Deze whitepaper is een vervolg op deze eerdere reeks over People Analytics en bespreekt de nieuwste trends. Inhoud: • Inleiding 1. Medewerkerswelzijn krijgt meer aandacht 2. HR Analytics wordt People Analytics 3. Het inzicht groeit dat People Analytics geen kant-en-klare oplossingen levert 4. De kloof tussen de vaardigheden en ambities wordt minder groot 5. Analyticsteams herbergen steeds meer expertise 6. Meer data worden gekwantificeerd 7. Steeds meer data worden van buiten de organisatie betrokken 8. Kunstmatige intelligentie kan voor onverwachte inzichten zorgen 9. Het aantal interne databronnen neemt toe 10. Er komt meer aandacht voor privacybescherming • Conclusie
Despite the promises of learning analytics and the existence of several learning analytics implementation frameworks, the large-scale adoption of learning analytics within higher educational institutions remains low. Extant frameworks either focus on a specific element of learning analytics implementation, for example, policy or privacy, or lack operationalization of the organizational capabilities necessary for successful deployment. Therefore, this literature review addresses the research question “What capabilities for the successful adoption of learning analytics can be identified in existing literature on big data analytics, business analytics, and learning analytics?” Our research is grounded in resource-based view theory and we extend the scope beyond the field of learning analytics and include capability frameworks for the more mature research fields of big data analytics and business analytics. This paper’s contribution is twofold: 1) it provides a literature review on known capabilities for big data analytics, business analytics, and learning analytics and 2) it introduces a capability model to support the implementation and uptake of learning analytics. During our study, we identified and analyzed 15 key studies. By synthesizing the results, we found 34 organizational capabilities important to the adoption of analytical activities within an institution and provide 461 ways to operationalize these capabilities. Five categories of capabilities can be distinguished – Data, Management, People, Technology, and Privacy & Ethics. Capabilities presently absent from existing learning analytics frameworks concern sourcing and integration, market, knowledge, training, automation, and connectivity. Based on the results of the review, we present the Learning Analytics Capability Model: a model that provides senior management and policymakers with concrete operationalizations to build the necessary capabilities for successful learning analytics adoption.
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Hoofdstuk 10 in HRM Heden en Morgen. Dit hoofdstuk is geschreven vanuit de overtuiging dat een gemeenschappelijke taal en begrip van people analytics, evenals enkele basale wetenschappelijke principes waarop het gestoeld is, het jonge vakgebied in de praktijk naar een hoger niveau kunnen tillen. En daarmee de (toekomstige) HRM-professionals werkzaam op en rondom dit uitdagende thema in staat kunnen stellen (nog meer) impact te maken in hun organisatie. Het primaire doel van dit hoofdstuk is om de (toekomstige) professional die dit leest, aan het denken te zetten. Dit kan betekenen inspireren, verwarren, of duiden. Maar ook aanzetten tot het concreet aan de slag gaan met people analytics in de eigen organisatie, op de grens van wetenschap en praktijk, because that’s where the magic happens.
Digital transformation has been recognized for its potential to contribute to sustainability goals. It requires companies to develop their Data Analytic Capability (DAC), defined as their ability to collect, manage and analyze data effectively. Despite the governmental efforts to promote digitalization, there seems to be a knowledge gap on how to proceed, with 37% of Dutch SMEs reporting a lack of knowledge, and 33% reporting a lack of support in developing DAC. Participants in the interviews that we organized preparing this proposal indicated a need for guidance on how to develop DAC within their organization given their unique context (e.g. age and experience of the workforce, presence of legacy systems, high daily workload, lack of knowledge of digitalization). While a lot of attention has been given to the technological aspects of DAC, the people, process, and organizational culture aspects are as important, requiring a comprehensive approach and thus a bundling of knowledge from different expertise. Therefore, the objective of this KIEM proposal is to identify organizational enablers and inhibitors of DAC through a series of interviews and case studies, and use these to formulate a preliminary roadmap to DAC. From a structure perspective, the objective of the KIEM proposal will be to explore and solidify the partnership between Breda University of Applied Sciences (BUas), Avans University of Applied Sciences (Avans), Logistics Community Brabant (LCB), van Berkel Logistics BV, Smink Group BV, and iValueImprovement BV. This partnership will be used to develop the preliminary roadmap and pre-test it using action methodology. The action research protocol and preliminary roadmap thereby developed in this KIEM project will form the basis for a subsequent RAAK proposal.
Digital transformation has been recognized for its potential to contribute to sustainability goals. It requires companies to develop their Data Analytic Capability (DAC), defined as their ability to manage and analyze data effectively. Despite the governmental efforts to promote digitalization, there seems to be a knowledge gap on how to proceed, with 37% of Dutch SMEs reporting a lack of knowledge, and 33% reporting a lack of support in developing DAC. While extensive attention has been given to the technological aspects of DAC, the people, process, and organizational culture aspects are as important, requiring a comprehensive approach and thus a bundling of knowledge from different expertise. Therefore, the objective of this KIEM proposal is to identify organizational enablers and inhibitors of DAC through a series of interviews and case studies, and use these to formulate a preliminary roadmap to DAC.
Globalization has opened new markets to Small and Medium Enterprise (SMEs) and given them access to better suppliers. However, the resulting lengthening of supply chains has increased their vulnerability to disruptions. SMEs now recognize the importance of reliable and resilient supply chains to meet customer requirements and gain competitive advantage. Data analytics play a crucial role in developing the insights needed to identify and deal with disruptions. At the company level, this entails the development of data analytic capability, a complex socio-technical process consisting of people, technology, and processes. At the supply chain level, the complexity is compounded by the fact that multiple actors are involved, each with their own resources and capabilities. Each company’s data analytic capability, in combination with how they work together to share information and thus create visibility in the supply chain will affect the reliability and resilience of the supply chain. The proposed study therefore examines how SMEs can leverage data analytics in a way that fits with their available resources and capabilities to improve the reliability and resilience of their supply chain. The consortium for this project consists of Breda University of Applied Sciences (BUas), Logistics Community Brabant (LCB), Transport en Logistiek Nederland (TLN), Logistiek Digitaal, Kennis Transport, Smink and Devoteam. Together, the partners will develop a tool to benchmark SMEs’ progress towards developing data analytic capability that enhances the reliability of their supply chain. Interviews will be conducted with various actors of the supply chain to identify the enablers and inhibitors of using data analytics across the supply chain. Finally, the findings will be used to conduct action research with the two SMEs partners, Kennis and Smink to identify which technological tools and processes companies need to adopt to develop the use of data analytics to enhance their resilience in case of disruptions.
