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Implementing a robust data and analytics platform can address various constraints in sustainability reporting. In this final part of our series on data management for ESG and sustainability reporting, we’ll provide high-level overviews of the data and analytics platforms as well as technological innovations that enterprises can consider to address the time-consuming and resource-intensive work in sustainability reporting.
In the first part of our article, we explained that certain assets prone to have poor data quality like flat files, outdated HR systems, and manually processed Excel workbooks can’t manage vast amounts of interconnected data. In turn, other equally crucial kinds of sustainability data aren’t properly incorporated in the report. They also cannot support advanced analysis necessary for compliant sustainability reporting. Here, we’ll tackle high-level snapshots of tools, technologies, and best practices for improving data integration, quality, and analytical capabilities for sustainability reporting.
Using data platforms for ESG and sustainability reporting
Data platforms include data warehouses, data lakes, and data lakehouses, each of which has unique features to improve sustainability reporting. They can support complex types of data, improve data integrity, and enforce relationships between data points.
Using either of these platforms would depend on the company’s sustainability data, current and future data needs, the complexity of analysis required, and resource constraints.
A data warehouse, for example, can be used for structured, quantitative sustainability data that requires frequent and consistent reporting, such as energy consumption, GHG emission levels (Scope 1, 2, 3), and waste generation rates. Data lakes can be used for large and unstructured datasets, where there should be flexibility in future use. This could include information on raw material sourcing where it would have data on certifications such as the carbon footprint of third-party vendors and suppliers. A data lakehouse would suit companies needing more data governance and capabilities to handle various types of data and perform real-time analytics. The data could include aggregated information on CSR initiatives, product life cycle assessment data, and financial metrics.
Here’s an overview:
| Data Warehouse | Data Lake | Data Lakehouse | |
| Overview | A central repository of integrated data from multiple, disparate sources; stores current and historical data and is designed for query and analysis rather than for transaction processing | Centralized, scalable repository for structured, semistructured, and unstructured data; can handle vast amounts of data in raw format and is designed to store data from various sources in native format until it is needed | Provides the data management capabilities of a data warehouse while maintaining the flexibility and scalability of data lakes |
| Benefits for Sustainability Reporting |
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| Adoption Challenges |
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| Sample Implementation | Consolidating energy consumption, GHG emissions, and water usage data across all offices to centralize reporting and enable stakeholders to access KPIs and metrics faster Analyzing employee data across all locations and tracking quantifiable metrics for achieving goals toward diversity and inclusion, workplace safety, and employee development |
Aggregating diverse kinds of sustainability data to predict and act on the potential impact of climate change on the business and local community Aggregating various kinds of supply chain data with market demand forecasts to optimize operations for economic sustainability |
Integrating real-time operational data to optimize resource use, reduce cost, and improve efficiency Integrating real-time sensor data from machineries to proactively manage workplace safety and avoid potential hazards and accidents |
| Examples of Sustainability related Data |
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Table 1. An overview of the benefits, challenges, and sample scenarios
of using a data warehouse, data lake, and data lakehouse for sustainability reporting
There is no single data platform that fits all scenarios. It would depend on the organization’s specific reporting requirements and business objectives.
For example, recycling and packaging data or similar environmental sustainability metrics often consist of both structured and unstructured data. This includes quantitative data such as amounts of materials recycled, types of materials used in packaging, and the efficiency percentage of recycling processes. They also include qualitative assessments like supplier sustainability practices, consumer feedback on packaging, images and videos of packaging and waste management, and data from IoT sensors.
Figure 1. A sample workflow that outlines a process for environmental sustainability reporting, where each data is tracked and managed from collection at local units to final reporting
The right data platform depends on the operational context of the organization. For companies primarily dealing with large volumes of diverse, unstructured data, a data lake or a data lakehouse would be more appropriate. However, for those focused on high-speed analysis and structured reporting, a data warehouse is a strong option.
For instance, in one of our projects in 2021 with a multinational beverage company, we chose the data warehouse and the Domo business intelligence platform for calculating and, in turn, reducing the GHG emissions of their freight operations.
Our analytics solution was built as such because the company already uses Domo, and their main objective was to tangibly measure and track their carbon footprint, which they have structured data for. Additionally, the BI platform has an extract-transform-load (ETL) development environment seamlessly bundled with data storage and dashboard design capabilities. The ETL capability helped automate the process of data transformation and integration. The BI platform also allows connectors to be built directly onto it via APIs. These capabilities eliminated error-prone manual processes, saved time and resources, and improved data consistency. The data is then visualized in an interactive dashboard, which its end users can then derive insights from.
By tailoring the company’s data management to specific needs and outcomes, organizations can strategically utilize these platforms to improve their sustainability performance and ensure compliance in reporting.
