Predict & Influence Muses - Series 015: Challenges in ESG Data Collection

While everyday there are announcements that different organisations are announcing initiatives around ESG or sustainability, those are good and encouraging moments. However, even for the organisations that are announcing these grand initiatives and plans, the fundamental question is whether they themselves are practicing right in all aspects of E, S and G, let alone the majority of the organisations that are carried on business as usual.

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ESG data collection is hard. In the first place, an organisation needs to define what you what to measure and track. If you are Public Listed Company (PLC), you will need to follow a Sustainability Reporting Guideline from the exchanges that you are listed. If you are government agencies or NGOs, you may have to follow Global Reporting Initiative (GRI), and if you are a financial institution, you will need to learn Taskforce for Climate Related Financial Disclosure (TCFD) by heart. ?Soon, IFRS, the international accounting body, will release IFRS Sustainability Disclosure Standard around Q3 2023 too, means majority of us to have standard accounting practice will need to follow suit. Beyond understanding and complying to these standards, how to you measure and track your progress?

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First thing first, you need to ensure that certain actions are taken. Ie There is anti-corruption training conducted for all employees, or there is a way to reliably measure waste across different sites or offices. If there is no such action taken, the organisation should first ensure that it starts with arranging and engaging with such activity to start to see the positive impacts to the entire ESG practice. There are also situations where, looking at the required ESG indicators, some indicators would not be tracked in the short term in a very effective way. In this case, the organisation should consider deriving some reasonable/defendable estimates based on industry practices.

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Now, you have data that can be sent real time (ie sensor/IoT data – more on that later), extracted from systems, and manually compiled. All these data will need to be further processed. Without reliably capturing, cleansing and processing, summarising and calculating as such as it is able to be audited, it may be worthless. Batch data from an ERP system like SAP would be cleansed and processed, performed the necessary calculation based on trusted formulas, and the final figure (ie total carbon emission) would be ready.?Data compiled by a collaborator such as HR or corporate secretary or facility manager would also be tracked as to conform to data needs, and it is being submitted by an authorised/trusted individual. From on boarding and enablement point of view, these setups can be standardised and fast. Through an end to end testing, these can be put in place quickly, and have it operationalised.

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However, having batch data and compiled data by collaborators may not suit everyone’s appetite. In the world of “Real Time”, everyone, especially the senior executives, want to see how current situations of carbon emissions, waste, electricity consumption etc. being reflected in the dashboard. Technologically, this can be done. However, in the context of “impossible triangle” of time (speed of implementation), money (investment to ESG solutions), and quality (what you will get, ie real time), as opposed to the previous approach of taking time and cost as priorities (I would argue that it has sufficient quality), enabling real time will require effort (time) and monetary investment to achieve additional quality (ie real time). To enable real time, APIs may have to be built. The difficulty lies on there are many systems, underlying infrastructure and enabling technologies that different organisations would use. Even if common ERP like SAP or Oracle, different versions, customisations that applied to the organisations‘ specific workflow etc。 would break any pre-built APIs or connectors. Further, if the organisation wants to tap into IoT data, that is an even a “wild wild west”, as there is no single standard that can be adopted for 1 type of indicator, for example. Is the IoT device connects to reliable internet connection? Can this IoT device push data or data needs to be pulled? What protocol does this device use? Those are the questions that need to ask and likely this piece of work will need to be customised for each type of IoT device within the organisation. It would add more time and monetary investment to enable such capabilities. Once these data points are in the platform, similar data processing will need to be applied to cleansed, processed, summarised and calculated so that it can be useful data points for ESG.

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My advice is, let’s start to have the basic first as it is a long journey. We can enable data collection through automating batch data collection, getting human curated data through collaborators that can be verified. With these data points collected, we can embark on the journey of knowing where you are. The current situation may not be rosy, nor data may be outdated or not 100% accurate, but you have a baseline. Now start to enhance the organisation ESG practice, and then find high impact real time use cases for APIs or IoT data integration.