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ESG management and reporting requires data and analytics which in many cases can be complex due to the diverse sources and the complexity of gathering data. To explore this further, Dimitris Kaskantanis, Senior Director in Global Internal Audit at Intrum, discusses the role of data and analytics on ESG management and reporting with Tasos Nikolaou, founder and CEO of Net Zero Analytics.

Corporate Social Responsibility (CSR) is not a new concept; nor are ESG (Environmental, Social and Governance) frameworks. However, both have grown significantly over recent years, and with increasing regulation to ensure enforcement, transparency, and comparability. ESG management and reporting thus requires data and analytics which in many cases can be complex due to the diverse sources and the complexity of gathering them.

We discussed the role of data and analytics on ESG management and reporting with Tasos Nikolaou, founder and CEO of Net Zero Analytics, an advisory firm that assists businesses in creating, communicating, and implementing their ESG and sustainability plans.

Q: What is the role of data analytics on ESG?

A: ESG refers to a set of criteria used by investors and organisations to evaluate a company's impact on the environment, society, and its governance practices. Data analytics plays a multifaceted role in ESG by enabling data collection, integration, analysis, and reporting. It empowers investors, organisations, and regulators to make informed decisions, manage risks, and drive positive social and environmental impact through investments and business practices.

Q: How can data analytics assist in transforming data to actionable insights?

A: Data analytics is a powerful tool for transforming raw data into actionable insights by collecting, cleaning, analysing, and interpreting data. It involves modelling, visualisation, and reporting to drive informed decision-making, with a focus on ethical considerations, collaboration, and continuous improvement. Moreover, it improves processes, and ultimately leads to better outcomes in various domains, including business, healthcare, finance, and more.

Q: Can you provide us with an example of applying analytics for ESG?

A: Say, an investment firm wants to assess the ESG performance of a portfolio of companies in the technology sector to make informed investment decisions and promote sustainable practices. Analytics could be applied as follows:

  • Data Collection and Integration: Gather ESG data from various sources, such as company sustainability reports, public databases, and ESG rating agencies. Integrate this data into a centralised database.
  • KPIs and Portfolio Comparison: Analytics could be used to assign ESG scores to each company based on predefined ESG metrics, such as carbon emissions, diversity and inclusion, and board governance. Time-series data could also be used to analyse trends in ESG performance over months or years to help identify which companies are making the biggest strides in improving their ESG practices.
  • Benchmarking: Analytics makes cross-sector ESG comparison easier by having clear measures of performance that can be tied back to ESG indices or benchmarks.
  • Risk Assessment: More predictive techniques can be used to identify ESG-related risks that may have a future effect on financial performance, while advanced modelling can be used to stress test the portfolio against critical changes in ESG factors.
  • Visualisation and Reporting: Creating ESG dashboards and reports to highlight key insights and trends, while making complex ESG data more accessible for decision-makers.
  • Actionable Insights: The insights delivered by analytics can provide concrete recommendations for portfolio adjustments, for example choosing to move investment towards companies with lower ESG risks. It also makes it easier to engage with portfolio companies to encourage improvement in ESG performance.
  • Ethical and Regulatory Compliance: Ensure that the portfolio aligns with ethical investment criteria and complies with any regulatory requirements related to ESG disclosure.
  • Continuous Monitoring: Continuously monitor ESG data, company reports, and news to stay updated on changes that may impact the portfolio's ESG profile.
  • Feedback Loop: Establish a feedback loop to assess the impact of ESG-driven decisions on the portfolio's financial performance and ESG outcomes.

In this example, data analytics enables the investment firm to assess ESG performance, manage risks, and make investment decisions that align with their ESG goals and ethical considerations, ultimately contributing to sustainable investing practices.

Q: What are the challenges for applying data analytics in an ESG context?

A: While ESG has existed for many years it is only recently that it has taken a more structured approach. More and more companies are issuing sustainability reports with detailed information on their business practices surrounding ESG, however, we are still in an infancy stage with the vast majority of such companies being the largest in their fields. The CSRD that was issued in 2022 puts a timescale by which SMEs will gradually be obliged to issue sustainability reports but this will take another 3-4 years to reach full scope. Hence the biggest challenge is availability of data, drawing on disparate internal and external sources (if the required data even exists yet). Other challenges include:

  • Data Quality: The range of data sources leads to inconsistencies and issues of standardisation. This is not helped by the lack of a single, global ESG reporting framework making comparisons difficult. Often, ESG data is compiled manually and has missing values and numerous errors.
  • Data Complexity: ESG data is often unstructured, drawing on PDF energy bills, news articles, social media, and other areas, making it harder to analyse. The volume of ESG data can also be overwhelming, both for the end user to collate, but also for the systems to process.
  • Data Privacy and Security: ESG data may contain sensitive information, raising privacy and security concerns when collecting and storing it.
  • Data Bias and Subjectivity: Reporting bias can be a major factor in ESG data, where companies choose only to present the data that shows them most favourably. Some ESG criteria can also be subjective (especially around social and governance metrics) which can make it harder to consistently assess and compare performance.
  • Regulatory Complexity: ESG regulations and reporting requirements are immature, vary by region and evolve over time, making it challenging to stay compliant, while interpretation of evolving regulations can be complex and require legal expertise.
  • Stakeholder Engagement: Engaging stakeholders, including company management, investors, and regulators, in ESG data analysis and decision-making can be challenging, particularly when seeking to present complex analysis in a transparent and understandable manner. Conversely, when companies are aware that their ESG data is accessible to investors, customers, and the public, they are more likely to invest in sustainable practices and ethical behaviour to maintain a positive image. Moreover, these companies are also more attractive to investors, leading to increased investment in responsible and sustainable firms. This pressure motivates companies to enhance their ESG practices.
  • Integration with Business Strategy: Integrating ESG analytics into an organisation's broader business strategy and decision-making processes may require a cultural shift. Many consumers and customers now prioritise products and services from companies with strong ESG records. As consumer demand shifts toward sustainable and socially responsible brands, companies are compelled to compete in these areas to retain and attract customers.
  • Talent and Expertise: The shortage of professionals in the analytics space is well known but is even more acute when expertise in ESG and sustainability is also required. This makes it almost impossible for smaller organisations to recruit and retain good quality ESG analysts. On the other hand, companies with strong ESG performance are often more appealing to top talent. This competition for talent encourages companies to invest in ESG initiatives to attract and retain skilled employees.

