Verslag van een presentatie. In onderzoeken naar de prioriteiten van HR-professionals staan analytics dan ook steevast onderaan het prioriteitenlijstje. Echter, nu elke dag meer data beschikbaar komen en alles is te meten, is dit niet langer een houdbaar standpunt. HR-professionals zullen op zijn minst moeten beseffen dat data waardevol zijn. Een Engelstalige definitie van People Analytics luidt: ‘The systematic identification and quantification of the people drivers of business outcomes, with the purpose of making better decisions.‘ Daarbij is het belangrijk om een goede businessvraag te stellen én – vervolgens –de resultaten van de analyse op overtuigende wijze over te brengen.
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Interview met Sjoerd van den Heuvel. HR-professionals willen meer doen met data, maar zij missen daarvoor de kennis en vaardigheden. Dat blijkt uit onderzoek dat de Hogeschool Utrecht heeft uitgevoerd onder leden van AWVN. Hoe tilt u people analytics in uw organisatie naar een hoger plan?
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
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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.
