Why Clean, Connected Data is Essential for Successful ESG and AI Strategies

Without clean, connected data, neither AI nor ESG reporting works. Companies that focus on data quality benefit from better decision-making, more efficient processes, and can simultaneously meet strict regulations such as the CSRD. Here are the key points:

• Data quality is crucial: 64% of CEOs see AI as a priority, but fragmented data silos and poor data quality remain major obstacles.
• Regulatory requirements are increasing: Starting January 2025, nearly 50,000 companies in the EU must collect comprehensive ESG data according to CSRD.
• AI and sustainability: AI can make ESG reporting up to 90% more efficient—but only with high-quality data.
• Challenges: Data silos, inconsistent data, and new regulations burden many companies.
• Solutions: Clear governance, automated data collection, and central platforms like data hubs.

Conclusion: Clean data is the key to successful ESG and AI strategies, ensuring long-term success in an increasingly data-driven market.

Why Data Management Drives the Success of ESG and AI

Robust data management is at the heart of successful ESG reporting and AI-powered sustainability strategies. Without high-quality, well-connected data, companies can neither meet regulatory requirements nor fully leverage the potential of AI systems. This becomes especially clear in ESG compliance and AI-driven sustainability processes.

How Data Supports ESG Reporting and Compliance

The EU Corporate Sustainability Reporting Directive (CSRD), effective January 2025, underscores the importance of high-quality ESG data. It will expand reporting obligations from currently fewer than 12,000 companies to nearly 50,000. All companies in the EU with more than 250 employees, revenues over €50 million, or a balance sheet total over €25 million are affected. The CSRD also requires the collection and reporting of 1,400 critical data elements (CDEs). These requirements demand the highest data quality, as regulators expect precise and reliable reports.

Five key dimensions determine data quality: completeness, compliance, consistency, accuracy, and integrity. Errors in any of these areas can significantly undermine the credibility of ESG reports.

An example of the consequences of insufficient ESG data quality is the case of Goldman Sachs: In 2022, the SEC imposed a $4 million fine because the bank failed to implement adequate ESG policies and procedures, despite marketing its products as ESG-compliant.

"All elements of ESG reporting are really based on proper risk management. You cannot manage your risk if you don't know what your risk is. It's the risks that you don't know about that will be the problem, and you cannot do that without a data-driven and tech-enabled risk management approach."
– Barbara Porco, Director for the Center of Professional Accounting Practices at Fordham Business School

Clear data provenance ensures transparency and traceability, especially in materiality assessments. This is particularly relevant as 88% of executives see data quality as one of their biggest ESG challenges. At the same time, 40 to 50% of companies have not yet integrated ESG data into their processes.

In addition to meeting regulatory requirements precisely, using data to optimize sustainability processes plays a central role.

How AI Uses Data to Achieve Sustainability Goals

High-quality data forms the foundation for deploying AI in sustainability. Only with clean, well-structured data can AI systems deliver precise analyses and improve operational efficiency. In fact, such systems can reduce the effort for ESG reporting by up to 90% (UNEP).

Two examples illustrate the benefits of AI in this context:

• Google: With the help of DeepMind AI, energy efficiency in data centers was increased by 15%, and cooling energy consumption was reduced by 40%.
• UPS: The ORION system optimized route planning, saving about 38 million liters of gasoline and reducing CO₂ emissions by 100,000 tons.

Additionally, AI can analyze sustainability trends and emission patterns to predict future hotspots and optimize energy consumption. Real-time adjustments by AI help reduce peak loads, lowering both costs and emissions.

The importance of efficient AI systems is also evident in energy consumption: data centers use about 1% of global electricity, and ChatGPT-3 alone has already caused over 502 tons of CO₂ emissions. However, techniques like knowledge distillation can shrink AI models by up to 90%, enabling significant energy savings.

By integrating AI into sustainability management, companies can go beyond mere compliance and create long-term value. But the key remains data quality: only with clean, connected data can AI systems deliver reliable insights and help companies achieve their sustainability goals.

Common Problems in ESG Data Integration

Integrating ESG data into business processes is no easy task. Many companies encounter challenges that can hinder their sustainability strategies. According to a global survey, 41% of executives see insufficient data as the biggest obstacle to ESG progress. Around 70% of organizations also struggle with data consistency and validation issues. Below, we take a closer look at some of the main challenges, including isolated data silos, poor data quality, and adapting to new regulatory requirements.

