Data Management: Streamlining the Corporate ESG Ecosystem

1. ESG Consulting Services WHITEPAPER Data Management: Streamlining the Corporate ESG Ecosystem

2. Data Management: Streamlining the Corporate ESG Ecosystem Summary Organizations can no longer ignore the significance of Environmental, Social, and Governance (ESG) data management strategies. Effective ESG data management and disclosures are paramount to ensuring organizations’ future viability. The lack of alignment between ESG data and performance standards indicates major hurdles in ESG data & disclosure management. By introducing ambitious and enforceable policies, constructing clear incentives within the ecosystem, creating ESG data awarensess across the organization and driving transparent ESG performance & standardized reports - organizations can leverage the benefits of effective ESG data management throughout the ecosystem. The Rising Appetite for Data in the ESG Landscape With investors are relying on ESG data to identify which enterprises are best positioned to flourish in a sustainable world. However, the lack of consistent data and reporting standards for ESG presents a major barrier. One of the main concerns for ESG investors is the lack of standardized data made available by an organization. While businesses reporting their ESG performance metrics endeavor to satisfy the rising demand for better data among investors and stakeholders, meeting this demand is particularly challenging. Investors are facing the challenge of evaluating company-generated sustainability reports, policies, and several other documents. This adds to the confusion and level of measure required to make sense of the new ways of evaluating a company’s ESG data management capabilities. sustainable investing movements evolving, Understand the relative significance of these issues and ways to report the relevant data Strategize the ESG management framework based on key ESG performance metrics, targets, and reporting standards Assemble all internal resources and data required to meet the ESG reporting targets Determine a disclosure strategy that brings maximum visibility to reported data and adds value to the organization Report the ESG performance data using the ESG management framework. The plethora of reporting requirements and the lack of consistent reporting standards for sustainable performance are forcing investors to expend the already limited resources to standardize unstructured ESG data, thereby slowing down the adoption of ESG investing. • • • • • data disclosed through Investors’ rising appetite and the potential for risk- adjusted returns have boosted assets in sustainable investment. However, one of the most pressing concerns is the lack of access to transparent, reliable, and consistent ESG data. Sustainable investing has become a mainstream concept, and organizations are incorporating the following steps to report ESG data: • the company • the stakeholder groups Recognize the spectrum of stakeholders impacting Map the material sustainability issues encompassing 2

3. Data Management: Streamlining the Corporate ESG Ecosystem Ascending the ESG Data Gathering Ladder Sustainability impacts every aspect of a business. With the world progressing rapidly, organizations are under pressure to meet increasingly stringent ESG targets. corporations. Investors now consider the ESG ratings of SMEs and suppliers before investing. Data is the foundation of all decision-making in today’s digital landscape. Yet, in its current form, ESG data reporting is not evolving at the desired pace. The challenges associated with data collection and processing are impeding outcomes and misleading stakeholders, thereby wasting resources and resulting in poor decisions. The ESG data market is estimated to exceed $1 billion in annual revenues, scaling three times its worth five years ago. Over the years, organizations have experienced an increase in the requirement for green investments by regulatory bodies to support the sustainability of small and mid-sized enterprises (SMEs) and large Figure 1: Sustainable Data Sharing Flow Intrinsic Motivation ESG Strategy Sustainabledatacollection Sustainable data Sharing Financing Requirements Laws and Regulation Source: Deloitte Key ESG Data Challenges 1. The inconsistency in ESG standards adds to the complications in the use of ESG data. For instance, organizations in the energy sector calculate their ESG data analytically and do not describe the actual energy performance of the building. However, the actual operational energy usage depends on the use of a building and not on its sustainability. The reason being the benchmarks for actual operational energy usage do exist but lack standards. Incomplete ESG Standards: Due to the rapidly growing investor expectations of ESG integration into investment decision-making, asset managers are seeking to expand their ESG capabilities to meet investor and regulator demand. However, for investors, the data challenges associated with ESG investing range from ESG data accessibility to consistent reporting measures and suitability. To tackle these challenges, organizations need to build value propositions to raise their sustainability standards. The lack of ESG definition consistency and data standardization landscape has led to various challenges. The following are four key challenges for optimal use of ESG data within the industry: 3

