How to Gain an Edge with Alternative Data

Chain of Demand

Alternative data is the new guidance to investment decisions. With the pandemic limiting access to traditional data sources, it has surged in popularity over the past several months. It will continue to remain a key factor in making decisions.

With so much talk about alternative data and its applications, the question remains, however: is it that easy to use? While it is true that alternative data for investors can prove highly useful in making investment decisions, it is not enough by itself.

To truly gain an edge with alt data, one needs to pay attention to some other related factors, which we list down below: 

1. Recognize the need

The first step to using alternative data for managers and investors is to recognize the need for it. It’s important to ask yourself exactly why you are turning to alt data and whether it is necessary to use it. Do you know exactly why you need it and how will it help you? Finding and buying alt data is not cheap, so simply turning to it blindly without really know how to go about applying it to your decisions may be counterproductive. 

2. Determine the type of data

If you’ve settled on the ‘need’ of alt data in your business decisions, the next step is to determine what type of alternative data is required. Alternative data can range from satellite images to weather forecast data. If you seek to know more about public reaction towards a specific brand or company, then gathering social sentiment data could be a good idea. However, on the other hand, collecting point of sales transaction data can also be useful for retail investors. In the end, the point of determining the type of data is to exactly what to look out for and then turning that into alternative data for investors. Then turning to the right source of data will allow you to do just that.

social media sentiment

3. Acquire the data

You can acquire alternative data for investors directly through data mining techniques. These days, platforms like Chain of Demand process millions of data points and provide lists of data sets that help investors and managers acquire the necessary sources to inform their decisions. While alt data is acquired through web-scraping, it’s often recommended to leave this to the experts, as this can save you tons of energy, time, and resources. 

4. Analyze the data

Unlike traditional data, alternative data is an unstructured form of data. This means that it needs to be cleaned, structured, and even modeled to analyze it properly. Given the huge volume of data, it is simply impossible to analyze it without automation. This is where artificial intelligence services come into play. Machine learning models help to take the data and make sense of it.

analyze data

5. Decision-making process

The decision-making process is the most crucial step in using alternative data for investment management. Today, a good decision combines data from different sources, synthesizes that information with experience to make an effective inference. Many investors only utilize alternative data to measure the right sizing and timing of an investment, however, another essential use of alternative data is to generate new ideas on investment. Using the insights obtained from alternative data, investors gain a better understanding of the market, which can then generate new business ideas.

Stay up-to-date

In today’s modern economic landscape, to sit still is to lose out on more opportunities. Staying competitively ahead means always be on top of the latest changes in the market. While alternative data isn’t the solution for everyone, it definitely helps make more sound decisions. As an investor, it is crucial to keep tabs on the market, and as such, acquiring and analyzing alternative data for investors can help you gain an edge. 

Yet, to do this effectively, you need to have accurate, high-quality data sets across various regions, platforms and companies. With Chain of Demand’s Predictive Insights, managers and investors can make data easy by leaning their decisions into millions of data points and predictive analytics.

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