Overview of a Reference ESG IT Architecture using SAP Sustainability Control Tower as central app

“You can have data without information, but you cannot have information without data,” by Daniel Keys Moran, is highly relevant to ESG data management.

Good decisions are the foundation for embracing ESG (Environmental, Social, and Governance) changes. These decisions rely on data, which is why there’s a growing demand for ESG-related information. A company’s IT system supports smart and fast decisions by providing clear insights on emissions, natural resources, and employees across the entire business. This leads to better investments, lower risks, an improved supply chain, stronger resilience, and attracts new customers, lenders, and investors.

A solid IT strategy is super important for hitting ESG targets. Many companies have some idea of their ESG impact, but a focused ESG IT setup can make it easier to measure, track, and improve their impact. This can uncover ways to cut waste, reduce carbon emissions, protect nature, and boost social good. It’s critical for reaching bigger ESG goals.

A well-designed and properly implemented IT system or IT architecture is essential for successfully guiding an organization’s ESG strategy and integrating it into business processes. The IT architecture I’m referring to is a comprehensive, organization-wide system that spans all business areas and is guided by the organization’s ESG strategy or we can call it as a ESG IT Architecture.

The ESG IT architecture is divided into four vertical layers, each representing a distinct function in the data lifecycle: Data Generation & Collection, Data Harmonization, Contextualization, and Staging, Data Analysis, and Steering and Reporting. These layers are supported by Data Governance, such as Master Data Management, Data Security and Privacy which ensure consistency, security, and governance across the ESG data lifecycle.

1. Data Generation and Collection

The Data Generation & Collection layer serves as the foundational step in the ESG data pipeline, where raw data related to environmental, social, and governance (ESG) factors is generated or sourced from diverse internal and external systems. This layer encompasses several key components: ESG-specific systems that capture data on emissions, environmental, health, and safety (EHS) metrics, sustainability indicators, and external ESG ratings; business transaction systems such as ERP, SCM, and HRMS, which provide critical business context like travel, procurement, and logistics data relevant to ESG impact; and other sources, including utility consumption records, IoT devices, facility management systems, and satellite or geospatial data. This layer feeds raw, often unstructured or semi-structured data into the Data Integration layer, where it must be normalized across various formats and systems to enable effective analysis.

2. Data Integration

The Data Integration layer transforms diverse raw data into a harmonized, contextualized, and structured format suitable for analysis. Its key components include an Enterprise Data Hub, which serves as a central platform for collecting data from all sources, and a Central ESG Data Hub, a specialized repository designed for ESG analytics and reporting. Processes such as data harmonization (standardizing formats), contextualization (adding meaning or context), and staging (temporary storage before use) are critical. This layer acts as a bridge, receiving data from the Data Generation & Collection layer and sending clean, consistent data to the Data Analysis layer for further processing.

3. Dat Analysis

This layer leverages cleaned and structured data to generate actionable ESG insights, perform calculations, and support sustainability goal-setting. Key components include the SAP Sustainability Control Tower, a central analytics platform, alongside ESG dimensions (e.g., emissions, diversity, safety), KPI calculations to track performance, and ESG metrics derived from integrated data. It also facilitates short- and long-term ESG goal planning. The layer ingests harmonized data from the Integration layer, provides analytical outputs to the Steering & Reporting layer, and may feed data back into operational systems to enhance processes.

4. Steering & Reporting

The final layer facilitates decision-making, internal governance, and external ESG disclosures by leveraging insights from the Data Analysis layer. Its key components include ESG reporting for regulatory (mandatory), voluntary, and internal purposes; management of ESG programs like decarbonization or circular economy initiatives; and advanced analytics, such as predictive modeling, real-time dashboards, and visual storytelling. This layer relies on accurate and timely insights from the Data Analysis layer to enable business units and executives to steer ESG strategy. Reporting outputs may also create a feedback loop, influencing data generation practices to refine the overall process.

The ESG IT architecture transforms raw data into actionable insights through a cohesive, interconnected framework. It collects and normalizes diverse data, integrates and harmonizes it for analysis, and generates ESG metrics to support goal-setting. The steering and reporting layer drives decision-making and compliance, enhancing organizational ESG performance.

Thank you for taking the time to read my blog. I truly appreciate your interest with this critical topic. If you have any thoughts, questions, or ideas to share, please feel free to leave a comment.