4. Data Management: Streamlining the Corporate ESG Ecosystem other operational activities with lenient government measures add to the low significance of ESG in decision- making. 2. ESG Data Sharing Hurdles: Data does not move freely in the whole ecosystem. Sometimes, stakeholders are hesitant to share ESG data to mitigate the risk that is then used to gain a competitive advantage. Accordingly, the entire ecosystem cannot optimally benefit from the available data. Organizational privacy laws further add to the complication. 4. Missing Coordination: The institutional ecosystem lacks coordination that propels organizations to collect and share their ESG insights. While standardization and synchronization are essential for efficient and effective data management, these processes can be executed only with proper coordination. With institutional investors constituting a small portion of the entire market, the push for ESG data insights is still less. 3. A Lack of Stimuli: Incentives for enhancing ESG performance are not strong enough. Differentiating factors such as limited green discounts on financing, energy expenses, and Figure 2: Challenges in Collecting ESG Data Incomplete/In consistent ESG Standards Hurdles in ESG data sharing A lack of Incentives Missing Coordination Source: Deloitte Formulating Quality ESG Data Investors rely on both qualitative and quantitative ESG data to determine leading sustainability-focused companies. The lack of consistent reporting inhibits transparency around a company’s ESG performance, creating a problem for companies and investors alike. 1. As the need for ESG data grows, data reporting standards are being widely embraced by organizations. However, the lack of standardized data indicates different data points being reported by companies within the same sector, thus creating a drift between data shared by companies from year to year. Quality ESG Data Equals Standardized Data: Some ESG metrics are qualitative, discretionary, and unregulated. Investors are required to spend excessive time attempting to interpret unstandardized data, which hampers investment growth. However, multiple issues contribute to this situation. Lack of standardized data reporting, inconsistent reporting metrics, variable scoring systems, and complex communications - all lead to building a chaotic data system. This led to the Task Force on Climate-related Financial Disclosures (TCFD) reporting framework becoming mandatory for organizations. The reporting standards enable investors to understand the material sustainability issues, thus assisting in creating a standard framework to report the company’s financial performance. Amid this unregulated chaos, how can organizations generate quality ESG data that supports their investor and stakeholder requirements? Companies should leverage widely used reporting frameworks to ensure standardization and maintain consistency year-on-year. 4

5. Data Management: Streamlining the Corporate ESG Ecosystem companies should break down ESG disclosures into the following four main areas: 2. Quality ESG Data Equals Consumable Data: Businesses formulate ESG data for stakeholders based on their needs and interests. However, it can overwhelm them and drive the need for clearly packaged, consumable ESG data. To formulate consumable data, • • • • Financial materiality Comparability Accessibility Reliability Figure 3: Decoding the ‘E,’ ‘S,’ and ‘G’ Engagement Statistics 2021 SOCIAL GOVERNANCE ENVIROMENTAL Sustainablity Governance & Transparency Greenhouse Gas Emissions Employment Practices 46 50 30 Renewable ENergy & Clean Employee Diversity & Board Structure & Composition 45 46 27 Tech Inclusion Energy Usage and Efficiency 42 Customer Privacy & Data Regulatory 35 24 Security Water Management & 26 Employee Health,Safety & Scarcity Board Diversity 29 23 Wellbeing Env Supply Chain Standards 22 Access & Affordability 16 23 Executive Compensation Waste and Hazardous Materials Management 20 Customer Welfare BUsiness Ethics 14 14 Material Sourcing 18 Humnroghts & COmmunity Accident and Safety Management 13 8 Relations Recycling and Reuse 16 Product Quality and Safety Ownership Structure 10 5 Biodivercity/Ecologycal 6 Impact Workforce Restructuning & Risk Management Physical Risks of Climate 4 5 5 Automation Change Labor Supply Chain Standards Competitive Behavior 3 4 4 Air Quality and Pollution Note: The chart highlights the number of meetings that were focused on ‘E,’ ‘S,’ and ‘G’ Engagement agendas. Source: MSIM 4. Transparent ESG Data Enables Higher ROI for Corporates and Facilitates Smarter Investing: Investors have drawn a positive correlation between sustainability and financial performance. However, ESG data in the current form may only add to their confusion. Either the investor is overwhelmed by the heap of unstructured data or is drawn to contradictory ESG scores that make it challenging to decide where to invest. 3. Measuring the Pulse of ESG Data Readiness: Organizations understand that ESG requirements form the first order of business. By employing abstract measures to analyze a corporation’s raw data, businesses can decode the ESG metrics required for quality reporting. To effectively adhere to the ESG reporting requirements, institutions need to scrabble through the enterprise- level data that exists on a large scale. This data requires to be aggregated, synthesized, and visualized so that it can coherently be communicated to a range of stakeholders. Investors working with ESG data directly reported by companies can weigh the factors based on their values and the issues that will have the greatest financial impact on the industry as well as the company. With the ESG marketplace growing and expanding through the asset management industry, forward-thinking stakeholders and investors are moving toward employing a more sophisticated approach. Organizations experienced paramount market opportunities when compared with their competitors. Along with meeting the regulatory and stakeholder demands, organizations can comprehend, analyze, and act upon this data to improve their business. This data can be employed as a risk management tool to understand non-financial metrics impacting the organization. For example, with detailed supply chain insights, businesses can dodge supply-side shocks. reporting ESG effectively have 5

