De meest gebruikte opbouw in business intelligence, predictive analitics en analytics modellen is de moeilijkheidsgraad: 1) descriptive, 2) diagnostic, 3) predictive en 4) prescriptive. Deze schaal vertelt iets over de volwassenheid van het gebruik van data door de organisatie. Een model dat niet op zichzelf staat en een achterliggende methode kent is de data driehoek van EDM (Figuur 1), welke in dit artikel zal worden toegelicht.
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Over the past few years, there has been an explosion of data science as a profession and an academic field. The increasing impact and societal relevance of data science is accompanied by important questions that reflect this development: how can data science become more responsible and accountable while also responding to key challenges such as bias, fairness, and transparency in a rigorous and systematic manner? This Patterns special collection has brought together research and perspective from academia, the public and the private sector, showcasing original research articles and perspectives pertaining to responsible and accountable data science.
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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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This chapter explores the legal and moral implications of the use of data science in criminal justice at two levels: police surveillance and the criminal trial of a defendant. At the first level, police surveillance, data science is used to identify places and people at high risk of criminal activity, allowing police officers to target surveillance and take proactive measures to try to prevent crime (predictive policing). At the second level, the criminal trial of a defendant, data science is used to make risk assessments to support decisions about bail, sentencing, probation, and supervision and detention orders for high-risk offenders. The use of data science at these levels has one thing in common: it is about predicting risk. The uncertainty associated with risk prediction raises specific related legal and ethical dilemmas, for example in the areas of reasonable suspicion, presumption of innocence, privacy, and the principle of non-discrimination.
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This video offers a concise exploration of the distinctions between Data Science, AI, Machine Learning, and Deep Learning. Starting with the foundational role of Data Science, it navigates through the various machine learning categories and touches upon the capabilities and constraints of Deep Learning. The discussion culminates in understanding the nuances of AI, differentiating between narrow and general AI. Through insightful examples, viewers are guided on selecting the right technique for specific projects, ensuring both clarity and cost-effectiveness in the realm of data science.
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In de agro-chemie industrie wordt tegenwoordig een grote hoeveelheid data gegenereerd. De inzet van sensoren voor het monitoren van productieprocessen, het sequensen van gewassen en het karakteriseren van bodemmonsters zijn voorbeelden van activiteiten die veel data opleveren. Tegelijkertijd dalen de kosten voor dataopslag en -verwerking sterk. Bedrijven die hier gebruik van weten te maken, hebben goud in handen.
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Terms like ‘big data’, ‘data science’, and ‘data visualisation’ have become buzzwords in recent years and are increasingly intertwined with journalism. Data visualisation may further blur the lines between science communication and graphic design. Our study is situated in these overlaps to compare the design of data visualisations in science news stories across four online news media platforms in South Africa and the United States. Our study contributes to an understanding of how well-considered data visualisations are tools for effective storytelling, and offers practical recommendations for using data visualisation in science communication efforts.
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In de accountancybranche heersen een aantal kenmerken die ervoor hebben gezorgd dat de adoptie van automatisering, digitalisering en data-analyse achterloopt. Dit heeft een aantal redenen: door de hoge werkdruk is er soms geen kans om te innoveren; de omzet is toereikend, waardoor de nut en noodzaak niet worden gezien; door het grote personeelstekort is er geen personeel voor een innovatietraject; MKB-accountants vinden het te risicovol om te investeren in digitalisering met het oog op pensionering en de verkoop van het eigen accountantskantoor. Onderzoekers van de Hogeschool van Amsterdam, Hogeschool Rotterdam en Hogeschool Utrecht hebben onderzocht hoe de mkb-accountant data-analyses kan inzetten in zijn beroepspraktijk, zodat beter aan de wensen van zijn mkb-klanten op het gebied van performance en directere sturing wordt voldaan, en de bedrijfsvoering en werkprocessen van de klanten efficiënter worden. De digitalisering biedt de accountant namelijk mogelijkheden om mkb-ondernemers op basis van data nog beter te adviseren. Het kan daarbij om financiële zaken gaan, maar ook over andere zaken die bij ondernemerschap horen. Denk bijvoorbeeld aan adviezen over voorraden, personeelszaken, procesverbeteringen, verkoopcijfers, duurzaamheid, etc. Op 6 oktober 2022 heeft de onderzoeksgroep de eindresultaten van het onderzoeksproject gepresenteerd op het mini-congres “Data science en mkb-accountants” bij de NBA in Amsterdam. Eén van de tools die het projectteam voor de mkb-accountantskantoren heeft ontwikkeld is een Data Analyse Protocol (hierna DAP). Het DAP geeft de accountant inzicht in vragen die bij mkb-ondernemers kunnen leven en waarbij de accountant kan helpen deze vragen te beantwoorden
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Recent years have seen a massive growth in ethical and legal frameworks to govern data science practices. Yet one of the core questions associated with ethical and legal frameworks is the extent to which they are implemented in practice. A particularly interesting case in this context comes to public officials, for whom higher standards typically exist. We are thus trying to understand how ethical and legal frameworks influence the everyday practices on data and algorithms of public sector data professionals. The following paper looks at two cases: public sector data professionals (1) at municipalities in the Netherlands and (2) at the Netherlands Police. We compare these two cases based on an analytical research framework we develop in this article to help understanding of everyday professional practices. We conclude that there is a wide gap between legal and ethical governance rules and the everyday practices.
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