Introduction
Sustainability data solutions in Microsoft Fabric help you gather, analyze, and report Environmental, Social, and Governance (ESG) metrics by turning scattered data into a unified ESG data model. The ESG data estate helps you merge ESG information from different sources into a common format that supports environmental, social, and governance data models.
In this module, you learn about the standardized ESG schema and the capabilities of the ESG data estate solution that helps you ingest and harmonize your sustainability data. You deploy the solution to your Fabric workspace and ingest the demo data so that you can explore the capabilities. Additionally, you explore how to bring your own data into the standardized data estate.
ESG data overview
ESG data refers to data, information, and metrics in relation to environmental, social, and governance factors:
Environmental (E) - This aspect of ESG data focuses on the issues pertaining to a company's environmental impact. It includes data on a company's carbon emissions, energy efficiency, resource consumption, waste management, and other environmental practices.
Social (S) - This aspect of ESG data includes information about how a company interacts with and impacts its employees, customers, communities, and society as a whole. This data might cover issues such as labor practices, diversity and inclusion, human rights, and community engagement.
Governance (G) - This aspect of ESG data includes information about a company's internal practices, policies, and leadership structure. It includes information such as board diversity, executive compensation, anti-corruption measures, and overall corporate governance.
With this standardized data, you can calculate quantitative metrics to comply with reporting rules, such as Corporate Sustainability Reporting Directive (CSRD). Additionally, you can use these combined datasets for analytics and visualizations with tools, such as Microsoft Power BI. You can access these aggregated datasets through external applications for data audits, CSRD report generation, and more.
Assets that deploy as part of this capability include data pipelines, lakehouses, and notebooks. The assets transform, compute, and store data from the raw form to computed ESG metrics based on standardized ESG models. The ESG data estate also integrates seamlessly with Microsoft Sustainability Manager, facilitating the import and transformation of sustainability data. Organizations can publish these computed metrics for downstream application consumption, which helps them more easily share their sustainability performance with stakeholders.
ESG challenges
Currently, organizations face increasing requirements in ensuring that their sustainability systems include ESG data, such as the need to:
Strategize the required optimizations for sustainability objectives, such as net-zero emissions, water positive, and zero waste.
Establish a baseline of their sustainability measures against publicly available, industry-relevant indexes and statistics.
Comply with various regulatory disclosures and reporting frameworks and innovations that organizations can bring to their workloads in the context of sustainability.
Other trends pertaining to ESG data include:
Increasing regulation - Organizations of all sizes, across various industries and geographies, are under pressure to align with the UN Paris Agreement and are limiting emissions on their borders. Additionally, new and proposed regulatory requirements, particularly in the EU, UK, and US, drive a need to capture data on current emissions and reduce future environmental impact.
Growing demand for change - Investors increasingly incorporate ESG considerations into their strategies. Simultaneously, consumers place greater emphasis on sustainability, compelling every organization to demonstrate tangible action and progress.
Lack of a system of record - Regardless of how mature an organization's infrastructure might be, elevated standards for reporting ESG impact, risk, and opportunities require refining and potentially reconstructing data management systems, processes, and controls. Dependence on historical or siloed data introduces problems of accuracy, relevancy, and consistency.
The current ESG data ecosystem is complex, varied, and widely distributed. Data that relates to different areas of ESG, such as carbon, water, waste, social, governance, biodiversity, pollution, and ecology, resides in different systems and formats. Organizations need to combine and harmonize disparate data to prepare it for sustainability scenarios.