Randy Bean, CEO and managing partner of NewVantage Partners, published an article in HBR citing his company’s Big Data executive survey.
Robert Maxwell, finINTEL Solutions CEO, recently published a report (attached) highlighting the way datafication is aiding oil & gas analysts.
This document highlights issues such as copyright infringement, breach of contract, the Computer Fraud and Abuse Act, trespass to chattels, and ‘Hot news’ misappropriation have to be considered when undertaking data crawling operations.
According to the study conducted by McKinsey Global Institute, the US is going to face a shortage of around 190,000 data scientists and 1.5 million managers and analysts who can work with big data by 2018.
Asset managers are always looking for new sources of alpha. The data revolution is impacting many different industries and it is finally here to change the investment industry as well.
Users generating data with sensors are not likely to represent the population. Social media users are usually young and tech savvy representing rather a specific demographic group. Search data, on the other hand, can be used in econometric modelling more straightforwardly.
Data is all around us, and is growing in volume every day. The amount of data created every 2 days is as much as was created from the beginning of time until 2003. If we were to put 1 year’s worth of data on DVDs, the stack would go from Earth to the moon and back.
80% of respondents who participated in BrightLocal’s 2015 Local Consumer Review Survey admitted they would trust online reviews as much as personal recommendations. Lee, Park and Han’s study showed that purchase intention increases with the perceived credibility of consumer reviews.
Over 7,000 articles have been written on the subject of sentiment analysis, primarily in the last decade. The asset management community has been experimenting with the use of sentiment analysis, specifically in analyzing the polarity of sentiment expressed in social media and news media.
Asset managers use information derived from alternative data companies that specialize in analysing satellite imagery to assist in their estimation of revenues of companies in different sectors, including retail, oil and gas and construction.
In this paper, the authors show how to use search engine data to forecast near-term values of economic indicators. Examples include automobile sales, unemployment claims, travel destination planning and consumer confidence.
