Each second, oceans of new data are being generated by the Internet, smartphones, satellites and other innovations. This data is commonly referred to as “Big Data” or “alternative data.”

Many asset managers are seeking to harness the power of Big Data by using technologies like natural language processing, image recognition and machine learning to analyze it and uncover new investment insights.

BlackRock’s Systematic Active Equity (SAE) investment team has been using these technologies to analyze alternative data for more than a decade. Over this time, the team has learned four key lessons about how to make the most effective use of these technologies within an investment process.

Lesson 1: Target the “right” data and multiple data sources

Investment teams looking to harness the power of Big Data must cast a wide net when seeking to find the right data to enhance investment outcomes. Why? The global economy and financial markets are highly complex, and the data they generate are often unstructured and noisy. It’s important to reach far to capture what is most relevant.

Yet data alone does not translate to alpha. It requires proper processing and analysis to arrive at investable insights. Once the “right” data is found, it is unlikely that a single dataset will produce accurate forecasts. SAE found that using multiple data sources to corroborate one another and answer the same investment question could significantly improve the quality of forecasts. The illustrative example below shows the various sources of information that can aid an asset manager in projecting a company’s sales growth.

The more data sources the better
Alternate data sources used to project sales growth

Alternate data sources used to project sales growth

Source: BlackRock, as of September 2020. Provided for illustrative purposes only, not meant to depict actual data.