6. Data Management: Streamlining the Corporate ESG Ecosystem To achieve levels of ESG data transparency, organizations need to mandate a few essential steps: This execution requires the right skills and capabilities, with a good understanding of ESG and the data management life cycle. The overall strategic aim is to reckon and report the organization’s sustainability ledger that matches the same rigor as that of its financial ledger. Sustainability-focused investors no longer wish to rely on outside recommendations and ESG scores. For the sustainable investing movement to grow, it is critical that all organizations work in unison and focus on improvements in the quantity, quality, and accessibility of ESG data. • Creating an architecture for the data landscape to guarantee the data is interoperable and integrated throughout the organization. Ensuring that the data layer is easily queried and analyzed with relevant data tools. Integrating data sources with third-party providers, such as ESG rating firms, to leverage the data output to deliver a compelling sustainability report. • • Figure 4: ESG Global Assets Under Management Representation 80.0 70.0 2014-2018 Actual ESG Assets 2020 2022-2026 Actual vs. Projected Projected ESG Assets 60.0 50.0 40.0 30.0 20.0 10.0 0.0 2014 2016 2018 2020 2020 2022 2024 2026 Europe United States Japan Canada Austalia/New Zealand Source: Global Sustainable Investment Alliance, Bloomberg Intelligence 6

