Maximising data value

In the last 10 years, active management has been under pressure from the low-cost passive returns from index funds and exchange traded funds.

In pursuit of higher returns, active investing strategies are starting to supplement traditional data sources with more diverse data to in order generate strong alpha. Buy side spend on alternative data has increased over the previous 3 years and is expected to continue to grow.

The associated technology to fully harness alternative data remains a significant barrier to all but the largest firms. How can data vendors develop a strong value proposition and capitalise on the market’s diverse data needs?

In this analysis, we outline the key challenges the investment industry faces in exploiting alternative data signals, introducing several untapped customer engagement models for data vendors. We also explore the components required in developing a signals factory proposition, and highlight the areas of focus to ensure the effective monetisation of data assets. We conclude by outlining the benefits and implications of using machine learning to improve data quality.
 

The majority of buy side believe alternative data will positively impact their investment performance. Deloitte has surveyed over 100 investment managers (IMs) and has observed significant technological, talent and risk challenges that integrating such diverse data presents.

Our proposition is intended to be a starting point for understanding the advantages and implications of using machine learning to maximise the value of alternative data. We thus empower data vendors to provide untapped utility for consumers of alternative data.