ESG & Data Governance

With the transformation of the global economy and society, the roles and responsibilities of enterprises are undergoing profound changes.The growing public concern for sustainable development has prompted businesses to shift from a sole focus on profit to a comprehensive value assessment. In this context, Environmental, Social, and Governance (ESG) criteria have emerged as key indicators of a company's overall strength and long-term value, highlighting the need for businesses to actively shoulder social responsibilities while pursuing economic benefits and achieving harmony with the environment.

The digital era presents new opportunities for businesses, where data becomes central to operations and decision-making. Data governance, which ensures data quality, security, and compliance, is increasingly being emphasized by companies.

ESG and data governance are interrelated, with the former providing a value orientation for the latter, and the latter serving as a crucial means to achieve ESG objectives. This discussion delves into their intrinsic connections, offering new perspectives for businesses to maximize sustainable development and social value.

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What are the main data dimensions of ESG?

Mr. Zhu Zhen: Understanding this question starts with the basic concept of ESG, which assesses three value-based factors. Specifically, it comprehensively evaluates the impact of business and investment activities on the environment (natural resources, greenhouse gas emissionsand climate, environmental risks and opportunities), society (human capital, products and services, customer engagement, social involvement, etc.), and corporate governance (corporate governance, corporate behavior, business stability). To quantify these specific dimensions, management tasks evolve into data work, leading to data demands for ESG assessment reports covering company information, global news media, judicial and government information, company reports and websites, third-party certification data, and self-assessment on ESG. As actual operations require obtaining data from public sources for cross-validation and supplementation to support ESG reporting, there is a need to gather from both external and internal sources like annual reports, financial statements, and legal disputes. This diverse range of data sources and formats poses significant challenges to effective ESG evaluation and the establishment of corporate data governance systems, hence the demand for ESG data governance.

What are the assessment methods and application scenarios of ESG?

Mr. Zhu Zhen: They include innovative models such as AI big data, the integration of blockchain and digital assets, cryptocurrencies, and new paradigms in the securities industry, with the greatest fusion being data assets, including innovations in cryptocurrencies, digital RMB, and digital securities.

Currently, major ESG assessment products each have unique features. For instance, Dun & Bradstreet possesses the world's largest enterprise database and ESG assessment database; S&P can cover both listed and non-listed companies, but its scope is generally limited to tens of thousands of enterprises. Ecovadis has developed a specialized system for sustainable supply chain assessment and rating, while Morningstar acts as a data analysis aggregator. Overall, these ESG assessment methods primarily involve distributing self-assessment questionnaires to companies and supplementing with third-party data to form a comprehensive aggregated dimension, constituting a complete ESG assessment framework. CDP stands out with a two-stage process of sending questionnaires to companies and allowing investment institutions to purchase data from CDP, along with providing questionnaires on emission reduction and energy conservation for climate change, forests, and water projects.

The application scenarios of ESG encompass finance, government regulation, and supply chain management.

In finance, ESG investments can be applied to project or equity investments, where financial institutions provide equity investment or creditsupport to projects or companies meeting ESG rating standards, resulting in products like green bonds. Index investing is also common, where funds are established around an ESG investment theme, with fund index fluctuations guiding investment decisions. Therefore, ESG is a crucial topic for listed companies.

In government regulation, ESG facilitates the establishment of efficient and unified national carbon asset trading markets by entities like stock exchanges, securities regulatory commissions, and banking regulatory commissions. It underpins government support and standardization of the carbon asset and green credit market, as well as the sustainable development and green investment and financing projects of listed companies. Future international transactions may hinge on carbon emissions quotas and carbon emission rights as mechanisms for trade or taxation, indicating huge prospects for development.

In supply chain management, ESG enables each part of the supply chain to demonstrate corporate social responsibility, allowing potential partners to prove adherence to industry best practices in worker safety, environmental protection, and business ethics. This fosters transparencyand mutual constraints among different entities within the supply chain. Notably, the Ten Principles of the UN Global Compact align with parts of the ESG framework, with human rights and labor standards corresponding to social responsibility, anti-corruption to corporate governance, and direct relevance to the environment, although these principles are not quantified. Thus, ESG can be seen as a more quantifiable set of global principles and audit frameworks for sustainable supply chain development.

Data Governance's Value in ESG Assessment: How is it Demonstrated?

