Navigating data complexities in ESG Reporting
There is a growing commitment to sustainability in the UK and worldwide as investors, stakeholders and regulatory bodies scrutinise corporate performance more closely than ever before. Long-term value creation is becoming increasingly important when assessing an organisation's performance and prospects, taking precedence over short-term financial gains.
Companies that report on Environmental, Social, and Governance (ESG) metrics more effectively are better positioned to identify and address financial risks tied to climate change, workforce, corporate governance, and other critical areas. Insights drawn from ESG data enables business leaders to spot new opportunities for more sustainable business practices.
To meet these changing expectations, organisations are integrating ESG performance with financial reporting, despite the significant challenge bringing these two streams of information into a cohesive narrative.
The office of Finance is already responsible for ensuring the integrity and reliability of financial data, but creating a comprehensive picture of a company’s risk landscape (including ESG considerations) presents several obstacles. Having identified ESG metrics material to your business, the next step is to enable collection of ESG data from source, so Finance teams can play their role in ESG Reporting effectively.
The ESG data dilemma
The leading challenges of integrating ESG with financial reporting lie in the complexity of ESG data management:
- Data collection is inherently time-consuming and resource-intensive;
- Processes for controlling and governing ESG data are often ineffective; and
- Systems and solutions to facilitate ESG data integration are typically inadequate.
With the role of Finance in ESG Reporting changing quickly, Finance teams are struggling to make sense of what ESG data is available, where it is stored, and how they consolidate it for analysis and reporting.
Collection & consolidation
Disparate data sources and silos – Gathering accurate and relevant ESG data from across the enterprise can be a tremendous challenge. A recent BARC study showed 42% of organisations identified too many different data sources as their biggest ESG Reporting challenge. From tracking down data on fuel usage and production volumes to requesting operational greenhouse gas emissions from key suppliers, ESG data is usually fragmented across a diverse range of sources. ESG metrics are gathered from internal departments, supply chains, third-party reports, and regulatory bodies. In many cases, there are even multiple sources for the same metric. For example, calculating annual employee turnover rate can involve data from HR systems, payroll records, and benefits or pension records. Collecting and consolidating such large volumes of data creates an internal overhead, especially where companies have limited capacity in resources and data infrastructure.
 |
Implement a centralised ESG data management platform to act as a single repository where all ESG-related data is collected, standardised, and stored. By providing a single source of truth, this will remove some of the strain on your Finance team and ultimately streamline the group reporting process. |
Inconsistency of metrics – The absence of universal standards and reporting criteria for ESG factors leads to inconsistencies in how data is collected across subsidiaries, departments and even functional teams. One department may use different metrics or definitions from another to collect the same data, making it difficult to compare data across the group. As an example, divisions may measure greenhouse gas emissions differently, using kilograms or metric tons of CO2 emissions.
|
Establish a common framework, such as adopting the GHG Protocol, and use consistent conversion factors to improve comparability of metrics. This will enable Finance teams to extend existing financial planning & analysis models to include ESG metrics for analysis and forecasting. |
Data accuracy and reliability – Ensuring the accuracy and reliability of ESG data poses another challenge given the variety of sources, and lack of verification and quality controls. The 2024 Bloomberg European ESG Data Survey highlighted data quality as another biggest challenge facing organisations in ESG Reporting. Incomplete, outdated, or erroneous data can severely impact the credibility of ESG reports. Many companies still rely on manual data entry for collecting ESG information, which increases the risk of transcription errors or incomplete records.
| |
Implement robust controls and validation checks through data entry workflows to maintain accuracy and consistency. This may involve cross-comparison with other data sources, creating validation workflows, and establishing clear data governance practices to ensure a reliable and consistent ESG reporting process. |
Timeliness of data – Timely ESG data is crucial for integrating ESG metrics into financial reports and ensuring accuracy of financial statements. However, it is often difficult to tie in ESG data because key information is not available from third-party suppliers before accounting journals are closed at the end of the month. For example, final water or power usage data may not be accessible ahead of month end, necessitating adjustments after journals are posted.
|
Establish real-time (or near-real-time) data collection processes to ensure ESG data is available when needed for financial close activities. This can involve IoT sensors providing automated data feeds to minimise delays and improve accuracy. Where this is not possible, set a schedule for ESG data submission that aligns with financial reporting timelines to gather and consolidate ESG data before finalising the close. This minimises the need for post-close journal entries and ensures financial statements accurately reflect the current reporting period’s activities. |
ESG data processes and controls
Lack of clear ownership – Establishing clear ownership of ESG data within an organisation can be difficult, especially given the wide range of data sources involved. Without defined roles and responsibilities or a clear governance structure, the absence of accountability quickly leads to inconsistencies and errors. Unlike financial data, which typically has assigned owners, ESG data often falls into a grey area where it is unclear who is responsible for overseeing its collection, storage, and reporting.