7. Data Management: Streamlining the Corporate ESG Ecosystem ESG through the Lens of Data Analytics Organizations have historically driven financial, security, and agility benefits through data analytics. With its rising popularity, data analytics has become more vital due to the need for actionable insights from ESG data. For investors with ESG goals, data is the representation of the entire framework. But with inconsistent data sets, dispersed evaluation methodologies, and a lack of global standards, investors are struggling to gain insights into their portfolios. Here’s how data analytics is assisting in making it easier. To fulfill investor expectations, enterprises are establishing an ESG performance measurement framework through data analytics techniques for reporting investment- grade ESG data. These systems will be able to accurately depict the outcomes of ESG management, a priority for today’s ESG investors. Businesses can sieve through the newly formulated data sources and methodologies and access the ESG datasets to deliver actionable insights. The right solution will provide a dataset that covers all the entities an organization invests in as well as the performance of every element of the environment, social, and governance framework. It will assist organizations in compiling input from multiple perspectives in a timely manner. Leveraging ESG data can unlock a wide range of new propositions across the industry. With respect to ESG, data is embedded in every decision, interaction, and operation. Organizations are now applying data-driven approaches sporadically throughout the processes, thus generating value. Business leaders are prioritizing the much-needed upgrade of their ESG management and reporting. For investors to commit capital with confidence, businesses assemble the caliber of ESG performance data that offers vital information. Streamlining the Broad, Non-standardized ESG Data Ecosystem with Data Analytics 3. Optimization & portfolio simulation When investors enter the realm of ESG, it is crucial for them to understand the changes that affect the portfolios on a pre-trade basis. Data analytics tools enable them to simulate changes to the portfolios and understand the probable impact on ESG scores. Data analytics helps in sorting heaps of information efficiently. Organizations are integrating technology to merge data from multiple sources and identify patterns to measure, analyze, and report their ESG investments across pivotal applications. To keep up with the emerging pressure surrounding ESG investing, capital market participants are taking to data analytics to enable timely and accurate decision-making by communicating them to stakeholders. 4. ESG reporting and benchmarking Investment teams are obligated to aggregate, analyze, and report the ESG framework. Digitizing this process will authorize managers to provide detailed evidence of the results. With datasets becoming extensive and more granular, investment teams face the difficulty of effectively analyzing and providing meaningful data intelligence without digitization. 1. A fundamental usage of ESG data is screening for suitable securities. With data analytics, managers can dig deeper into screening, along with comparing securities and their impacts across different ESG segments and industries. With the right data program, investors can analyze and weigh varied factors on a level-playing field to decide which organization meets their sustainability objectives and better fits the portfolio. Screening A recent study highlights that more than 99% of CEOs from large corporations agree that sustainability issues are vital to the future success of any business. Two-thirds of the organizations view the fourth industrial revolution technologies as a critical factor in accelerating the socio-economic impact. Among the CEOs, 59% stated that they would be deploying low-carbon and renewable energy across operations in their organization. 2. Performance analysis Asset managers measure and report the outcomes if they meet the expectations of their investors. The need for better sustainability data is on the rise as regulators are placing more demands on investors to report on ESG-related risks. By quickly pulling together custom data views on different holdings, managers can work toward acquiring their clients’ trust and access to their capital. 7

8. Data Management: Streamlining the Corporate ESG Ecosystem Figure 5: ESG Focused Engagement 2017 105 249 178 38 2018 180 67 2019 76 170 2020 106 2021 81 Total Management Meetings with ESG Dedicated ESG Engagements Source: MSMI The New Frontier: ESG Data at Scale Continued advances in ESG Data are still promising to produce systems that perceive, analyze, decide, and act. However, the effectiveness of these approaches is limited by the challenge of effectively industrializing them on an organizational scale. On the upside, institutions worldwide are understanding and incorporating the significance of ESG data reporting. With this shift in mindset, ESG regulations are no longer viewed as a burden, but as a means for operational transparency. And this transparency tool holds the potential to unlock capital as well as create solutions for the global challenges organizations face today. This trend is now pointing toward the likelihood that more enterprises will be obliged to assemble, report, and publish their sustainability information. Businesses along with asset managers are now working to overcome their ESG data challenges. However, it can be inferred that the ESG data sector is still far from meeting these exceeding investor expectations. While there is a gap between the development and effective industrialization of ESG data, to mitigate these challenges, organizations need to shift their perspective and focus on developing sustainable deployment standards at scale. 8

9. Data Management: Streamlining the Corporate ESG Ecosystem About the Author Jayaprakash Mallikarjuna • Senior Vice President - ESG Services & Data Modernization Passion for generating actionable insights from data is something that drives Jayaprakash. In his 20+ years of professional journey, his focus has been to enhance customer experience and drive value for businesses. At SGA, he plays a key role in enabling the growth of ESG and Data modernization businesses. Prior to SGA, he spent over a decade at Thomson Reuters in the investment research space and then jumped into the start-up world with a mandate to drive operations strategies and grow the business. Being an ardent chess player and a runner help him in strategizing paths to the end-goals consistently while his puns keep him high. Disclaimer This document makes descriptive reference to trademarks that may be owned by others. The use of such trademarks herein is not an assertion of ownership of such trademarks by SG Analytics (SGA) and is not intended to represent or get commercially benefited from it or imply the existence of an association between SGA and the lawful owners of such trademarks. Information regarding third-party products, services, and organizations was obtained from publicly available sources, and SGA cannot confirm the accuracy or reliability of such sources or information. Its inclusion does not imply an endorsement by or of any third party. Copyright © 2022 SG Analytics Pvt. Ltd. www.sganalytics.com GET IN TOUCH New York | Seattle | San Francisco | Austin | London | Zurich | Pune | Hyderabad | Bengaluru 9