Figuring out how to use big data is the next frontier for the asset-management industry. Equity investors must have the right culture—and ask the right questions—to successfully integrate data science into research and investment processes.

Why Is Big Data So Important?

There’s a colossal amount of data available to investors today. For example, more than 8,000 US-listed companies produce quarterly 10-Q and annual 10-K reports, each hundreds of pages long. We’ve collected 675,000 of these reports that were filed over the past 26 years. Globally, companies also conduct about 20,000 earnings calls a year in English, each yielding detailed transcripts. And if you include non-English corporate documents from around the world, the data mountain mushrooms.

In theory, portfolio managers must pore over thousands of pages of data to fully gauge the risks and opportunities that a company faces. Practically speaking? It isn’t humanly feasible.

Data science offers a solution by applying machine learning and artificial intelligence (AI) techniques to process information. Yet even the smartest software requires human direction and expertise to translate data into investing conclusions.

Bringing Together Research Skills

Generating insights requires a broad set of investment skills. Large data sets must be crunched and combined with complex statistical and economic models. Investment organizations rooted in quantitative research may seem more attuned to data science but might not be equipped to make sense of the information.

Fundamental analysts can apply research intuition by asking the right questions needed to extract useful information from huge pools of data, but they may lack the technical skills to process it efficiently.

We’ve been introducing advanced big data techniques to tackle equity investing conundrums that couldn’t be solved by human researchers alone. In the following case studies, we aim to show how a hybrid approach drawing upon diverse analytical skill sets can help investment teams rise to the data challenge.