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The value that can be extracted from a growing wealth of data across boundless sectors is only just beginning to be grasped. If you look at search engines or digital commerce platforms, an almost direct relationship exists between the amount of data users willingly give up and the value this has. There is also the fact that those with the most data at their disposal will probably have the best artificial intelligence in the future, making them nigh on invincible.

In finance, data of one sort or another has always held intrinsic value. People who trade in the zero-sum game of capital markets all need a Bloomberg terminal or Thomson Reuters data to have a look at all the traditional price information, earnings estimates and so on.

In addition to this, large quant funds have established in-house data science teams performing deep explorations of newer and larger data—and this trend is spreading to traditional asset managers as well.

Alternative data being leveraged might include esoteric data sets like satellite data, GPS and IoT ("Internet of Things") data used in industry or farming. It also might include so-called exhaust data—a byproduct of industries like e-commerce—such as emailed transaction receipts. Another area is sentiment data, which has become popular with the advent of social media. In some cases, those data sets can then be combined.

Generally, these insights are used to make predictions about listed stocks. But they can also be of interest in areas such as private equity, relating to private, unlisted companies.

Rado Lipuš, CEO of Neudata, which allows money managers to outsource part of the data-science function performed in-house by large quant funds, thinks that look-throughs into unlisted companies for private equity firms is an interesting new area.

"If you think about it, a lot of the data we see relates to nonlisted stocks. It could be Uber or other fintechs or startups, or just privately held companies in various sectors," said Lipuš.


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