IT infrastructure considerations for ESG and sustainability reporting
The IT infrastructure serves as the necessary technological foundation for managing and analyzing sustainability data. Here’s a high-level overview of what needs to be considered:
| Components | Impact to Sustainability Reporting | |
| Cloud | Cloud solutions and services based on the organization’s security, scalability, and performance needs Encompasses hardware resources, development tools, and software |
Affects scalability and flexibility in data storage and processing as well as collaboration and data sharing |
| Integration of Advanced Analytics | Implementation of platforms for statistical analysis, predictive modeling, and machine learning algorithms as well as tools for converting data into visualized, dynamic reports | Supports faster decision-making and predictive insights into the company’s sustainability performance |
Software and Applications |
Tools for data ingestion, data cleansing, processing, and analytics, including specialized sustainability management software Applications that create dynamic reports and visualizations to communicate sustainability performance |
Enables analysis and interpretation to uncover insights on sustainability performance Provides stakeholders with comprehensible and actionable sustainability reports |
| Compliance | Solutions to protect data integrity and specialized tools to help manage compliance with environmental regulations and standards | Protects sensitive sustainability data and ensures that sustainability reporting practices comply with regulatory requirements |
| Communication | Systems and collaborative platforms that enable the organization to share its sustainability initiatives across the company Solutions for securely exchanging sustainability data or report with external stakeholders such as third-party auditors |
Fosters transparency within and outside the company Supports timely and accurate reporting of sustainability performance |
| Support and Maintenance | Dedicated support for troubleshooting and maintaining the systems used for sustainability reporting Encompasses continuous training for employees and end users of sustainability reports |
Ensures that the IT systems are always operational Enables the organization to quickly adapt to changes in sustainability reporting, such as updates on regulations |
Table 2. An overview of the components in an IT infrastructure
that companies should consider for their sustainability reporting
Companies that operate across multiple regions or globally could have more significant challenges, particularly in consolidating complex and fragmented data that typically comes from both internal and external sources. These tools, technologies, and best practices can help:
Utilize a centralized repository: Cloud platforms such as Amazon Web Services (AWS), Google Cloud, and Microsoft Azure have extensive tools for data integration and management that help ensure a single source of truth for sustainability reporting. They can be complemented by cloud-native ETL services that provide capabilities for data extraction, transformation, and loading.
Employ automated data integration tools: Ensure seamless data flow from diverse sources into the central repository. Data virtualization techniques can create a unified representation of data from multiple sources without the need to move or copy the data. This helps maintain data integrity and reduces redundancy. Additionally, APIs enables smoother exchanges of data between different systems. These APIs can be customized or utilized from prebuilt selections, depending on the compatibility and needs related to systems that might not be natively supported.
Reinforce data governance: A robust data governance framework ensures the availability, usability, integrity, and security of the data used. There should be data quality metrics, role-based access controls, and processes that manage changes in the data environment (such as adding new data sources or updating the IT infrastructure).
Sample use case: Google Cloud Platform for sustainability reporting
Let’s take our engagement this year with a global CPG company as an example, where we designed an architecture for carbon emissions reporting in transportation using the Google Cloud Platform (GCP).
GCP provides a comprehensive suite of tools for sustainability reporting, such as building data platforms, hosting applications, and performing AI-powered analytics.
Figure 2. A sample high-level architecture for a carbon emissions reporting in transportation using GCP
As shown in Figure 2, we designed the architecture for automation, scalability, advanced analytics, and integration:
1. Data ingestion (collecting and importing data from various sources)- Data from outbound transportation management system (TMS) and external geographical and logistical data are collected via APIs. These are necessary for transportation management and route optimization.
- A separate, custom API is used to calculate emissions based on transportation data.
- Data ingestion is enabled by data integration tools and processes. Data such as orders, shipment details, and location can be converted to GPS coordinates for more precise tracking.
2. Data processing and storage - GCP’s Google Cloud Storage (GCS) serves as the repository for raw data. It goes to an initial landing zone, then moved to two layers via GCP’s BigQuery.
- After the data undergoes these two phases, it is cleaned, transformed, and harmonized for consistency.
3. Data transformation and analysis - After transformation, the data is structured into final tables where specific SQL procedures and modeling are applied to integrate transit and emissions data.
- Data that has been analyzed and structured is made ready for export or for use in external systems for further analysis.
- For visualization and reporting, the data is pushed into a BI platform such as Power BI. It’s organized in one layer then presented in another, where datasets are organized, and reports are generated.
4. Integration and output - The processed and analyzed data is routed through an API.
- The data can be integrated with other enterprise systems and shared with external stakeholders.
GCP supports each stage of the workflow through:
- API management for integrating various data sources.
- Data collection through GCP’s GCS.
- Scalable, cost-effective data processing.
- Advanced analytics through GCP’s capabilities to integrate machine learning models.
- Integration with other BI platforms for data visualization and reporting.
AI and machine learning for sustainability reporting
Using AI and machine learning (ML) for sustainability is a growing trend that will further accelerate in the coming years. Many enterprises and public agencies have already implemented AI and ML in their sustainability initiatives, but they can also be applied to reporting.
