Abstract Despite the numerous business benefits of data science, the number of data science models in production is limited. Data science model deployment presents many challenges and many organisations have little model deployment knowledge. This research studied five model deployments in a Dutch government organisation. The study revealed that as a result of model deployment a data science subprocess is added into the target business process, the model itself can be adapted, model maintenance is incorporated in the model development process and a feedback loop is established between the target business process and the model development process. These model deployment effects and the related deployment challenges are different in strategic and operational target business processes. Based on these findings, guidelines are formulated which can form a basis for future principles how to successfully deploy data science models. Organisations can use these guidelines as suggestions to solve their own model deployment challenges.
In dit rapport wordt verslag uitgebracht van het onderzoek naar de resultaten van de toepassing van een cyclisch model voor observatie en data-gestuurde feedback.
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..
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.
Professionals worden steeds vaker ondersteund door AI (Artificial Intelligence, kunstmatige intelligentie). Maar hoe ervaren professionals dat? Welke vorm van ondersteuning versterkt hun professie en wat willen ze vooral niet? In dit project onderzoeken we hoe verschillende rollen voor AI (besluitvormer, adviseur of kennisbron) worden ervaren door aankomend professionals in de preventieve zorg. Doel Krachtige samenwerking professional en AI Met het project willen we inzicht krijgen in welke invloed verschillende vormen van samenwerking met AI heeft op waarden als autonomie en vertrouwen bij professionals. Deze inzichten willen we vertalen naar vormen van samenwerking waarbij de kracht van zowel professional als AI optimaal tot uiting komt. Resultaten Het beoogde resultaat van het project is een set aan concrete richtlijnen voor het context-afhankelijk ontwerpen van mens-AI samenwerkingen die recht doen aan persoonlijke waarden. Looptijd 01 april 2021 - 31 maart 2022 Aanpak We onderzoeken verschillende rollen van AI door middel van Wizard of Oz experimenten. Hierin voeren studenten paramedische studies een preventieve gezondheidscheck uit met behulp van een gesimuleerd AI algoritme. De resulterende richtlijnen toetsen we in focusgroepen met zorg professionals. Relevantie voor beroepspraktijk Het gebruik van AI heeft grote potentie voor de beroepspraktijk. Er zijn echter ook zorgen over de impact van AI op de maatschappij. Met dit project dragen we bij aan een ethisch verantwoorde inzet van AI. Cofinanciering Dit project wordt uitgevoerd als onderdeel van het programma R-DAISES dat wordt uitgevoerd in het kader van NWA route 25 – verantwoorde waardecreatie met big data en is gefinancierd door NWO (Nederlandse Organisatie voor Wetenschappelijk Onderzoek)