Data Silos and Disparate Systems

Data silos occur when data is isolated within specific departments or systems and is not accessible to other parts of the company. This fragmentation hinders a comprehensive view of ESG issues and makes consistent reporting difficult. In fact, 42% of respondents said that large amounts of data remain hidden in such silos. Poorly integrated or isolated data can be costly—up to 30% of annual revenue can be lost as a result.

One example shows how integration can help: A mid-sized e-commerce company linked data from its CRM, email marketing platform, and customer service ticket system in a central dashboard. This led to 25% shorter response times, 15% more identified upselling opportunities, and a 40% reduction in time spent on manual data reconciliation.

"Sustainability offices have been collecting activity data on greenhouse gas emissions from across the company and sometimes from external suppliers for some time... While this data is valuable for compliance purposes, it remains inaccessible to others in the company who could use it for analysis and decision-making."
– Matthew Sekol, ESG and sustainability expert and author of the book ESG Mindset

The technical infrastructure is crucial here. Companies that use entity-relationship diagrams (ERDs) in their system design report a 40% reduction in data redundancy and a 35% increase in system maintenance efficiency. But even when data silos are overcome, ensuring data quality remains a major challenge.

Maintaining Data Quality and Accuracy

High-quality data is the backbone of any ESG strategy. But ensuring this quality across departments and sources is complex. Different collection methods and a lack of standardization often lead to inconsistencies that make validation difficult. Faulty ESG data can result in legal issues and reputational damage.

According to a survey, 54% of consumers avoid companies that make misleading sustainability claims, and 38% would withdraw their investments from such companies. Validating supplier data is particularly problematic, as external partners often use different standards for data collection and reporting. This makes data consolidation and verification significantly more difficult.

Adapting to New Regulations

Regulatory requirements in the ESG field are constantly evolving, presenting companies with additional challenges. For example, the CSRD requires the collection of over 1,100 data points—far more than previous standards. In addition, different countries and industries have specific requirements and timelines. German companies must comply with both EU regulations like the CSRD and national or industry-specific rules.

Another problem is the lack of standardized metrics. This not only makes performance comparisons difficult but also complicates informed decision-making for companies and investors. As ESG reporting is still a relatively new field, data comparability remains limited. While many companies are beginning to prioritize sustainability disclosures, they are often poorly prepared for the upcoming reporting obligations. Adapting to new regulations therefore requires not only technical adjustments but also organizational changes and targeted employee training.

Inconsistent ESG data can have serious consequences: from regulatory violations to loss of investor trust, operational inefficiencies, and reputational damage. To minimize these risks, clear data integration strategies are essential. The next chapter describes how companies can develop such strategies. But first, it is important to understand the challenges in detail to lay a solid foundation for sustainable data strategies.

Best Practices for ESG Data Management

Well-designed ESG data management is key to overcoming challenges in integrating ESG data. It improves data quality, reduces compliance costs, and creates a solid foundation for leveraging AI. Clear governance structures and defined roles play a central role here.

Defining Clear Governance and Roles

A well-structured data governance framework forms the basis for successful ESG data management. The Data Governance Institute describes data governance as “a system of decision rights and accountabilities for information-related processes, executed according to agreed-upon models which describe who can take what actions with what information, and when, under what circumstances.”

Defining clear roles in data management is essential. Here are some key roles and their responsibilities:

• Data Admin: Responsible for overseeing the governance program and resolving data-related conflicts.
• Data Steward: Acts as a bridge between business units and IT, standardizes data definitions, and ensures consistency. Laura Madsen aptly describes this role:

  “Data stewards were meant to help solidify the squishy… They speak the language of IT and translate that back to the business. The role requires the patience of a kindergarten teacher and the ability to negotiate a hostage situation successfully.”

• Data Custodian: Responsible for data access, storage, and security.
• Data User: Uses data to make informed decisions.

An example of clear role allocation is shown in the following table:

Once roles are defined, data processes should be closely linked to strategic business goals.

Aligning Data Processes with Business Goals

ESG data should not be viewed merely as a reporting obligation but as core business data. Microsoft is a good example of how ESG goals can be linked to corporate strategies. Microsoft’s goal to become carbon negative by 2030 supports UN Sustainable Development Goal 13 (Climate Action). At the same time, Microsoft promotes digital skills, supporting Goals 4 (Quality Education) and 8 (Decent Work and Economic Growth).

