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Designing and running an effective ESG and data operating model that aligns compliance requirements with the strategic use of ESG data has proven to be a major headache for many of our clients. According to Workiva’s latest ESG Practitioner Survey, 79% of respondents agree that verifying ESG data within their company’s reporting processes is challenging. These challenges include a lack of clarity in defining ESG strategy and identifying material factors, complexities in data management, difficulties in integrating ESG considerations into business processes and effectively engaging stakeholders. Additionally, the variety of standardised reporting frameworks and inconsistent data definitions across organisational units further complicate the alignment process. This blog post explores typical challenges organisations face and provides insights into the solutions and services available to address them.
Discover the blog series
Effective internal alignment
A key challenge many organisations face is the lack of internal stakeholder alignment, resulting in unclear processes, governance, and poorly defined roles and responsibilities for ESG data management. In the afore-mentioned survey, 81% of respondents reported difficulties with cross-departmental collaboration. ESG capabilities are often siloed across the organisation, typically concentrated within IT, which hampers the ability to serve broader business needs. As a result, ESG efforts and data initiatives frequently become disconnected from the overall ESG strategy, limiting their role to mere compliance reporting.
A starting point for internal alignment often is choosing the correct organisational archetype. While for some companies a decentralised model works best, others decide to set up their ESG (data) capabilities in a hub-model or create centralised structures to facilitate standardisation and streamlined processes. This thought-process usually works best with an ambition that exceeds the need for mere regulatory compliance and revolves around unleashing the full potential of ESG-data insights.
"A well-structured data operating model should prioritize clear organisational design, processes and workflows together with roles and responsibilities accountability, and (data) governance that glues everything together."
As ESG data is part of a corporation’s (annual) non-financial report, the CFO increasingly plays a pivotal role. Acting as a bridge between business and IT, the CFO ensures that ESG data initiatives aren’t siloed but instead are integrated into the organisation’s overall strategy. In doing so, the CFO draws on their own expertise in financial reporting, risk management and resource allocation. ESG reporting increasingly affects financial performance, investor relations and long-term sustainability efforts, as nearly 40% of our Global CSRD Survey respondents (strongly) agree upon. The CFO is uniquely positioned to oversee ESG reporting, making sure that it not only meets regulatory requirements but also informs strategic decision-making.
ESG roles
In larger global organisations, clearly defined ESG data roles, bringing a blend of data and ESG skills, are key to maintaining data integrity and strategic alignment. A typical ESG reporting team should include:
Disclosure Manager (central)
Handles inquiries, assures all necessary information (from requesting parties) is there, checks for available data to craft a report. In case data is not available: Positions ESG data requests with Data Manager. Once data is available, this role crafts the report and after obtaining internal approval, discloses it to the requesting party (e.g. regulatory body).
Data Manager (central)
Manages the ESG data repository (preferably a single source of truth for ESG reporting). Translates disclosure management requirements (for non-available ESG datapoints) into 'data requirements' that they will then hand over to affected data custodians (at regional levels). As soon as data is collected (incl. validation) and aggregated, this role once more validates the data (for completeness and adherence to specific methodological requirements tied to the related framework) and calculates metrics, that are then once more validated with the Data Custodian.
Data Custodian (local/regional)
Cascades data requirements to site-level (if company owned) or 3rd party providers. Aggregates and validates (accuracy, consistency, quality of data) collected data and hands it over to Data Manager.
Data Collector (local)
Responsible for collecting data (or in charge of automated data collection performed on recurring basis).
These roles form a cohesive structure, collaborating with the rest of the business driving ESG data initiatives. They align ESG data with business goals and promote transparency, compliance and informed (strategic) decision-making.
Complexities in data management and (data) architecture
Following its relative novelty compared to financial reporting, collecting, analysing and reporting on ESG data is complex and resource intensive. Data is often incomplete, inconsistent or of varying quality due to the multitude of sources. This limits the usefulness of ESG data as a strategic asset for organisations. To overcome this challenge, it’s highly recommended that companies invest in robust data management systems and processes. For example, a company that wants to track its carbon emissions might collect data from various sources such as energy bills, transportation records and production data. As corporations grow bigger and potentially acquire other companies, this data needs to be collected from various standalone reporting units, countries and/or production facilities. However, data may be incomplete, inconsistent or even completely unavailable, making it difficult to get an accurate understanding of the company’s carbon footprint. Implementing standardised data management practices, such as establishing data collection mechanisms and implementing data governance frameworks, can be beneficial. By deploying technology solutions that streamline data management processes, ESG data availability, accuracy and reliability can be enhanced. For instance, the use of automated data collection tools can help gather data from different sources more efficiently, reducing the risk of errors and inconsistencies.
Organisations need to establish a robust data architecture that can accommodate the diverse requirements of ESG data into its operating model. This sets the standard for collecting data, establishing governance frameworks and developing strategies for integrating ESG data with existing systems. Companies collect ESG data from various sources like sustainability reports, employee surveys and financial statements. But aligning these different data sources with the company’s ESG strategy and seamlessly executing data collection and integration can be difficult.
Building an ESG (data) architecture around existing architecture helps organisations to retain what works well and prevents (re)inventing the wheel. By developing a centralised data repository that consolidates ESG data from these various sources, companies can ensure seamless integration and analysis with their existing data architecture. Modelling ESG data into a unified data model allows for a comprehensive understanding of cross-functional aspects of ESG data. This enables organisations to identify relationships and patterns within the data that may not be apparent when looking at individual data sources in isolation.
Implementing scalable infrastructure and deploying cloud-based solutions can also ensure flexibility in handling the increasing volume and complexity of ESG data. This allows organisations to efficiently store, process and analyse large amounts of data to derive meaningful insights and make informed decisions based on real-time ESG insights.
Reporting frameworks and standards
The wide variety of standardised reporting frameworks and inconsistent data definitions across industries make it challenging to compare and benchmark ESG performance. Companies may find it difficult to choose the most suitable reporting framework for their specific needs on a global level. To overcome this challenge, organisations should adopt globally recognised reporting frameworks such as the Global Reporting Initiative (GRI) or Sustainability Accounting Standards Board (SASB). These frameworks provide a structured approach to ESG reporting, ensuring consistency and comparability across companies and industries. Thanks to a collaboration between GRI and EFRAG (the European Financial Reporting Advisory Group), adhering to the Corporate Sustainability Reporting Directive (CSRD) will result in strong alignment with GRI standards. Comprehensive ESRS-GRI data point mapping enhances the credibility and transparency of your global ESG reporting.
Conclusion
In conclusion, defining an ESG data operating model is essential in solving the challenges related to ESG reporting. It provides clarity, establishes robust data management practices, facilitates integration into business processes and promotes stakeholder engagement. By implementing such a model, organisations can effectively align their ESG strategy with execution, drive sustainable practices and meet stakeholder expectations in a transparent and accountable manner. PwC can help to streamline all these challenges and support you in implementing your ESG (data) operating model.
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