Mr. Zhu Zhen provides insights into how data governance contributes to the value of Environmental, Social, and Governance (ESG) assessments. He outlines several key aspects:

1.Sources of ESG Assessment Data: In managing ESG reports, companies deal with diverse and varied data sources. These include ESG dataaggregators that transform non-standard data into usable information, facing the challenge of correlating raw data from news and social media to specific businesses; publicly available open data, which is most accessible; and ESG index scores and analyses provided by rating agencies for competitive intelligence. This diversity highlights significant room for data governance in ESG data sourcing.

2.Data Governance Work Required for ESG Assessments: Central to this process is data processing and quality assurance, achieved through tools like score sample databases, web scraping, NLP analysis, and master data management. This converts multi-source, heterogeneous data into standardized formats. The core tasks involve defining and standardizing data (e.g., greenhouse gas emission scope, key indicators, representation of sustainable development goals), managing master data (unifying IDs for third-party data consolidation and ongoing monitoring), ensuring compliant storage and retrieval (leveraging cloud computing, ETL, and managing cross-system, cross-border access), providing frequent updates and visualization, and maintaining compliance across borders. Thus, ESG assessment workflows inherently encompass standard data governance tasks.

3.Technologies for ESG Assessments: ESG assessments must also adhere to a data governance framework, encompassing service evaluation, authorization, standards, and strategies (S), capability enhancements in business adjustment, IT integration, and active monitoring (C), output quality (O), operational practices like business governance and data quality management (P), and awareness promotion, communication, training, and tool deployment (E). Key technologies such as NLP, cloud computing, and AI models are employed for data authenticity checks, registration, interaction, and privacy-preserving computation, data spaces, and metadata management for compliance.

4.The Relationship between ESG Assessments and Data Governance: ESG assessments face challenges including inconsistent data, low standardization, dispersed data sources, lack of transparency, and limited integration, with the first three being the most prominent. Data governance aims to address these issues by ensuring compliance, timeliness, precision, and standardization.

5. ESG Assessment as a Driver for Digital Transformation: The need for ESG assessments prompts companies to collect and process data, often revealing inadequate digital infrastructure. This leads to digital transformation efforts, including establishing unified data dictionaries, integrating multiple data sources and systems, building ESG data lakes, and developing ESG analytics platforms, all of which can be integrated into broader digital transformation initiatives.

What are the key points and value of CDOs in ESG assessment work?

Mr. Zhu Zhen: ESG assessments actually provide a great opportunity for data and technology professionals to advance data governance, as solidifying data infrastructure and refining data governance systems through data governance can facilitate the provision of higher quality, more accurate, and representative ESG information to both internal company departments and external regulatory bodies.

On one hand, ESG data constitutes a part of a company's data assets. CDOs should leverage ESG assessment work to drive business growth, with value typically manifested through several avenues: enhancing current operational efficiency, avoiding risks and losses, transforming data services into monetizable products and services for additional revenue, boosting customer satisfaction to enhance brand reputation and intangible assets. Once data ceases to be solely a cost-generating factor, the board of directors, investors, and financial regulators will change their perspective on data, potentially even using it as collateral for financing, thereby realizing the monetization of data. This is the key point I wish to emphasize today – that whether CDOs or other data technicians, all should adopt a view of data from an asset perspective and actively work towards incorporating data assets on the balance sheet.

On the other hand, to achieve these objectives, CDOs need to focus on the following key points in ESG assessments: data comparability and standardization, clear disclosure scopes and transparent methodologies, temporal stability of data, integration with existing ESG data, dataaccessibility, strategic approaches, oversight by senior management, robust execution, and verification based on fundamental certification standards. Under this context, CDOs can contribute by fueling digital transformation through excellent data governance, establishing a solid foundation for the company's ESG evaluation. This is specifically realized through streamlining data application scenarios, further advancing data ownership clarification, sovereignty construction, data quality assessment and optimization, data compliance, and data supply chain construction, culminating in the development of data tech products that empower through data technology.

How does data governance ensure the accuracy, consistency, and reliability of ESG data?

Mr. Zhu Zhen: Firstly, given the diverse sources of ESG data and the presence of varying standards across multiple institutions, data governanceinitially helps establish a unified data dictionary. This means defining within the group every data term, as well as the scope of different concepts under each dimension, creating a coherent set of definitions. Based on these definitions, we can proceed with data collection.