Q: Has the application of analytics on ESG improved transparency and promoted healthy competition among peers?

A: As already explained, we are currently experiencing the infancy stage of ESG reporting. Nevertheless, there are considerable steps forward and the application of analytics on ESG data has significantly improved transparency and created competition benefits in various ways:

  • Data Standardisation: Analytics has helped promote the standardisation of ESG data reporting. Organisations and regulators are increasingly adopting common frameworks and standards (eg, GRI, SASB, TCFD) for reporting ESG information. This standardisation makes it easier to compare and assess ESG performance across companies and industries.
  • Enhanced Reporting: ESG analytics has enabled organisations to provide more detailed and transparent ESG reports. These reports go beyond basic compliance and provide stakeholders with a deeper understanding of a company's ESG initiatives, goals, and progress.
  • Data Validation: Analytics tools can help validate the accuracy and reliability of ESG data, reducing the likelihood of misleading or inaccurate information being presented to stakeholders. This validation improves trust and transparency.
  • Benchmarking: ESG analytics allows for benchmarking a company's performance against peers and industry standards. This comparison helps investors and stakeholders assess a company's relative transparency and commitment to ESG factors. In addition, when companies see that their peers are outperforming them in specific ESG areas, it can incentivise them to improve and remain competitive.
  • Trend Analysis: Analytics tools enable organisations to analyse historical ESG data and track trends over time. This historical perspective provides context and transparency regarding a company's progress or setbacks in ESG performance.
  • Regulatory Compliance: Analytics assists companies in complying with ESG-related regulations and reporting requirements, ensuring that they transparently disclose the required information.
  • Consumer and Customer Demand: Many consumers and customers now prioritise products and services from companies with strong ESG records. As consumer demand shifts toward sustainable and socially responsible brands, companies are compelled to compete in these areas to retain and attract customers.

Q: Is the application of analytics in ESG mainly focusing on past performance, or could it be more forward looking?

A: Going back to our original drawback, it is only recently that ESG has taken a more structured approach. Most analysts are still at a stage of gathering existing data and therefore focusing on past performance. In the near future and as more companies issue sustainability reports, technological advancements such as AI will definitely assist in forward looking analytics.

Q: How can soft aspects such as wellbeing and workplace safety be measured?

A: To measure soft aspects like wellbeing and workplace safety effectively, it is essential to use a combination of quantitative and qualitative methods. Most importantly, organisations should create an environment that encourages open communication and reporting, where employees feel comfortable sharing their concerns related to these aspects. This holistic approach helps organisations identify areas for improvement and create a safer and more supportive workplace.

Q: What controls should exist in the application of data analytics for ESG to prevent "greenwashing"?

A: To prevent "greenwashing" in the application of data analytics for ESG reporting, organisations should implement robust controls and practices to ensure the accuracy and transparency of their ESG data and communications. Greenwashing occurs when an organisation exaggerates or misrepresents its environmental or sustainability efforts to appear more environmentally responsible than it actually is. Much like financial reporting, ESG data should be scrutinised through a third line of defence mechanism (internal audit) and also verified by external third-party auditors. As per the CSRD all sustainability reports issued from 2025 (2024 reference year) will be obliged to initially have a limited third-party assurance. The limited assurance will shift to reasonable assurance at a later stage. The newly published IFRS Sustainability Disclosure Standards, once adopted by regulators, will also help ensure more robust and consistent reporting. Another factor in preventing "Greenwashing" is ethical leadership – company management should demonstrate ethical leadership by aligning actions with stated ESG commitments and principles.

Q: Is the current regulatory framework robust enough to ensure enforcement, transparency, and comparability?

A: The regulatory framework is evolving, and it is important to note that regulations related to ESG are subject to change, with global geopolitical developments affecting regulations. In general, ESG regulations vary significantly by region and jurisdiction, leading to a lack of global harmonisation. This makes it challenging for multinational companies and investors to comply with and compare ESG requirements across different markets. The enforcement of ESG regulations is often inconsistent, and penalties for non-compliance are often limited raising questions about the effectiveness of regulatory oversight.

Since regulatory environments are dynamic and subject to change, it's essential to stay updated on the latest developments and regulatory changes in the ESG space to assess the current robustness of the framework accurately. Organisations should also consider voluntary standards and best practices to enhance their ESG reporting and disclosure efforts in the absence of comprehensive regulatory standards.

Q: Do you see the rapid evolution of AI affecting ESG management and reporting?

A: Definitely, however, while AI offers significant opportunities for enhancing ESG management and reporting, it also comes with challenges, such as data privacy, bias, and ethical considerations. Therefore, organisations need to implement responsible AI practices and ensure that the use of AI in ESG aligns with their sustainability and ethical goals.

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