To effectively integrate ESG data into business strategy, it should be aligned with areas such as procurement, product development, logistics, HR, and finance. ESG metrics must be linked to operational KPIs, financial targets, and corporate objectives to play a role in daily decision-making.

Involving relevant stakeholders is crucial. Understanding their priorities and concerns helps identify key issues and set ambitious yet achievable ESG goals. Interdisciplinary collaboration improves data collection and integration, which in turn enhances data quality.

Building Data Traceability and Compliance

In addition to governance and process integration, data traceability is a key component of effective ESG data management. With increasing demands from consumers, regulators, and for risk mitigation, traceability is becoming ever more important. Data sovereignty plays a central role, as data is subject to the laws of the country in which it is processed. An expert sums it up:

“Data sovereignty is not just a compliance issue—it's a strategic asset that can either protect or jeopardize a supplier's long-term position in the global market.”

To implement this, first check which traceability information is already being collected. Then, suitable traceability solutions can be evaluated and internal policies developed. When selecting platforms, the following criteria should be considered:

• Local data centers to comply with regulations like the GDPR
• Granular access controls
• Robust encryption and regular security audits
• Automated compliance updates
• Open APIs for seamless data flow

Regulatory requirements deserve special attention. For example, in 2023, the German Federal Office for Economic Affairs and Export Control (BAFA) audited 468 companies under the Supply Chain Due Diligence Act. It continues to review aspects such as risk management systems, human rights officers, and complaint mechanisms.

Companies must apply the same standards to ESG reporting as they do to financial reporting. This means that the materiality assessment process must be substantiated with traceable data.

Software and Platforms for Data Integration

Choosing the right software for ESG data integration is a crucial step for German companies to comply with regulations and successfully implement AI-powered sustainability strategies. Modern platforms are the backbone of digital transformation in the ESG sector.

Key Features of ESG Data Management Software

Good ESG software must offer several core functions to meet complex requirements. For example, automated data collection is indispensable, as companies must collect up to 12,000 data points under the CSRD (Corporate Sustainability Reporting Directive). This automation minimizes manual effort and ensures more accurate data.

Another key element is real-time tracking, enabling companies to respond quickly to changes and make informed decisions. The regulatory compliance feature is also crucial: automatic updates on ESG regulations keep companies up to date—a vital support, as over 140 ESG KPIs must be collected and disclosed under CSRD.

Don’t forget data security and integration with existing systems. Robust encryption, detailed access controls, and seamless system integration protect sensitive ESG data and optimize workflows. Equally important is data provenance transparency, providing details on availability, ownership, security, and data quality.

In addition to these basic functions, specialized platforms offer further advantages.

The Role of Data Hubs in Data Integration

Data hubs serve as a central point of contact for ESG data, allowing companies to focus on analyzing and using data instead of spending time on manual preparation. This is especially relevant, as the average annual cost of CSRD reporting can reach up to €1.6 million. Automated data collection and validation help minimize errors and make processes more efficient.

A good example is the ESG Data Hub from BearingPoint, based on Snowflake. This solution offers comprehensive data management functions and addresses challenges such as inconsistent data quality, inadequate tools, and lack of transparency in ESG data. Similarly, the Osapiens HUB for CSRD demonstrates how specialized platforms can support German companies in CSRD-compliant reporting. Features include an AI-powered assistant, automated integration via REST API, decentralized data collection, and internal approval workflows with automated consistency checks.

These centralized data pools open up new opportunities to efficiently leverage different platform types.

Comparing Different Platform Types

The choice of the right platform depends heavily on a company’s individual needs. Cloud-based solutions offer flexibility and scalability, while on-premise systems provide more control over data. Large enterprises often benefit from customizable solutions, while small and medium-sized businesses (SMEs) tend to prefer user-friendly and cost-effective options. Modularity is also an important factor, as it allows for easy integration into existing systems.

