Challenges in ESG Report Automation

In this blog, KPMG explains the challenges in ESG Report Automation with a focus on data.

The potential consequences of unreliable sustainability data include reputational damage to a company, and possibly to the directors responsible for the disclosures, along with legal ramifications tied to inaccurate reporting or perceived greenwashing. To navitage the 'sustainability data challenge' CIOs, data managers, and corporate sustainability teams can undertake several targeted steps.

Scalability
Understanding your company’s sustainability strategy and the new sustainability reporting requirements is essential for integrating sustainability data effectively into company IT processes. However, the first significant challenge to address is the sheer volume and complexity of ESG data, which companies struggle to collect, analyze, and report on efficiently. The extensive and diverse range of metrics used  also reduce the scalabilty of the data. 
Continuously aligning a  data management strategy with emerging regulations, frameworks, and standards  and ensuring readiness for future changes is challenging. The evolving nature of ESG standards and reporting frameworks mandates ongoing updates to data collection and reporting processes for compliance and relevance. This complicates automation, potentially necessitating system adaptations to meet varying reporting requirements.

Reliability
Also looking at the  desire of a lot of companies to have CRSD report automation, this is a demanding task of disclosing more and more data on sustainability KPIs. However, many companies lack the know-how and resources to comply with the directive's demands which usually results in collecting and managing ESG data in a cycle of inefficient one-off “task-force” campaigns, rather than using a systematic approach. 

Auditability 
Collecting and managing ESG data manually in spreadsheets makes reasonable assurance from an auditability perspective nearly impossible, meaning automating the collection process is paramount.  However, the necessary data needed for such insights is often scattered across various sources within and outiside an organization making systematic collection difficult.

We foresee three critical success factors to automate ESG data gathering, management, and reporting: 

1. Data type-specific gathering process: 
   There is no one-fits-all approach. Various types of data need to be clustered and the structure of the data gathering, and management should be designed accordingly. Once companies determine the necessary reporting information, they must invest in sustainability data management systems. These systems should not only fulfill immediate needs but also be adaptable for future reporting and performance management requirements. Many companies, previously reliant on Excel spreadsheets for sustainability data reporting, should explore automated solutions capable of producing auditable and assured data.
   
   
2. Governed data platform: 
   A robust and structured data platform is crucial. Microsoft's Cloud for Sustainability offers the capabilities to  record, store, and manage the vast heterogeneous data necessary for the respective stakeholders. In addition to the actual data, evidence to track and document activities and legacy data needs to be available on demand in a structured way. At the core of Microsoft Cloud for Sustainability is the Common Data Model, which provide customized tech solutions running on the Azure platform.
   
   
3. Correct interpretation and visualization from an ESG perspective:
   The third critical step is to correctly interpret and report the data. After overcoming the challenge of having all necessary data available, different regulatory reports and public stakeholders need to be addressed correctly. This challenge is best tackled with a diverse team comprised of an ESG specialist, someone who is responsible for the collected data, and a reporting expert. New Microsoft AI solutions can also be used for interpretation, conversational interfaces, and many other solutions.

The scalability challenge arises from the extensive and diverse nature of ESG metrics, making it difficult for companies to effectively gather, manage, and integrate this data into their reporting processes. Moreover, the evolving nature of ESG standards requires continuous updates to stay compliant, complicating the automation process.

Ensuring the reliability and accuracy of ESG data is another hurdle, as data quality issues can undermine the credibility of the reports. Manual data collection processes often lack standardization and may result in inconsistencies and gaps.

Auditability becomes challenging when ESG data is managed manually in spreadsheets, making reasonable assurance difficult. The automation of data collection is crucial, but the scattered nature of data sources within and outside organizations poses a significant obstacle.