ESG is a Data Challenge

Environmental, Social, and Governance (ESG) reporting has rapidly become a key focus for businesses, regulators, and stakeholders. For accountants, it represents not just a reporting challenge but a data conundrum.

Insights from industry papers, such as the IFAC's "The State of Play in Sustainability Assurance" and Eden Smith's research into data-driven ESG strategies, underscore the importance of accurate, transparent, and actionable data in addressing these challenges.

The journey from financial to non-financial reporting exposes significant gaps in data collection, management, and interpretation. Interestingly, the skills and data foundations required for ESG mirror those demanded by artificial intelligence (AI). Accountants now find themselves at the nexus of these two rapidly evolving fields.

ESG reporting requires tracking metrics such as carbon emissions, diversity metrics, and governance frameworks - data that often resides in disparate, unstructured formats. Unlike financial reporting, ESG lacks universal standards, adding complexity to its preparation and assurance.

Key data challenges include:

• Data Fragmentation: ESG data often originates from diverse systems, ranging from operational records like energy bills to HR systems for workforce diversity data.
• Data Integrity: Ensuring accuracy and auditability is critical, particularly as stakeholders scrutinise ESG reports for greenwashing risks.
• Data Complexity: ESG demands handling both qualitative and quantitative data, necessitating advanced analytical capabilities.

The skills and frameworks required for ESG reporting closely mirror those essential for AI adoption. Both are grounded in the ability to collect, analyse, and act upon large volumes of complex data.

Insights from Deloitte's Powering AI Report emphasise that organisations must integrate AI and ESG strategies, as their success depends on similar data infrastructures and analytical expertise.

1. Data Collection and Integration

In both ESG and AI, data is sourced from numerous, often siloed systems. For ESG, this may include energy sensors for emissions tracking, supplier audits for governance, or surveys for social sustainability such as employee wider social participation, demographics, and impact behaviours. AI, similarly, depends on aggregating data from disparate sources to train algorithms.

Eden Smith’s insights into data-driven workforce solutions highlight the need for accountants to:

• Leverage centralised data platforms.
• Ensure that ESG and AI datasets are clean, comprehensive, and harmonised.
• Navigate unstructured data sources effectively.

2. Analytics and Decision-Making

AI uses data analytics to generate actionable insights, while ESG reporting seeks to tell a story about a company’s sustainability activities. Both require interpreting data trends and predicting future outcomes.

Accountants must develop skills in:

• Predictive Analytics: Forecasting ESG risks using AI-driven models.
• Scenario Analysis: Identifying how factors like climate change or regulatory shifts might pose risks and provide opportunities for the organisation.

3. Ethics and Governance

Both ESG and AI operate in areas that demand ethical oversight. ESG reporting must avoid greenwashing, while AI implementations face risks of bias and lack of transparency. So called “AI-washing” is a new criticism levelled at organisations using AI to overplay their tech credentials. Accountants play a critical role in ensuring robust governance frameworks for both ESG and AI, as outlined in PwC’s A Practical Guide to Responsible AI.

Where and how sustainability fits within an organisation is still being determined. Some organisations are developing a dedicated sustainability division, which has ESG as its first focus, some are placing it firmly within other established teams such as data, financial reporting or even HR.

Whether or not organisations have decided or considered the impact and direction of how they track their sustainability goals, it presents an opportunity for the finance and accounting teams to play a central role. They can use their understanding of the business and their skills in data and mathematics to support the organisation with this data challenge.

To understand how the data challenge of ESG intersects with the accounting profession consider the following:

• Measurement and Attribution: ESG requires a close link to the supply chain and procurement partners in tracking  the carbon footprint of a product and services to meet Scope 3 measures. With knowledge of all the purchased components of a business the accountancy and finance teams are primed with the information and knowledge of who and where to start this analysis.
• Real-Time Reporting and Audit: Investors increasingly expect real-time ESG updates, much like financial reports. Mergers and acquisitions will increasingly look for sustainability metrics in due diligence and audits. Having experience in auditing and gathering this information, accountants who are closer to ESG metrics will support these requirements. This necessitates continuous data collection and automated reporting systems.
• Scenario Analysis: Evaluating future ESG risks, such as climate impacts or regulatory changes, involves sophisticated modelling tools. These draw directly from techniques used in AI-driven decision-making and statistical analysis t- a part of financial analysis, crime, and audit. This is a well-known and established practice that often sits within the finance and accountancy practice of an organisation.

Without robust data systems and analytics capabilities, ESG reporting risks becoming superficial, undermining its credibility and utility.

To address the data challenges of ESG and AI, accountants must expand their skillsets beyond traditional finance. Key competencies include:

1. Data Literacy: Understanding, managing, and analysing ESG and AI datasets using tools like Python, SQL, Power BI, or Tableau.
2. Ethical Governance: Ensuring ESG data is trustworthy, and AI models are fair, unbiased, and auditable.
3. Critical Thinking and Storytelling: Translating ESG and AI insights into narratives that stakeholders can understand and act upon.
4. Collaboration and Cross-Disciplinary Knowledge: Working with data scientists, sustainability experts, and technologists.

Programmes such as Future Learning Group’s POWERskills Training emphasise developing these skills and empowering professionals to navigate complex, data-driven landscapes confidently.

Organisations must also invest in the right infrastructure to support accountants in these roles. This includes:

• Implementing centralised data platforms for ESG and AI data.
• Providing ongoing training in emerging technologies and sustainability.
• Collaborating across departments to break down silos and ensure data cohesion.

The convergence of ESG and AI represents a transformative moment for accountants. Both require a foundation in data management, ethical oversight, and strategic analysis - areas where accountants have the potential to excel. Organisations like Eden Smith are at the forefront of this shift, providing tools and training to equip accountants with the people & skills they need to succeed.

For the ICAEW and its members, the challenge is clear: embrace this new era of data-driven decision-making, where ESG and AI are not just reporting challenges but opportunities to lead with integrity and innovation. By investing in the right skills, infrastructure, and partnerships, accountants can redefine their value in a rapidly changing business landscape.