In focus - Thought Leadership

Harnessing the data science revolution

A mass of new information types, combined with new methods of analysis is giving investors the tools to enhance long-term performance.


Ben Wicks

Ben Wicks

Head of Data Insights and Research Innovation

Mark Ainsworth

Mark Ainsworth

Head of Data Insights and Analytics

The process of collecting and analysing data has undergone a revolution. No longer is it sufficient for active investors to tease investible insights from familiar information like trading figures, market shares and economic updates. A mass of information, from geospatial data to customs records and demographic data, is now available and can be manipulated in ways unheard of 30 years ago. If investors want to stay ahead of the game, they need to channel this deluge and harness its power to generate alpha in new ways.

To do so successfully, we argue, requires an understanding of what data can do for investors, what needs to be done to it to make it useful and who is equipped to do it. Just as important, however, is an understanding of its limitations and why data science alone cannot replace a good portfolio manager. Those who can marry industrial-scale data processing with tried and tested investment expertise will emerge as winners.

The growing importance of data is why at Schroders we have set up the Data Insights Unit. The team was formed in 2014 and has grown to over 20 data scientists, sourced from a variety of backgrounds and industries. Using data science skills, including machine learning, the team builds tools and provides a research service to help Schroders investors to make sense of alternative data and enable them to make better investment decisions.

What data are we talking about?

“Data” means far more than market data or accounting data. It includes large and “alternative” datasets that may be poorly configured for financial market analysis. Much of this is often thought of as “Big Data”. The dramatic increases in computer processing power, storage capacity and information mean that the amount of data that can potentially be interpreted by an analyst or fund manager is growing at an exponential rate, and in a thoroughly unstructured fashion. At the same time, a cadre of data science professionals is emerging and fashioning the necessary techniques to process this data.

These developments pose disruptive challenges to the investment industry. But they also provide a major opportunity for adaptive, well-structured organisations. The investment management industry is, at heart, a data processing industry: taking in data about companies, industries and economies, processing and analysing it and producing portfolios of investments as a result.


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