The global investment landscape is undergoing a significant transformation, with environmental, social, and governance (ESG) factors becoming central to contemporary investment strategies. This shift presents a complex challenge due to the varied methodologies used to interpret ESG data. For example, differing approaches to assessing environmental impact, such as water stewardship, can lead to conflicting ratings because some analyses focus on total water volume, while others consider regional stress and management practices.
Asset managers must navigate these complexities to make informed investment decisions. Fragmented ESG data can result in misinformed decisions, increased regulatory scrutiny, and a loss of investor confidence. Therefore, standardizing ESG data is essential for both performance and compliance.
Growing investor demand for transparency and regulatory pressures for comprehensive ESG reporting have made robust and reliable ESG data paramount for asset managers. According to Bloomberg Intelligence, global ESG assets under management are expected to exceed $40 trillion by 2030, underscoring ESG's pivotal role in modern portfolio construction. A BNP Paribas survey of 420 institutional investors revealed a strong trend of ESG integration: 51% of respondents have incorporated ESG expertise and data into portfolio management and investment decisions, with 57% of these respondents based in the APAC region. Additionally, 42% of respondents have integrated ESG into risk management, and 37% are focusing on monitoring ESG compliance. These results demonstrate that institutional investors are mobilizing capital towards investments with measurable positive impacts alongside financial returns.
As investors increasingly prioritize investments that yield both financial returns and positive societal impact, ESG performance has become a key determinant of investment value. However, the lack of standardized data remains a critical barrier, hindering asset managers' ability to accurately evaluate sustainability and compare investment opportunities.
Key Challenges for Asset Managers
- Unreliable data: The reliability of ESG data is a significant issue for asset managers. Variations due to a lack of data integrity and self-reported figures without third-party verification result in unreliable data. This poses major challenges and impedes asset managers' ability to conduct accurate performance evaluations across companies and sectors. In the BNP Paribas survey, 71% of respondents cited inconsistent and incomplete data as a major barrier to ESG adoption, highlighting the challenges in ESG data management.
- Unreliable data: The reliability of ESG data is a significant issue for asset managers. Variations due to a lack of data integrity and self-reported figures without third-party verification result in unreliable data. This poses major challenges and impedes asset managers' ability to conduct accurate performance evaluations across companies and sectors. In the BNP Paribas survey, 71% of respondents cited inconsistent and incomplete data as a major barrier to ESG adoption, highlighting the challenges in ESG data management.
- Inconsistent ESG ratings: The lack of a standardized ESG rating framework result in diverse coverage and varying granularity of metrics used by different providers across various ESG topics. This complicates the effective evaluation and integration of ESG factors into investment strategies by creating critical ‘blind spots’ within sustainability assessments that obstruct accurate comparative analysis and potentially misalign investment strategies. These blind spots can lead to investments in companies with hidden ESG risks, resulting in reputational damage, regulatory fines, and diminished returns.
- An OECD analysis, Behind ESG ratings: Unpacking Sustainability Metrics, published in 2025, of more than 2,000 ESG metrics across eight rating products highlighted significant inconsistencies. While core areas like corporate governance, business ethics, and environmental management showcased the use of more than 20 metrics on average, critical areas such as biodiversity and land use had fewer than five. Moreover, 11 essential topics were absent from at least one rating product. This divergence was evident in metric ranges: corporate governance varied from 4 to 113 metrics, environmental management from 1 to 43, and GHG emissions from 1 to 47. This lack of uniformity severely hinders accurate ESG comparisons. x
- Evalueserve’s research also shows inconsistencies in how ESG data is evaluated by different providers. Some rely solely on publicly available data, while others incorporate proprietary company data. Furthermore, the focus of analysis often varies—some providers prioritize quantifiable metrics like emissions, while others examine intensity data. A prime example of this divergence can be seen in the evaluation of climate commitments. Some providers mandate dedicated environment policies, while others accept statements within broader CSR reports that outline specific impact reduction efforts.
- Multiplicity of Reporting Frameworks: Navigating the complex landscape of multiple, often conflicting standards, such as GRI (Global Reporting Initiative), SASB (Sustainability Accounting Standards Board) and TCFD (Task Force on Climate-related Financial Disclosures) leads to inconsistencies in reported ESG performance, severely compromising comparability. With over 600 sustainable investment reporting standards currently in use, the need for harmonization or a unified reporting methodology is increasingly evident. While the GRI has achieved widespread adoption and is frequently supplemented by SASB and TCFD disclosures, relying solely on one framework does not adequately meet the diverse expectations of stakeholders. Consequently, many companies feel compelled to adopt multiple frameworks, resulting in redundant reporting, increased potential for confusion and errors, and significantly higher costs.
- Dynamic ESG risks: Legislative changes, technological advancements, and shifting social movements continuously evolve ESG risks. Asset managers need flexible solutions to adapt to these changes. A 2023 Bloomberg survey of about 200 European financial market participants revealed that 41% of respondents considered constantly evolving data a leading concern, highlighting the need for agile ESG data solutions.
