Hi there. It's Ekku from Datarade. Welcome to Data explained.

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Today we're going to talk about alternative data. We felt the need to explain this data type a bit further as it is quite unusual.

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It can be essentially sourced from a variety of different unexpected sources.

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To explain what it is; it can be essentially any type of data. It just needs to be unexplored, to be considered non-traditional and unrecognized data, that still has potential for its specific use case.

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So for the longest time, companies have been competing against each other in terms of information that they possess, that can help them to gain some sort of competitive advantage.

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And with the advancements in technology and resources, it has been coming possible to collect more and more different types of data for business use cases.

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Nowadays, for example, ESG data can be collected directly from company reports that are being mandatory written by some companies in various countries.

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Web data can be now collected through technology like natural language processing.

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IoT data can be collected directly from the sensors that are implemented in the products themselves.

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These are just some some examples of of of different data types. As mentioned previously, alternative data can be essentially anything.

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Let's get into the use cases.

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As the name suggests, alternative data can be used as a proxy to measure information in alternative ways, and it will gain you access to insights before your competitors or help you get better accuracy for your predictions.

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For example, investors and hedge funds can nowadays use even weather data or information collected from social media, to understand the market a bit better and calculate their Alphas’.

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So for anyone willing to use alternative data in their business, there are a couple of things to consider.

Given the nature of alternative data, its potential is highly related on its level of exclusivity. And for this reason, you want to make sure that your data provider is giving you insights that your competitors are not yet having.

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The two other points related to this data are; application and correlations.

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They actually come together.

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So for example, as weather data was not necessarily meant to be used for financial analysis, you want to make sure that you have the expertise in your team that  can provide you with actionable insights from that data set that actually correlate with the real life information that you're trying to understand.

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That was all for this time.

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See you next week!