In recent times, alternative data has made its mark in the investment space. Various organizations are using alternative data to get an insight into that stock market. This is eventually going to bode well for any company that knows how to use the data.
Understanding how alternative data can help the asset management of an organization can make or break a company. We take a look at how.
Types of Alternative Data
Knowing more about a product can be exceptionally helpful for a company. Product data provides an in-depth analysis of a consumer. Websites such as Amazon, Walmart, eBay, Target, etc., offer a plethora of insights.
A product’s rank based on its sale rate can indicate strong performance. Likewise, the poor ranking and a discount history can expose inventory problems and margin reductions.
Moreover, advertising data provides deep insight into corporate spending behind product launches and anticipates revenue changes. Product data from retailers are powerful predictive indicators of revenue performance.
Scraping product reviews can give the investors a chance to gather desired information on a product life cycle. This helps to make relevant and updated breakdown of company earnings.
Various companies are utilizing scraped product reviews to evaluate a trending product. Negative feedback can enhance stock volatility and highlight the risk to shareholder value.
Almost everybody uses social media these days, whether it be Facebook, Twitter, or YouTube. Public sentiment on social media gives insight into both the public at large and how people are thinking or feeling about a given topic. Pulling the data of words correctly, companies can understand consumer sentiment very well. From company popularity to product reviews, investors can use sentiment analysis of social media platforms to analyze the market and make more informed financial decisions.
Risks of Alternative Data
Alternative data comes with its own potential risk. If not mined correctly, the wrong alternative data may breach someone’s privacy, contain sensitive data, or not meet data guidelines. For example, a company using CCTV data of Black Friday to understand consumer behavior better may breach privacy by showing glimpses of credit card transaction details.
Along with the nature of data, entrusting your data with the wrong team can prove to be risky as well. Bad modeling can cost a fortune. For data to be useful, it needs to be cleaned properly and effectively. Messy datasets that do not offer backtest make it harder for the quality to be assessed.
Companies are using alternative data in asset management to reduce the potential risk of loss and have more sustainable development in their organization. To stay ahead in the competitive landscape these days, companies should learn to adopt alt data into their strategy.
However, to do this effectively, you need to have accurate, high-quality data sets and a team that knows how to clean, model and make sense of it all. With Chain of Demand’s Predictive Insights, managers and investors can make data easy.