Recent forecasts suggest that the green bond market is set to nudge $100bn in 20161. This is good news – the flow of capital into sustainability geared investments is undoubtedly a positive move. But when you start thinking about what attracts investors to put their money into a fund you cannot avoid the notion of long-term value creation and what needs to be measured to ensure that the value is not only created but maintained. After all, sustainability – by definition – must reject short-termism. From within an organisational boundary this is hard enough but what kinds of challenges do organisations face external of their operational and fiduciary limits in capturing useful Environmental, Social & Governance (ESG) data?
There are continually expanding options for organisations to gather granular data – all of which is exceptionally useful in building a macro-level ESG picture. What the challenge is really about is establishing thresholds, how are they defined and what is the data you should be capturing at any given moment in time. This is why supply chains present such a sizeable challenge – they are dynamic, distributed, complex and are often vulnerable to disruption. Today, there simply isn’t a one-size-fits-all data capture provision that enables comparative analyses or advanced machine learning to intelligently decipher the patterns and logic within these spaces. However, that doesn’t mean you can’t leverage significant value from data collection platforms by adopting a systematic and considered approach to gathering a range of non-financial datasets.
So, what is the right way to treat supply chain ESG data?
Whilst there is no immediate panacea for solving the complexity there are some key aspects that should inform an organisational approach to supply chain ESG data;
Materiality matters at every tier of the supply chain. Options such as SASB’s materiality matrix is one way to segment your supply chain by sector and associated risk. However, because your supply chain will inevitably be in a permanent state of flux it is imperative that continuous monitoring, evaluation and capacity building are inherent in your data systems. Adopting a static approach to materiality assessment could easily miss changes in the supply base and rapidly undermine the value of any green bond or ESG-linked equity debt.
Subjective-ness is an ever-present issue when measuring supply chains. Unfortunately, there is no quick fix and eliminating subjectivity when looking at qualitative topics such as social and governance metrics is paradoxical in the sense that psychological traits are the very thing that help us to make good judgment. However, using robust methodologies, professional training and continual evaluation techniques along with full and transparent stakeholder disclosure is proving highly effective at reducing the deviances in qualitative data prior to disclosure.
Resolving the difference between quantitative and qualitative datasets through the aggregation process is also a challenge. Arriving at a consistent and dependable value of supply chain sustainability (or what we prefer to call the risk of an occurrence of unsustainable practice(s) in the supply chain) has to, by default, require a consistent methodology which withstands scrutiny. However, methodology alone cannot compensate for the differences in datasets and the constituent parts so we need to actively pursue ways in which consistency can be framed and implemented. And it may not be so much as a case of ‘resolving’ the difference between datasets but in aligning them to carry specific weighting in a range of scenarios including when a supplier is not willing to disclose information.
Exposing opportunities. Innovations stemming from accurate and appropriate data collection should also be factored into your ESG reporting. Too often, organisations take a risk-only approach to identifying data hotspots and ignoring the innovations that create and add-value to their business. In framing and asking respondents for data it is equally important to examine the front-line innovations that not only reduce risk but provide evidence of greater value creation.
Aggregation of ESG data. Isolated elements of an ESG dataset can spell danger for investors wanting to know more about the long-term performance of a business. Given that the components of each tenet are comprised of multiple datasets it is imperative that the aggregation techniques, weightings and applicability are structured in a way to cope with dynamic changes in materiality and scope.
All-in-all it seems like there are many challenges in the collection and aggregation of ESG data but none of these are insurmountable. We, as are other vendors, maturing the systems and platforms to aid organisations with their data capture and to also deliver meaningful and actionable insights from the data.
Written by Ecodesk’s COO, Damien Smith
November 17th, 2016