Data Science in Economics and Finance for Decision Makers
First published:
ISBN: 978-1-78272-394-3
If you are a Risk.net subscriber you are entitled to 20% off your Risk books purchases. Please email enquiries@riskbooks.com for more information.
As part of your Risk.net subscription you are entitled to 20% off all of your Risk Books purchases. If you would like to place an order please email enquiries@riskbooks.com
Implementing big data solutions
Adrian Waddy
Implementing big data solutions
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
In this chapter we look at the topic of big data. In particular, we consider the rise of the phenomenon and talk about how an organisation can identify whether a big data approach to their data assets is likely to be beneficial. We shall consider the closely related but separate consideration of cloud-based computing and the options that encounter a decision maker at the outset of a big data project. We also discuss the benefits of standardising approaches to similar types of data assets and how we might store and model them depending on differing use cases, and examine the advantages and disadvantages of a monolithic big data platform approach compared with a multi-cluster one. And finally, we shall briefly note the importance of organising and staffing for a successful big data implementation.
In a few short years big data has gone from being a fringe subject, of interest to only international scale physics experiments11See https://home.cern/science/computing/storage. and Internet giants, to one that affects many businesses and organisations across nearly all industries. This has been precipitated by the rapid digitisation of data, the receding of digital storage as a
Copyright Infopro Digital Limited. All rights reserved.
As outlined in our terms and conditions, https://www.infopro-digital.com/terms-and-conditions/subscriptions/ (point 2.4), printing is limited to a single copy.
If you would like to purchase additional rights please email info@risk.net
Copyright Infopro Digital Limited. All rights reserved.
You may share this content using our article tools. As outlined in our terms and conditions, https://www.infopro-digital.com/terms-and-conditions/subscriptions/ (clause 2.4), an Authorised User may only make one copy of the materials for their own personal use. You must also comply with the restrictions in clause 2.5.
If you would like to purchase additional rights please email info@risk.net
