When it comes to data gathering for ESG, no stone should be left unturned. Operations, hiring practices, and vendor relationships are just a few of the diverse metrics that must be captured, centralised, and analysed. Let’s explore how data plays a pivotal role in ESG reporting and sustainability objectives.
5 Mins
20th September 2024
Article
Sustainability, CUBΣ
The corporate sustainability report has evolved from being a mere reflection of company ethics to a central document that reflects an organisation’s effective governance and resource management.
Its implications are significant; in fact, 85% of investors now believe that the disclosure of ESG metrics and KPIs should be assured at the same level as financial statement audits.1
So, how can we ensure that the data captured for these reports is comprehensive and accurate enough to meet compliance and maintain credibility?
The backbone of ESG compliance is data collection. The specific data required varies by organisation, often resulting in the entire period between annual reports being dedicated to gathering, measuring, and recording information. Scope 3 emissions, which are outside the organisation’s direct control, complicate this further as suppliers must provide reliable, accurately measured data.
It is no surprise that the ESG data market is valued at $1 billion in annual revenue—a threefold increase in just five years.2
To ensure compliance and avoid the risk of greenwashing, data must be collected from multiple sources, centralised into a real-time database, and analysed to prepare for submissions and report writing.
Capture
Effective ESG data capture relies on a multitude of sources, including endpoints, the edge and the cloud.3 This data must be both accurate and real-time; technologies like 5G and IoT enable rapid, low-latency data capture across these sources. Such capabilities allow ESG teams to access vast volumes of data, helping them meet reporting requirements while minimising the impact of data processing.
Solutions such as Singtel’s Eco Insights are designed to capture data within the network. The customised dashboard consolidates usage data and carbon emission statistics from our networks and ICT assets into a single view, providing insights into both Scope 2 and 3 indirect emissions.
Centralise
According to 39% of enterprise leaders, the biggest challenge for data-driven businesses is making collected data usable.3
Utilising a Single Source of Truth (SSoT), such as an ESG reporting dashboard, offers a comprehensive view of the data and corporate performance at any time. This centralised dashboard not only facilitates ESG reporting and compliance but also prepares for auditing by recording the data sources in one streamlined view.
Centralised dashboards for sustainability extend beyond ESG data capture. The Singtel Empower Portal, which houses Eco Insights, offers a holistic view of business operations. Integrated into the CUBΣ suite of services, the Empower Portal leverages AI and predictive analytics to provide a single platform for proactive network monitoring across multiple vendors and third-party networks, asset and incident management, sustainability metrics tracking, and more.
Analyse
When data drives multiple layers of business decision-making, the entire digital ecosystem must work in harmony to enable effective, action-driven analysis.
For ESG analysts, the benefits of these services become evident once the sustainability report is finalised. With automated data analysis, they can shift their focus from tedious tasks to identifying areas for improvement in the coming year.
As ESG reporting frameworks evolve to reflect changing definitions of sustainability, the pressure on data-gathering teams increases.
These teams will need to leverage AI to gather and analyse data from a broader range of metrics, including the notoriously challenging Scope 3 emissions. AI presents a significant opportunity for sustainability, allowing metrics gained from reporting to highlight the best next steps for resource allocation and operational efficiency.
With this growth in required ESG data comes a greater urgency to protect it from nefarious actors. Employee and customer data are particularly vulnerable, requiring protection for privacy and compliance with ESG metrics. Data leaks not only erode trust but also hinder the fulfillment of social criteria, the ‘S’ in ESG.
AI-powered phishing attacks pose a notable risk in this landscape. Capable of generating convincing emails and mimicking voice messages, these attacks can target up to 60% of victims—similar to human-generated scams.4
For companies to succeed in their ESG efforts, they must adopt cyber security strategies that prepare for an AI-driven threat landscape while harnessing the sustainability benefits it offers.
There are no limits to data collection for ESG. Being over-prepared allows for swift responses to changes in reporting frameworks and helps safeguard a company’s strong sustainability standing.
By layering robust cybersecurity measures and centralising data capture dashboards, companies can confidently report on their sustainability efforts and focus on actions that make a material impact towards a better, greener world.
References:
Sustainability, Transportation, Smart cities
Sustainability, 5G, Manufacturing and logistics
Sustainability
Sustainability, Artificial intelligence and machine learning, Quantum computing
SD-WAN, Sustainability, Artificial intelligence and machine learning, IoT
Sustainability, IoT
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