Whitepaper in een serie over HR Analytics Elke organisatie neemt voortdurend HRM-beslissingen, zoals over het aannamebeleid, de beloning en talentmanagement. Wanneer ze daarbij gebruik maken van predictive analytics, oftewel voorspellende analyse, kunnen organisaties de kans berekenen dat een individuele medewerker bepaald gedrag gaat vertonen. Voorspellende analyse leidt daardoor tot betere besluiten en maakt het mogelijk om gericht actie te ondernemen. Inhoud: • Inleiding 1. Zorg voor kwalitatief hoogwaardige data 2. Maak de stap van dataverzameling naar rapportage 3. Ontwikkel voorspellende modellen op basis van historische data 4. Gebruik voorspellende modellen om inzicht te krijgen 5. Baseer HRM-maatregelen op inzichten uit voorspellende analyses • Conclusie
Whitepaper in een serie over HR Analytics. Steeds vaker worden HRM-beslissingen gebaseerd op voorspellende modellen die ontwikkeld zijn op basis van historische data. In deze whitepaper bespreken we een aantal best practices die organisaties daarbij kunnen helpen. Zo is het belangrijk om goed te letten op de oorsprong van gegevens. Objectieve meetgegevens zijn bijvoorbeeld vaak van grotere waarde dan subjectieve antwoorden uit enquêtes. Wanneer een organisatie data wil verzamelen voor een People Analytics-project, is het daarnaast belangrijk om zeker te weten dat er meetinstrumenten worden gekozen die ook echt meten wat ze beogen te meten. Inhoud: • Inleiding 1. Kies de juiste steekproef 2. Let op de grootte van de steekproef 3. Geef de voorkeur aan objectieve gegevens 4. Zorg voor valide meetinstrumenten 5. Koppel data op een privacyvriendelijke manier 6. Denk na over het gebruik van gemiddelden 7. Verwar oorzaak en gevolg niet 8. Laat je niet foppen door percentages 9. Let op verklaarde variantie 10. Kijk altijd naar de netto opbrengst 11. Voer waar nodig extra analyses uit 12. Maak voldoende tijd vrij voor Analytics • Conclusie
Although learning analytics benefit learning, its uptake by higher educational institutions remains low. Adopting learning analytics is a complex undertaking, and higher educational institutions lack insight into how to build organizational capabilities to successfully adopt learning analytics at scale. This paper describes the ex-post evaluation of a capability model for learning analytics via a mixed-method approach. The model intends to help practitioners such as program managers, policymakers, and senior management by providing them a comprehensive overview of necessary capabilities and their operationalization. Qualitative data were collected during pluralistic walk-throughs with 26 participants at five educational institutions and a group discussion with seven learning analytics experts. Quantitative data about the model’s perceived usefulness and ease-of-use was collected via a survey (n = 23). The study’s outcomes show that the model helps practitioners to plan learning analytics adoption at their higher educational institutions. The study also shows the applicability of pluralistic walk-throughs as a method for ex-post evaluation of Design Science Research artefacts.
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.
