Climate change has now been identified as one of the biggest threats to human civilization as we know it. World over, there are calls to align investment decisions with Sustainable Development Goals.
One of the key drivers of any programme is the way investment decisions are arrived at and the milestone monitoring during the life span of the asset. The principles for responsible investment (PRI) recognize that institutional investors have a duty to act in the best long-term interests of their beneficiaries and that environmental, social, and corporate governance (ESG) issues can affect the performance of investment portfolios1. Private Equity (PE) investors have been traditionally using the risk ratings based approach for making investment decisions.
Most Equity Investment tools are now actively seeking to quantify the ESG (Environmental, Social and Governance) risks. The ratings from these tools are used as one of the key decision-making parameters against the investment value chain which starts with ESG Risk Assessment and ends with ESG reporting.
Consistency in the quantification of risks and opportunities to advise the PE investor in building a resilient portfolio is the objective of all ESG risk rating systems. Currently, there is no agreed industry wide methodology for the identification, classification, and assignment of risk ratings. It is proposed that any risk future ESG rating methodology that is standardised across the PE investment should include Geospatial data for decision making.
The decision making tools are invariably digital and have three major layers; 1. Data layer 2. Rating Layer 3. Data Visualisation layer.
Data Layer
The data from multiple sources, ranging from internal business data to data crawled over the internet, is collected and aggre- gated. It is here that the Geospatial and Climate databases, free or paid, can be integrated into economic and social data.
The datasets that are available from the likes of UNEP, NASA, and ESA (European Space Agency) cover a wide range of observation data that have reliable data accuracy and temporal consistency.
Output and insights generated from these datasets would depend a lot on the temporal and spatial resolution of these datasets.
By definition, a lot of ESG data would be non-spatial and unstructured. This calls for the aggregation layer to be designed and deployed as a Data Lake. This would ensure all the data is stored in the native format and Schema on Write will be implemented at the time of data parsing. Data currency and Data security aspects should be thought through at the design stage as they would impact the quality of insights and the overall vulnerability of the system.
Rating Layer
The main purpose of the Rating layer is to churn the datasets and come up with an order of importance based on the methodology of risk rating.
AI-driven rating engines are the choice of the day. As much as the AI engines can comb the data for trends, the key challenge is to Train the Machine Learning (ML) engine to generate the insights. The textural data for Social and Governance risk is relatively easy to train.
The data from multiple sources, ranging from internal business data to data crawled over the internet, is collected and aggregated. It is here that the Geospatial and Climate databases, free or paid, can be integrated into economic and social data.
The Geospatial AI poses a specific challenge, where all the social and governance will need to be linked to the Geographical coordinates. Since most of the PE investment decisions are about identifying underlying risks of the acquisition of potential assets, it makes sense to tag the geolocation of the assets.
The geocoded asset data can then be parsed to run buffer analysis and can be aggregated using the Geospatial AI. The business process for quality control of final risk ratings should also include human intervention and moderate the risk scores.
Visualization layer
Geoscientists would easily identify themselves with the visualisation layer for representation of the data and insights. Even though usage of the Geo-visualisation layer as a Common Operational Picture for all the stakeholders is a common practice in the geospatial community, it is still not a preferred view in the PE world.
Most of the PE investment is directed at acquiring and/ or maintaining the physical assets that are geo-located. With a better UX (User Experience), it should be possible to introduce geo visualisation to the PE investors. The existing users in the PE world range from analysts to decision makers and there is a strong case for providing the Geo dashboards to all the personas.
The UX trends suggest that the visualisation layers that offer data editing capability are adopted faster than the layers that do not have editing capabilities. This calls for the integration of the tools that would display the graphical data over a geographic backdrop to offer GeoInsights. A visualisation tool offering GeoInsights is potentially a Decision Support System and would command better value in the ecosystem.
This system of GeoInsights will also be a common language between multiple businesses to ascribe values to multiple assets.
The Single version of visual truth presented by the ESG data presented on a geographical backdrop would form the backbone of Data-Driven Decision Making.
The ESG risk ratings system is not a complex one in terms of architectural design but a few notes in this regard are called for. While coming up with the architecture the recent trend is to host the data on the cloud and consume the infrastructure, data and application services that are available as IAAS/PAAS/SAAS.
One of the key benefits of embracing the cloud model is to be future-ready for scaling by design. In past, the applications thrived on mutual exclusivity, but the future is collaboration. An API first approach will allow collaboration between various tools and systems being used at the customer organisation. Digitalisation and standardization of all the business processes and deliverables would ensure interoperability between all three components.
Finally, future software developers specialising in Geospatial technology would need to be part of a wider and multidisciplinary team operating in the DevSecOps model. Embracing the newer technology trends will not only allow the Geospatial professionals to make themselves relevant to more business areas but would also create more economic value.
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(Mayur Gori is the Director of Product Delivery and Operations, ERM. The views expressed in the article are the personal opinions of the author.)
Disclaimer: Views Expressed are Author's Own. Geospatial World May or May Not Endorse it
