Alternative Data and Machine Learning

Alternative Data and Machine Learning

The landscape of data is ever-changing, meaning analysts need to evolve both their thinking and data collection methods to stay ahead of the curve. In many cases, data that might have been considered unique, uncommon or unattainably expensive just a few years ago is now widely used and often very affordable. It is the analysts who take advantage of these untapped data sources, while they remain untapped, who can reap the rewards by gaining a competitive advantage before the rest of their industry or peers catch on.

This type of data is often referred to asalternative data, and with the ever-increasing levels of data available in the modern world comes the opportunity to gain unique insights, competitive industry advantage, and boosted profits. It is perhaps no surprise then to hear that the scramble to get hold of such data has been dubbed thenew gold rush.

With so many of our customers here at AYLIEN using ourText AnalysisandNews APIsto source and analyze alternative data in the form of unstructured content, we thought we would take a look at this trend to give you an idea of how and why it is becoming so popular and important.

Alternative data can be described as data that has been derived from non-traditional sources. Data that can be used to complement traditional data sources to produce improved analytical insights that would otherwise not have been achievable with traditional data alone.

Put simply, it’s data that isn’t commonly being used within a specific industry or use case, but can potentially be used to gain a competitive advantage over those that do not have access to it.

Let’s look at investors as an example. Ask any investor what data source they could not do without and they’ll most likely say it’s their Bloomberg terminal, or a similar device. Bloomberg’s data services enable investors to easily scan financial data that has been generated by thousands of companies. The ubiquity of terminals such as Bloomberg’s means that every investor requires one to be successful. However, it therefore also makes it difficult for investors to gain any sort of competitive advantage, seeing as they’re all receiving the same data, at the same time.

So, how is alternative data being sourced and utilized?

To give you an idea of just how significant alternative data can be, and the seemingly endless channels from which it can be sourced, we’re going to look at recent examples involving three brands; Chipotle, GoPro and JCPenney.

The CEO of Foursquare, which is a search-and-discovery service mobile app, predicted a30% drop in Q1 salesfor Chipotle, based on footfall data accumulated by their app users. Foursquare could see a drop in customer footfall based on a decrease in their users ‘checking-in’ at Chipotle restaurants, and they were correct.

In November of last year, Wall Street was shocked by the news that GoPro had reported a loss of 60 cents per share on $240.56 million in revenue, after analysts had predicted a far less severe loss of 36 cents per share on $314.06 million in revenue.

While Wall Street never saw this coming, Quandl, a financial data provider were able to foresee the drop in GoPro earnings. In this case, the data came fromelectronic receipts extracted from over 3 million inboxes. A significant decline in email sales receipts from GoPro’s biggest distribution channel, Amazon, was a key indicator of what was to come.

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