Alternative Data News and Insight

The outbreak of coronavirus was a massive shock to an already fragile global economy – causing many markets to slip into recession earlier last month. Much about COVID-19 remains uncertain, however, alternative data can offer investors and policymakers greater clarity on its most damaging impacts. We outline the datasets that offer timely signals of the most affected areas of the global macroeconomy.

How much does alternative data cost? The million dollar question. We leverage our unique position in the industry and proprietary data on pricing to shed some light on how much datasets really cost as well as some past and expected future trends in pricing.

From layoffs to discussions of bailouts, it has been a turbulent few weeks for the global hospitality industry as a result of the COVID-19 pandemic. In this Intelligence report, we provide a deep dive into the various alternative datasets that can be used by investors to monitor the state of the industry, specifically the lodging, cruise, and restaurant sectors.

To aid our clients in navigating the unprecedented situation that the global economy now faces, we have collated a list of 80 free alternative data sources, over 90% of which are updated at weekly-or-higher frequencies. Highlights include human temperature mapping from smart thermometers, country-level internet pressure tracking, and global foot traffic data sourced from 128 countries.

Where to begin? As of March 11th, the world has been officially diagnosed with a pandemic and is now standing on the brink of a global recession. Here follows Neudata’s attempt to guide our clients through the most pertain alternative data products: from China’s recovery, to macro-level economic insights, supply chain analysis, healthcare and tourism sectors, and, finally, epidemiological data on the virus itself.

In times of uncertainty, indicators that measure economic activity and sentiment can commonly diverge, and the financial behaviours of businesses and consumers can be inconsistent with their economic expectations. In this report, we explore alternative data sources that we believe give a clearer picture of macroeconomic outcomes. We illustrate these examples in the context of Brexit and the global financial crisis – which highlight the difference that financial uncertainty and political uncertainty have on the macroeconomy.

Anyone interested in the global live streaming industry must study the China market, which is enormous in terms of both users and demographics. Chinese streaming companies occupy a leading position with regards to new streaming technology, gamification, monetization methods, and product design. In the following report, we provide an introduction to the market itself, as well as how investors can leverage alternative data to analyse the revenue and performance of different categories of live streaming and short video platforms.

The lack of industry consensus on whether ESG integration compromises on returns stems from the lack of uniformity across ESG ratings methodologies. In short - different ESG datasets result in differing financial outcomes. In this report, we summarize the academic studies, investment strategies and datasets that have been empirically proven to add alpha from ESG.

Datasets derived from the Internet of Things often lie on the fringe of the established alternative data universe. They are rarities or curiosities. In our IoT series, we shed light on the technologies that enable device connectivity, issues of data ownership, and alt data applications of IoT technology. In part one, we take a big picture approach by providing an introduction to IoT search engines.

Given the recent spate of dataset terminations, we thought it pertinent to share our insight on the topic of permanency risk. For an industry reliant on the sharing of data, it comes as no surprise that the ever-growing spotlight on data privacy has huge implications for dataset permanency risk. As the alternative data space becomes increasingly saturated, it’s also the case that lower quality datasets are getting filtered out of the market more quickly. So what do these developments mean for data buyers?