Tracking Companies’ Real Time Sustainability Trends: Cognitive Computing's Identification of Short-Term Materiality Indicators

Abstract

The article argues that what as known as big data analytics using cognitive computing can be applied to sustainability (environmental, social and corporate governance) analysis. This can supplement what is now almost entirely done by human analysis, but this new technology is scalable and can create multiple real time data points (trends). Proprietary sustainability data analytics has been developed by a technology start up, TruValue Labs, Inc.. Using early data for a six month period, we have found statistically significant short term volatility variations correlated against what is called (and defined as) compounded TruValue (cTV), a sustainability indicator. This has been especially true in the E and S, and to a lesser degree, G areas. We argue that such short-term movement, previously undetected, and therefore not measured, has potential materiality (hence value) implications. The article sets the stage for these conclusions with a review of sustainability ratings and measurement systems currently in use, a brief review of cognitive computing, a discussion of materiality and sustainability as well as how TruValue data is generated.

Keywords: sustainability, big data, cognitive computing, ESG, materiality

JEL Classification: D60, D62, G19, G29, Z00