|
Designate specific individuals or teams as owners of certain types of ESG data. These owners will be responsible for the accuracy, completeness, and timeliness of the data they manage. By clearly defining who is responsible for certain datasets, organisations can ensure a co-ordinated effort founded on accountability, reducing the risk of inaccurate data. |
Inadequate data controls – Data can quickly become outdated, inconsistent, or subject to manual manipulation. The absence of robust controls over ESG data is guaranteed to lead to inaccuracies and discrepancies. Data quality management is essential for ensuring the reliability and consistency of ESG reporting and for aligning ESG with the financial close and statutory reporting cycle.
|
Establish rigorous validation processes and define standardised data collection protocols to ensure data quality. This involves setting clear procedures for gathering, storing, and processing ESG data, as well as implementing standardised workflows that guide users through the process of submitting, validating and reviewing data to improve accuracy and consistency in the data upload process. |
Inconsistencies in manipulating source data – There is a significant lack of visibility when different departments within an organisation use their own methods to process or adjust raw ESG data. For example, energy consumption data might require normalisation to account for seasonal variations, or carbon emissions data might need conversion from various units to a standard format. When departments manipulate data in isolation, without a common framework, it can lead to inconsistencies that conflict with accurate reporting.
|
Establish a standardised framework for processing and adjusting ESG data to improve the reliability of ESG reporting. Solutions that automate the extraction and transformation of ESG data from source systems will minimise manual manipulations and ensure reliable numbers for financial analysis and reporting. |
Data integration challenges
No single source of truth – Traditional finance systems are generally designed for specific accounting processes and may not accommodate the broad spectrum of ESG data. When attempting to access data from other departments in the organisation, Finance teams often face hurdles. Key information is stored in siloed systems, typically in large Excel spreadsheets, that requiresignificant effort to locate, review and extract data that meets the need of ESG reporting. This disconnect between ESG and financial reporting, and the lack of a single source of truth, leads to inconsistencies and inaccuracies between datasets.
|
Invest in cloud-based technology that serves as a single source of truth for both financial and ESG data. This will break down barriers to accessing the necessary data and improve group-wide collaboration on ESG initiatives. Automating data collection through cloud technology will facilitate the collection of more reliable ESG data. |
Incompatible source systems – ESG data can be stored across multiple systems, many of which are not designed to integrate with one another or with legacy financial platforms. This can lead to technical challenges when trying to connect these disparate systems. Legacy systems used for financial reporting may also lack the flexibility to accommodate new ESG data structures.
|
Automate data extraction and minimise manual data handling using a solution designed to integrate with multiple source systems and file formats. Integration with source systems will reduce the risk of errors when pulling ESG data into a single source of truth. |
Complexity of data structures – ESG data comes in a wide variety of formats, from qualitative narratives and policies to quantitative metrics and statistics. Capturing ESG data against these varied data structures alongside financial data can be complex. This complexity necessitates having a data model that can support the intricacies of financial reporting and ensure local relevance to ESG data collected at source.
|
Bridge the gap between unstructured ESG information and structured financial data by investing in a solution with a suitable data model architecture. |
Addressing the challenges
Effectively navigating the complexities of integrating ESG and financial data requires a strategic and systematic approach to ESG data management, beginning with the initial data collection. Establishing a clear governance structure, refining data collection processes and adhering to best practices for managing financial data will enhance the accuracy and reliability of your ESG data. Additionally, aligning ESG efforts across the enterprise will improve data integrity and strengthen reporting capabilities.
Our key takeaway: Before diving into defining KPIs and creating your ESG reports, spend some time getting your data in good order by implementing robust data management processes to capture the relevant ESG data in the right format.
Start the conversation…
At Concentric, we will help you map out a route to unlocking the full potential of integrated reporting for driving long-term value and impact. Find out more about how we can support you on your ESG reporting journey with a free ESG Solution Discovery!
This session involves a short collaborative workshop designed to explore your organisation’s ESG reporting needs:
- Scope of your ESG and sustainability data
- ESG data collection processes
- How ESG data is controlled
- Current approaches to reporting the impact of emissions reduction initiatives
- Alignment of ESG and financial reporting
Complete the form to request a no-obligation discussion:
For more information, contact our expert consultants via email
