Data has been collected since mankind, but in the recent years the technical innovations enable us to collect exponentially growing amounts of data through the use of sensors, smart devices and other sources. In her lecture Nanda will explore the role of Big Data in urban environments. She will give an introduction to the world of Big Data and Smart Cities, and an assessment of the role that data analytics plays in the current state of the digital transformation in our cities. Examples are given in the field of energy and mobility.
Ook binnen het human capital domein van organisaties wordt data-analyse steeds meer ingezet ten behoeve van evidence based besluitvorming, op zowel operationeel-, tactisch-, als strategisch niveau. Geïnspireerd door succesverhalen van organisaties die vele tientallen miljoenen aan besparingen hebben gerealiseerd, en tegelijkertijd de productiviteit en bevlogenheid van medewerkers hebben verbeterd, wordt People Analytics mainstream. Mede doordat de human capital kosten in organisaties (bv. recruitment, salaris, training, ziekteverzuim) gemiddeld ongeveer 60% van de totale organisatiekosten omvatten, is de potentiele invloed van People Analytics op het succes van organisaties aanzienlijk15. Bovendien is het human capital domein traditioneel een terrein waar veel data worden vastgelegd, denk bijvoorbeeld aan functionerings- en beoordelingsdata, data over trainingen en opleidingen, en salarisgegevens. Daarnaast zijn er buiten de organisatiegrenzen steeds meer social mediadata over potentiele medewerkers beschikbaar, die – uiteraard binnen de wettelijke en ethische kaders – gebruikt kunnen worden voor onder andere arbeidsmarktanalyse
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.
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