Data Science in Economics and Finance for Decision Makers
First published:
ISBN: 978-1-78272-394-3
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Data visualization: developing capabilities to make decisions and communicate
Valérie Michèle Saintot
Foreword
Introduction
Digitalisation and transformation in economics and finance
Big data for policymaking in economics and finance: the potential and challenges
Quality matters: for insightful quality advice, get to know your big data
Statistics and machine learning: variations on a theme
Advanced statistical analysis of large-scale Web-based data
Text analysis
Prudential stress testing in financial networks
Data visualization: developing capabilities to make decisions and communicate
Data science in economics and finance: tools, infrastructure and challenges
Data science and machine learning for a data-driven central bank
Large-scale commercial data for economic analysis
Artificial intelligence and data are transforming the modern newsroom: a Bloomberg case study
Implementing big data solutions
A borderless market for digital data
Legal/ethical aspects and privacy: enabling free data flows
Assessing the trustworthiness of artificial intelligence
“Big tech”, journalism and the future of knowledge
This chapter focuses on the communication dimensions related to data visualization for both decision-making and outreach purposes. It emphasises why visualization matters, what matters when working with data visualization and how to make data visualization matter even more. It combines well-established good practices in the domain of data visualization with lessons learned from concrete insights stemming from the author’s practice in the field. The iterative nature of the visualization process and the fact that multidisciplinary teams working on data visualization add significant value to the process and the outcome are also underlined. The chapter concludes with the importance of using the power of data, information and knowledge visualization to mobilise attention to challenge and transform society in a world where volatility, uncertainty, complexity and ambiguity are common currencies.
INTRODUCTION
Reflecting on data visualization goes beyond designing infographics (Engbretsen and Kennedy 2020). The field has reached a stage of maturity that requires us to consider a number of more philosophical, transformational and ethical questions. Visualized data can change
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