Secondly, in terms of data integration and aggregation, not only technical means of data governance are required but also compliance assurance. This is because the scope of collectible data, its ownership rights, and the legality of the collection process must adhere to the full lifecycle management of data.

Lastly, for the seamless fusion of data, mastering data master management is crucial. Currently, in the field of data governance, the introduction of ID identification technologies significantly enhances the efficiency of this task. By employing these data governance measures, we ensure the accuracy, consistency, and reliability of ESG data.

How does data governance help unify ESG evaluation criteria among different departments or stakeholders?

Mr. Zhu Zhen: To address this question, it's essential first to understand the differing perspectives and interests of various departments. For instance, when deciding on ESG evaluation criteria, the procurement department might focus on the company's performance in the 'E' ( environmental) aspect, as this could impact credit levels and financing capabilities through mechanisms like green credits, affecting investment behaviors throughout the supply chain.

Conversely, the purchasing department may prioritize the volatility of commodity prices during delivery and thus be less concerned about the 'E' in ESG rating systems, focusing more on corporate governance and social responsibility issues that could influence public opinion and disrupt product transactions. This is why large multinational corporations like Apple, Volkswagen, BMW, and Intel have each announced their own carbon neutrality targets for their supply chains in recent years.

The task of data governance is to create a unified standard system with consistent definitions, reducing data flow barriers between departments through the establishment of a standard terminology system. By mapping out unified lineage, different stakeholders gain a sharedunderstanding of the flow and value of various business data, facilitating the unification of their ESG evaluation criteria.

How can effective ESG assessments be conducted in the absence of high-quality data?

Mr. Zhu Zhen: First, let's clarify what constitutes high-quality data – it's data that has undergone data governance. Often, initial data governance or ESG assessment efforts begin with self-assessment, and if satisfactory results are obtained, there may then be a tendency to seek further evaluation from third-party data sources and assessment agencies.

When turning to third-party data, the issue of data quality arises. Additionally, data gaps can be encountered, such as companies not knowing their own carbon emissions during assessments. One solution here is benchmarking, researching comparable firms in terms of size, model, and industry to see if they have published relevant data for reference. However, discrepancies between standards and rating objectives can hinder useful comparisons; for example, the EU's CBAM (Carbon Border Adjustment Mechanism) significantly differs from China's "3060" carbon goals. Therefore, our recommended approach is to promptly establish a company's data governance system, addressing the root cause by collecting and governing data systematically. This tackles the issue of inadequate data governance head-on.

How does the CDO coordinate and manage the process of ESG data collection, analysis, and reporting?

Mr. Zhu Zhen: It's evident that the tasks of ESG data collection, analysis, and reporting cannot be accomplished by a single individual. Many companies might have referred to a book by Huawei titled 'The Way of Huawei Data.' A common finding in the book is an organizational structure called the Group Data Governance Committee, under which sits the Office of Data Governance, and overseeing that is the CDO.

The CDO is a member of the Data Governance Committee and also heads the Office of Data Governance. This organizational structure is comprehensive and extensive. Thus, within this structure, it's necessary to integrate the company's complete data governance team and systemfor ESG data governance. In other words, the CDO can incorporate ESG data into the company's routine data governance processes, merely adding another domain. Just as we incorporate data governance for supplier data, financial teams, human resources, and various other segments of the company. Naturally, the entire process relies on a complete data governance system.

When confronted with a vast amount of ESG data, how do you determine which data points are critical and which can be disregarded?

Mr. Zhu Zhen: First, strictly speaking, no data should be ignored, although their specific weights may vary.

Secondly, the perception that some data can be overlooked might stem from an incomplete understanding of ESG assessments or a lack offamiliarity with many assessment dimensions and scopes. Therefore, I recommend directly engaging in the ESG data governance system and conducting an assessment of ESG data itself. This involves identifying which business modules, data sectors, and data points it touches upon, starting with a data asset inventory, forming a data catalog, and even developing an analytical framework specifically for ESG data. This processenables us to pinpoint which data pertains to ESG and, through assessing the specific business activities involved and the value of these data assets, determine the significance of various ESG data points within the assessment framework, thereby identifying key data amidst the multitude of ESG data.