- Regulatory requirements: Asset managers face significant challenges in harmonizing ESG data due to varying standards and increased reporting complexity, which diminishes the comparability and reliability of ESG metrics. The 2024 EY Global Corporate Reporting Survey, involving over 2,000 finance leaders and 815 institutional investors globally, highlighted deep concerns about sustainability and transparency. When asked about the cost and complexity of complying with new reporting regulations and standards, 55% of finance leaders indicated that the costs would be substantial and 44% stated that the process would be highly complex.
Given the critical challenges, such as the lack of a uniform ESG rating system, data reliability issues, fragmented reporting frameworks, evolving regulations, and dynamic risks, asset managers face significant operational and strategic hurdles. These challenges highlight the urgent need for ESG data harmonization. Standardized data is essential for asset managers, as it will enable them to overcome these obstacles and unlock substantial benefits, including data-driven investment strategies, enhanced risk management, optimized reporting, and improved operational efficiency.
Benefits of Standardized ESG Data
- Smarter investments: Standardized data allows asset managers to make precise comparisons, informed decisions, and optimize portfolios. For example, they can accurately compare the carbon intensity of companies within the same sector and select the lower-emitting option to align with climate goals.
- Enhanced risk management and portfolio agility: Comprehensive ESG data supports strategic risk management, refined fund evaluation, and agile portfolio adjustments. Standardized data enables early detection of emerging risks, allowing for proactive adjustments to portfolios.
- Streamlined compliance and reporting: Consistent ESG data simplifies compliance and reporting processes, reducing penalties and enhancing investor trust. Streamlined compliance processes minimize the resources needed for reporting.
- Improved operational efficiency: Data harmonization reduces the effort and cost of data cleansing, allowing asset managers to focus on strategic priorities.
- Advanced data analytics: Harmonized data facilitates sophisticated analysis, including trend identification and predictive modeling, providing robust insights into ESG performance and financial impact.
- Better innovation and investor engagement: Robust ESG data enables asset managers to enhance strategies, develop effective risk mitigation processes, and create innovative products. These improvements increase investor appeal and provide a competitive edge.
To fully capitalize on these core benefits and effectively address the outlined challenges, asset managers can implement a structured approach. The following step-by-step guide provides a practical framework for building a comprehensive ESG data strategy:
- Step 1: Establish a strategic ESG framework
- Understand stakeholders’ needs.
- Align executive support for ESG integration.
- Define clear investment objectives using industry standards.
- Prioritize material ESG factors.
- Ensure alignment with the firm's investment philosophy.
- Step 2: Effectively source and manage ESG data
- Develop a scoring system to evaluate potential data vendors based on their ability to provide standardized data, disclose methodologies transparently, and offer clear data lineage for accountability.
- Conduct internal skills assessments to determine if data normalization can be handled internally or if external vendors are required.
- Select suitable platforms capable of integrating and normalizing ESG data, considering custom development if necessary.
- Step 3: Develop data models and automated pipelines
- Collaborate with industry peers to design standardized data models with consistent fields and definitions, incorporating version control and feedback loops for continuous improvement.
- Implement automation tools to streamline data processing and ensure consistent flow.
- Establish a robust data governance framework with regular audits to maintain data integrity, accuracy, and transparency.
- Step 4: Establish a unified taxonomy and central repository
- Engage with regulatory bodies to create a clear, industry-aligned taxonomy.
- Designate a team to manage a centralized repository for data consistency and accessibility.
- Develop protocols to normalize and standardize data before integrating it into investment workflows.
- Step 5: Ensure data quality and analysis
- Develop a comprehensive data quality control checklist for robust cleansing and validation.
- Engage data analysts to create advanced tools for trend identification and performance benchmarking.
- Generate clear and concise reports with data visualization to facilitate meaningful comparisons.
- Step 6: Implement robust governance and regulatory adaptation
- Establish and enforce data governance policies through a dedicated committee, ensuring data accuracy, security, and ethical use.
- Implement a regulatory monitoring system to adapt to evolving ESG requirements and minimize compliance risks.
- Actively participate in industry forums to contribute to standardized metric development and innovation.
- Step 7: Foster technological advancement and continuous improvement
- Leverage AI for data analysis and performance prediction.
- Regularly review strategies with stakeholder input to stay aligned with evolving ESG practices.
- Ensure better investment decisions, risk management, and regulatory compliance through data harmonization.
In conclusion, ESG data harmonization is no longer a 'nice-to-have' but a strategic imperative for asset managers seeking to unlock significant long-term value through enhanced portfolio performance and risk-adjusted returns. This harmonization is crucial to mitigate the increasing risks associated with regulatory scrutiny and meet the evolving demands of investors who now prioritize transparent and impactful sustainable investments.
To fully capitalize on these benefits, asset managers should conduct a comprehensive audit of their current ESG data infrastructure. They should also actively engage with industry peers in standardization initiatives and consult with specialized ESG data experts to implement robust data management and analysis systems. By embracing these steps, asset managers can build a foundation for sustainable growth and effectively navigate the complexities of the modern investment landscape.