Here’s a gist of what they can do for sustainability reporting:
| Use Case for AI and ML |
Benefits of AI and ML |
Implementation | Sample Scenario |
| Improved accuracy and consistency in reporting | Automatically detecting and correcting errors in data, standardizing data from various sources and formats, reducing or eliminating manual work | AI-powered tools that identify anomalies or inconsistencies and automate the validation process ML algorithms that apply uniform metrics to all datasets |
AI solution that consolidates various kinds of sustainability data across multiple regions into a unified, global sustainability report |
| Predictive analytics for managing carbon emissions | Automated data collection, real-time monitoring | Deploying ML models for predicting future emissions | Analyzing data to identify the most fuel-efficient routes and predict optimal times for vehicle maintenance |
| Automated sustainability reports | Scalability in handling large datasets | Using natural language processing (NLP) for transforming raw data into clear, comprehensible reports for all stakeholders | NLP tools that digest vast amounts of CSR reports to extract relevant data, then synthesizing into public reports |
| Optimizing waste management and recycling | Improved accuracy in distinguishing waste materials | Using vision-based AI systems, using ML to predict future waste | AI solutions that improve the granularity and accuracy of waste management data |
| Sustainable investment reporting | Enhanced portfolio analysis by providing insights into their sustainability impact | Using AI to analyze ESG data from multiple external sources and using ML algorithms to automatically generate reports | Sustainable investment fund using AI to continuously monitor and analyze the ESG performance of its assets |
| Assessing sustainability practices | Streamlined compliance process as well as improved accuracy and objectivity in evaluating suppliers | NLP-powered systems automatically gathering and analyzing reports and audit data from suppliers, ML models that predict potential compliance risks | AI-powered solution that analyzes data reported by suppliers, highlighting risks and recommending interventions |
| Scenario planning and risk management | What-if simulation, data augmentation, automated reporting | AI-powered solution that enhances existing datasets or maps missing data | Enabling the company to see how different actions or strategies could affect sustainability outcomes |
Table 3. An overview of how AI and ML can be used in sustainability reporting
Preparing for the future of sustainability reporting
Sustainability is now integral to corporate strategy, with new regulations and upcoming standards that will compel organizations to adopt more rigorous reporting practices. Based on our experience working with leading companies around the world, we foresee paradigm shifts that will reshape how businesses manage and disclose their sustainability efforts:
Compliance issues: Regulatory bodies are further tightening their scrutiny of companies’ sustainability practices. This trend is leading to stricter enforcement of existing regulations and the imposition of fines for noncompliance. Disclosure requirements will also increase, focusing on the effectiveness and outcomes of sustainability initiatives. In one recent example, a sole case alone led to a US$10-million settlement due to greenwashing.
New and more mandatory standards in the US and Europe: The EU is expanding its Non-Financial Reporting Directive (NFRD) to a broader range of companies and introducing more specific reporting requirements under the Corporate Sustainability Reporting Directive (CSRD). The CSRD includes the European Sustainability Reporting Standards (ESRS), which provide detailed guidelines on how companies should report their sustainability information. Sustainability reporting under the CSRD will be more focused on materiality, which will be more challenging to do.
Similarly, the US Securities and Exchange Commission (SEC) is proposing rules that would require listed companies to disclose climate-related risks and their actual and potential impact on business strategy, financial condition, and operating performance.
Moving to a more reasonable assurance for attestations: Many organizations adhere to limited assurance standards for their sustainability attestations, but there is a shift toward requiring reasonable assurance. This involves higher assurance requirements, with regulators and stakeholders demanding more reliable and detailed assurances on sustainability reports.
Consequently, the auditing process will become more rigorous, requiring more in-depth verification of data and processes used to compile sustainability reports.
Figure 3. A sample visualization showing the ever-increasing complexity in ESG and sustainability reporting
These intricacies are why enterprises need to proactively adapt to the evolving landscape of sustainability reporting. Companies can address these by employing advanced data management technologies and practices, aligning with new regulatory standards, and enhancing the rigor of their assurance processes.
It’s also why Lingaro’s Sustainability Analytics Practice adopts an end-to-end, triple-bottom-line approach to sustainability reporting. Our GRI-certified professionals work with enterprises to create a starting point for achieving sustainability goals through data assessments and feasibility studies. Our stakeholder workshops address immediate concerns and plan for a future state, ensuring that all voices are heard and integrated into the sustainability strategy. Our GRI Community membership means we have the expertise and resources to guide companies through the nuances of global sustainability standards.
We then collaborate with our own technology leaders and experts to develop AI-powered analytics solutions to ensure comprehensive, compliant sustainability reporting. This integration of innovative technology and domain expertise enables us to provide tailored, actionable strategies that not only meet regulatory demands but also drive meaningful change and business resilience.
To see how your enterprise can improve its sustainability performance and ensure compliance, explore our Sustainability Analytics page and learn more about our capabilities, success stories, and expert insights.
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