May 14, 2021
Alternative data has been broadly welcomed by companies looking to improve prediction rates, maximize returns and decrease unforeseen risks. In addition, this new source of data provides security, offering businesses more than just big data.
The rapid generation of alternative data obtained through emails, social networks, satellites, financial reports, geolocation, among other sources, has introduced a great new opportunity to improve profits in the market. The correct use of alternative data through machine learning gives companies a fascinating advantage; access to valuable information about the behaviour of customers - and future customers - allowing companies to grow rapidly.
Nevertheless, its advantages come along with several challenges, and only some companies have been able to incorporate this type of data. The main limitations around alternative data lie in incomplete data sets, limited volume, low-quality data, lack of data verification, and data history to support it.
Alternative data is on its way to becoming a more frequent source for decision making, but its difficult implementation demands companies to rethink if they are ready for the challenge.
First of all, it is important to note that, thanks to alternative data, companies can compare and measure information in real-time, generating truly innovative indicators. For example, in this way, credits can be offered on the spot to applicants with an alternative credit score.
Alternative data improves the self-knowledge of a company. A large amount of existing alternative data allows institutions to have a more accurate perception of their performance. The connection of these insights enables companies correctly interpret how they are perceived by people, comparing themselves against competitors, suppliers, and other secure connection networks.
Another important benefit to be considered is the capacity to operate predictively with the usage of alternative data. Through this novel source of information and in conjunction with traditional sources, investors can more reliably predict long-term results. In addition, alternative data allows companies to predict clients’ behaviours, and in this way, growth projections become more effective, optimizing decision-making and reducing the probability of risk.
Alternative data is relatively new, so at first glance, it can raise several questions. There is a lack of historical records, plus not all alternative data is publicly accessible. Therefore, it is profoundly important to have an accurate understanding of where information is coming from and the limitations of usage.
Currently, there is a wide variety of alternative data sources, so it is essential for companies to organize them and have a clear distinction between those that are most relevant to the business. Furthermore, as there is no standard pattern of use, it is of utmost importance to understand how they work holistically and completely in relation to all sources of information.
Added to this scenario, companies face the challenge of data interpretation. With a wide variety of sources, it can be challenging to distinguish and judge the quality of each source to choose the correct one. Any corruption of this data can result in incorrect decision making and great harm to the company.
Another important aspect to be considered is the accurate knowledge of the private legal terms of each source. This includes understanding where the information is coming from, the limitations of its implementation and correct and ethical use of its storage.
Finally, it is vital to highlight that as some data may be unstructured and incomplete, its usage is limited. For example, by having an incomplete history record, without the necessary backup of information, sometimes it is needed to let time pass and continue collecting data that will allow us to generate definitive patterns of behaviour in the future.
Alternative data allows companies to take a more promising and competitive role in the industry, so there is no doubt that, if used correctly, the company's growth is pretty much guaranteed. But to ensure that these sources will lead to correct decision-making, it is vital to generate technology teams or use external agencies to produce safe data. In this way, the provenance and accurate analysis of the alternative data is protected.
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