"The World of the [open] innovator" described the background of the revolution we are in in innovation and what the consequences are for innovation, changing towards design driven open innovation. We reframed innovation to meet new needs and values of companies and organizations in our work field. We do not take this light-hearted. We know the field of innovation and used our experience and conversation with stakeholders to come up with the insight of The [open] Innovator. What strengthened us were reactions from companies and organization we asked to cocreate or participate. There seemed to be an instant recognition and appeal to our vision and approach. But we also realize that we are in the stage of prototyping and we need you, as our lead users to be critical, yet to trust us. You, being an [open] innovator, will do great wonders, because you will be taught to deal with this uncertainty and dig in new, unknown situations or problems. You will learn the tools for research, for communication, for visualization. You will become a cooperative, open-minded problem solver. You will be able - with all the skills and tools we will provide you - to make the difference. But we need you to reflect upon your progress and needs; help us to get an insight in to your uncertainties, values and unmet needs, to enable us to improve our thinking and teaching. However, innovation can only be learned by doing! Start cracking, start experimenting, start having fun. Welcome to the future, that has just started.
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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.
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