly, school leaders and teacher coaches can use this knowledge to guide educators to experiences that are likely to result in the development of data-use skills to aid in classroom instruction. [ABSTRACT FROM AUTHOR]

When considering how to obtain funding for outreach services, GIS can be used to translate information about current outreach services and areas of need where services can have the most impact. Using ArcGIS Pro, census data, and data gathered by the Adult Literacy division of the Nashville Public Library, areas of impact for those at high poverty levels and without high school diplomas were compared to current library locations and locations currently served by the division. The maps show areas that present opportunities for more equity in services and can be presented to funders to garner interest in outreach services. [ABSTRACT FROM AUTHOR]

This paper analyzes the relationship between firms' use of big data analytics and their innovative performance in terms of product innovations. Since big data technologies provide new data information practices, they create novel decision-making possibilities, which are widely believed to support firms' innovation process. Applying German firm-level data within a knowledge production function framework we find suggestive evidence that big data analytics is a relevant determinant for the likelihood of a firm becoming a product innovator as well as for the market success of product innovations. These results hold for the manufacturing as well as for the service sector but are contingent on firms' investment in IT-specific skills. Overall, the results support the view that big data analytics have the potential to enable innovation. [ABSTRACT FROM AUTHOR]

This paper reports on qualitative research from an Australian, K-6, faith-based school about teachers’ experience of a job-embedded professional learning initiative (the Whole-School Benchmarking of Writing, WSBoW) that was designed to improve teachers’ capacity to apply data-driven decision-making to achieve improvements in student learning. Teacher interviews provided the data about experiences and influences on teching practice. The findings demonstrated that the professional learning initiative provided teachers with a supportive pedagogical tool for focusing their teaching practices on improving student learning. A research based evaluative framework was used as a heuristic to analyse the change initiative. This study used the framework and employed a social constructivist and sociocultural epistemology that recognised the importance of leadership and school context in the development of a culture focused on collaborative communities of practice and data-driven teacher decision-making. The study has significance for leaders, schools and systems that engage teachers in professional capacity building. [ABSTRACT FROM AUTHOR]

Formative assessment and effective use of data to support student learning are key components of practice in early childhood (EC) centres. Although Trinidad and Tobago has developed an impressive EC care system, to decrease the achievement gap which international studies have shown to be significant in core subjects, educational planners must ensure that teachers make better use of data. This study explored data use practice in eight geographically diverse EC centres. Using maximum variation sampling, data were collected from focus group teacher interviews, observation and document analysis. Thematic analysis and constant comparison were used to identify differences across sites and to generate theory on practice. The theory identifies data use practice in an emerging Caribbean society lacking a robust data culture and with limited professional development opportunities. Implications for policy, professional sdevelopment and system reform of the EC sector in Trinidad and Tobago are included. [ABSTRACT FROM AUTHOR]

Data play a crucial role in health research, education, and patient care. Initiatives such as Big Data to Knowledge (BD2K) and Precision Medicine point to the increasing focus and value of data use and reuse. As the universe of data continues to grow exponentially, health education needs to provide students with practical hands-on learning sessions that illustrate the multitude of uses for data and statistics. Giving students early exposure to data reuse provides a solid background for the more complex data they may gather later in their education. This column describes how librarians at a general academic library used data dashboards and Tableau in two library sessions to provide students with hands-on experience in data visualization and its role in decision-making. [ABSTRACT FROM PUBLISHER]

The purpose of this article is to improve our understanding of data-driven decision-making strategies that are initiated at the district or system level. We apply principal-agent theory to the analysis of qualitative data gathered in a case study of 4 urban school systems. Our findings suggest educators at the school level need not only systemic support but also enough decision-making autonomy to make site-level decisions on the basis of data. Secondly, we found that building expertise and capacity at the school site for data-driven decision-making is necessary but not a sufficient condition for success. Finally, in designing an accountability system, the imbalance in the distribution of information between the central office and the schools must be accommodated. Implications for further research and policy, based on these findings, are also discussed. [ABSTRACT FROM AUTHOR]