Sreekanth

Environmental, Social, and Governance (ESG) is a framework for assessing an organization’s practices and performance on sustainability and ethical issues. An effective ESG IT architecture supports rapid growth, adapts to evolving requirements, enables flexible reporting, ensures robust data security, and fosters stakeholder trust through transparent and accountable practices. “You can have data without information, but you cannot have information without data,” by Daniel Keys Moran, is highly relevant to ESG data management. Good decisions are the foundation for embracing ESG (Environmental, Social, and Governance) changes. These decisions rely on data, which is why there’s a growing demand for ESG-related information. A company’s IT system supports smart and fast decisions by providing clear insights on emissions, natural resources, and employees across the entire business. This leads to better investments, lower risks, an improved supply chain, stronger resilience, and attracts new customers, lenders, and investors. A solid IT strategy is super important for hitting ESG targets. Many companies have some idea of their ESG impact, but a focused ESG IT setup can make it easier to measure, track, and improve their impact. This can uncover ways to cut waste, reduce carbon emissions, protect nature, and boost social good. It’s critical for reaching bigger ESG goals. A well-designed and properly implemented IT system or IT architecture is essential for successfully guiding an organization’s ESG strategy and integrating it into business processes. The IT architecture I’m referring to is a comprehensive, organization-wide system that spans all business areas and is guided by the organization’s ESG strategy or we can call it as a ESG IT Architecture. The ESG IT architecture is divided into four vertical layers, each representing a distinct function in the data lifecycle: Data Generation & Collection, Data Harmonization, Contextualization, and Staging, Data Analysis, and Steering and Reporting. These layers are supported by Data Governance, such as Master Data Management, Data Security and Privacy which ensure consistency, security, and governance across the ESG data lifecycle. 1. Data Generation and CollectionThe Data Generation & Collection layer serves as the foundational step in the ESG data pipeline, where raw data related to environmental, social, and governance (ESG) factors is generated or sourced from diverse internal and external systems. This layer encompasses several key components: ESG-specific systems that capture data on emissions, environmental, health, and safety (EHS) metrics, sustainability indicators, and external ESG ratings; business transaction systems such as ERP, SCM, and HRMS, which provide critical business context like travel, procurement, and logistics data relevant to ESG impact; and other sources, including utility consumption records, IoT devices, facility management systems, and satellite or geospatial data. This layer feeds raw, often unstructured or semi-structured data into the Data Integration layer, where it must be normalized across various formats and systems to enable effective analysis. 2. Data IntegrationThe Data Integration layer transforms diverse raw data into a harmonized, contextualized, and structured format suitable for analysis. Its key components include an Enterprise Data Hub, which serves as a central platform for collecting data from all sources, and a Central ESG Data Hub, a specialized repository designed for ESG analytics and reporting. Processes such as data harmonization (standardizing formats), contextualization (adding meaning or context), and staging (temporary storage before use) are critical. This layer acts as a bridge, receiving data from the Data Generation & Collection layer and sending clean, consistent data to the Data Analysis layer for further processing. 3. Dat AnalysisThis layer leverages cleaned and structured data to generate actionable ESG insights, perform calculations, and support sustainability goal-setting. Key components include the SAP Sustainability Control Tower, a central analytics platform, alongside ESG dimensions (e.g., emissions, diversity, safety), KPI calculations to track performance, and ESG metrics derived from integrated data. It also facilitates short- and long-term ESG goal planning. The layer ingests harmonized data from the Integration layer, provides analytical outputs to the Steering & Reporting layer, and may feed data back into operational systems to enhance processes. 4. Steering & ReportingThe final layer facilitates decision-making, internal governance, and external ESG disclosures by leveraging insights from the Data Analysis layer. Its key components include ESG reporting for regulatory (mandatory), voluntary, and internal purposes; management of ESG programs like decarbonization or circular economy initiatives; and advanced analytics, such as predictive modeling, real-time dashboards, and visual storytelling. This layer relies on accurate and timely insights from the Data Analysis layer to enable business units and executives to steer ESG strategy. Reporting outputs may also create a feedback loop, influencing data generation practices to refine the overall process. The ESG IT architecture transforms raw data into actionable insights through a cohesive, interconnected framework. It collects and normalizes diverse data, integrates and harmonizes it for analysis, and generates ESG metrics to support goal-setting. The steering and reporting layer drives decision-making and compliance, enhancing organizational ESG performance. Thank you for taking the time to read my blog. I truly appreciate your interest with this critical topic. If you have any thoughts, questions, or ideas to share, please feel free to leave a comment. Sreekanth Read More Technology Blog Posts by Members articles

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