From Data Silos to Efficiency: ESG Data Management in Companies

ESG processes are becoming more complex, regulatory requirements stricter, and stakeholders expect comprehensive, auditable reports. Companies must therefore deliver precise ESG insights based on data from internal systems and external sources throughout the value chain.

However, many companies underestimate the costs and challenges of building an efficient ESG data management system. ESG data is scattered across various systems, departments work in isolation, and executives still rely on Excel spreadsheets despite inefficient processes.

The key lies in automation and integration. With the right ESG software and a clear strategy, companies can break down data silos and turn ESG data into a valuable asset. This article explores key challenges and offers practical solutions for more efficient ESG data strategies.

Challenges and Requirements in ESG Data Management

Collecting and managing ESG data presents significant challenges for companies. Fragmented data sources, manual processes, and inconsistent data standards complicate efficient ESG reporting. Integrating data from different systems is particularly time-consuming and error-prone.

Effective ESG data management involves three key levels:

• Data Collection: ESG data is gathered from internal (e.g., HR, compliance) and external sources (e.g., suppliers, regulatory bodies).
• Data Management: Processes such as ESG data modeling, KPI definition, validation, and governance structure the data and ensure reliability.
• Strategic ESG Functions: Materiality assessments, KPI dashboards, and benchmarking enable informed decision-making and compliance with reporting requirements.

The following diagram illustrates the core architecture of efficient ESG data management and reporting. It highlights that the value of ESG functions—such as KPI dashboards, materiality analysis, or scenario modeling—depends on the quality and consistency of the underlying data.

Integration and Structuring of ESG Data

Efficient ESG data management begins with collecting diverse data sources, including environmental metrics, supply chain information, and social and financial data. These data points enter systems through various channels—APIs, web forms, or spreadsheet imports. An ESG data hub plays a crucial role in coordinating data flows, synchronizing metadata, and providing overarching governance.

By structuring ESG data management, companies can avoid isolated solutions and create a solid foundation for informed decisions and transparent reporting. Only through comprehensive data integration and validation can strategic functions like KPI dashboards and scenario modeling be effectively implemented.

ESG Development in Companies

The ESG reporting market is evolving rapidly. This is evidenced by the recently published EU Commission’s Simplification Omnibus Package. Companies must advance their ESG processes to meet increasing transparency requirements.

CIOs and sustainability managers should collaborate closely with finance, legal, compliance, and supply chain teams to develop an integrated ESG strategy. The goal is to optimize existing systems and strategically integrate ESG technologies.

The following diagram illustrates the maturity stages of ESG data management in companies. It shows how organizations transition from manual, isolated, and retrospective ESG reporting to integrated, automated, and strategically valuable ESG management.

Companies with low ESG maturity often rely on manual processes, spreadsheets, and annual reports. Higher ESG maturity enables continuous, audited, and database-driven reporting. This enhances data quality and shifts from retrospective analysis to forward-looking insights that support informed decision-making and targeted ESG improvements.

Software Solutions for ESG Data Management

The growing importance of ESG reporting presents companies with the challenge of efficiently collecting, managing, and analyzing vast amounts of ESG data. Manual processes and fragmented data silos often lead to errors and inefficiencies.

Key Benefits of ESG Software:

• Automated Data Collection: Integration of ESG data from internal and external sources
• Data Management and Consolidation: Unified data storage and quality assurance
• Regulatory ESG Reporting: Report generation in accordance with CSRD, GRI, SASB, and other standards
• Data Analysis and Benchmarking: Transparent ESG metrics for better strategic decision-making
• Reduction of Manual Processes: Saves time and minimizes errors
• Improved Data Quality: Automated validation increases ESG report accuracy
• Future-proof Compliance: Flexible ESG software adapts to evolving regulations

Identifying Data Sources and Existing Management Systems

ESG data is highly diverse and distributed across multiple enterprise or point solutions. Identifying source systems and current data management systems is essential for effective ESG data mapping.

• Identify source systems: Where does the primary data originate? These may include enterprise systems such as ERP software or GRC (governance, risk, and compliance) platforms.
• Identify current management systems: Where is ESG data currently centralized, managed, and governed? This includes specialized sustainability point solutions such as ESG reporting and management software.

Best Practices for a Successful ESG Data Strategy

For efficient ESG data utilization, companies should follow a structured approach:

1. Conduct a Gap Analysis: Identify regulatory requirements and internal needs.
2. Review Existing Data: Determine which ESG data is already collected and where improvements are needed.
3. Develop an ESG Business Case: Highlight both quantitative (time savings, reduced audit costs) and qualitative benefits (improved ESG performance, higher data reliability).
4. Evaluate the Right ESG Software: Consider industry-specific needs, IT integration, costs, user-friendliness, and scalability.

Conclusion: Why ESG Data Management Matters

Manual ESG data collection via spreadsheets is unsustainable and prone to high error rates. Specialized ESG software streamlines data collection, analysis, and reporting, ensuring companies meet regulatory requirements and achieve strategic sustainability goals.

Key Benefits of an ESG Data Model:

• Compliance with corporate ESG reporting obligations
• Better decision-making through real-time analysis of environmental and social impacts
• Future ESG performance forecasts considering growth factors

Challenges of Manual Data Collection:

• Insufficient for complex ESG reporting and compliance requirements
• Increased risk from isolated data collections that hinder consolidation
• Productivity loss: Sustainability experts spend too much time on data entry and validation instead of focusing on strategic improvements

With increasingly complex regulations, companies require a holistic view of their ESG performance to ensure long-term sustainable business practices.

The ESG tool Sunhat enables companies to

• automatically collect ESG data from different departments, sources, and stakeholders
• consolidate data for the entire group of companies
• efficient collaboration with all employees and stakeholders involved
• manage ESG projects clearly and efficiently through task assignments and review loops
• comply with regulatory ESG standards, such as those of the CSRD, using templates and instructions