When selecting a platform, companies should consider the following criteria:

• Use of local data centers for GDPR compliance
• Detailed access controls
• Robust encryption
• Regular security audits
• Automated compliance updates
• Open APIs for seamless data flow

Another important aspect is implementation expertise. Dr. Thomas Schlaak of Deloitte emphasizes:

“We believe our innovative cross-functional approach is decisive for the successful implementation of a robust sustainability strategy and reporting process. Bringing in experts from a vast array of disciplines really sets us apart from the competition—a critical success factor for our expert implementation approach.”

With these diverse software solutions, companies can successfully overcome the challenges of ESG reporting in Germany.

German Market Requirements and Considerations

German companies face unique challenges when implementing ESG and AI strategies. Regulatory requirements are constantly evolving, while data protection and transparency are highly valued.

Complying with German and EU Regulatory Standards

Regulatory requirements for ESG in Germany are largely shaped by the EU. One of the key changes is the introduction of the Corporate Sustainability Reporting Directive (CSRD), which will increase the number of reporting companies in Germany from 550 to 15,000. Across the EU, this number will rise to 50,000 companies. For many companies in Germany, this means building and implementing comprehensive ESG reporting for the first time.

“The new rules will hold companies more accountable for their impact on society and lead them towards an economy that benefits people and the environment. Data on ecological and social footprints would be publicly available to anyone interested in these footprints.”
– Jozef Síkela, Czech Minister for Industry and Trade

In addition to the CSRD, the German Supply Chain Due Diligence Act obliges companies to carefully review their business activities and supply chains. At the same time, compliance with the GDPR remains critical. Violations can be costly—up to €20 million or 4% of global annual turnover. A notable example: In 2018, Google was fined €50 million by the French data protection authority CNIL for insufficient transparency in data processing and inadequate user consent for personalized advertising. This ruling was later upheld by the French Council of State.

Other relevant regulations include the Sustainable Finance Disclosure Regulation (SFDR), the Climate Benchmark Regulation, and the planned regulation on ESG ratings. Companies must also prepare for the Digital Services Act (DSA), the AI Act, and the Data Governance Act. These developments show that ESG requirements are becoming increasingly binding—what was once “soft law” is becoming “hard law.” Companies must therefore adapt their data management systems early and strategically.

But it’s not just regulatory requirements—internal expectations for data handling in Germany also play a decisive role.

German Expectations for Data Handling

In Germany, there are clear preferences for how companies should handle data—and these go far beyond mere compliance. According to a 2024 survey, every second large company (with more than 250 employees) in Germany already uses AI systems. They prefer technologies developed in Germany to ensure data sovereignty.

Companies also rely on strict internal policies prohibiting the entry of sensitive information into AI systems running on third-party servers. This protects trade secrets. Transparency is another central aspect: German companies place great value on openly communicating the benefits and risks of AI so that the public is fully informed. This also includes emphasizing human oversight and protecting vulnerable groups from potential AI misuse.

A strong compliance culture is firmly anchored in German companies and is seen as both an investment in the future and a competitive advantage. Effective data protection requires a balance between technological innovation and strict compliance. Comprehensive due diligence and early consideration of the Data Act can provide crucial advantages.

These specific requirements of the German market make it clear that only well-structured and connected data can pave the way for successful ESG and AI strategies.

Conclusion: Clean Data Drives Sustainability and Growth

The previous sections have made it clear how important high-quality data management systems are for modern business strategies. Clean and connected data forms the backbone of successful ESG and AI strategies. Interestingly, 95% of executives see access to quality sustainability data as essential, but only 27% of companies actually have such data. This gap clearly shows that urgent action is needed—a point emphasized by experts like Steffen Müller.

“Reliable sustainability data is essential for making informed strategic decisions, identifying inefficiencies, and increasing revenue. High-quality data enables companies to monitor performance, identify environmental risks, and improve operational efficiency. Technological innovations such as AI and digital agents play a decisive role by automating reports, improving data quality, and providing real-time insights.”
– Steffen Müller, Global Director for Sustainability Advisory at Salesforce

Companies that invest in robust data management systems secure a clear advantage. By 2023, $480 billion flowed into ESG funds—a growth of 22.76% compared to 2021. The benefits are especially tangible in Germany: Extreme weather events caused estimated damages of €145 billion between 2000 and 2021. Investments in ESG data and AI technologies not only boost resource efficiency but also supply chain security. These challenges highlight why automating ESG systems is a decisive lever.

The combination of AI with ESG strategies, supported by the best practices in data management described above, goes far beyond regulatory compliance. Automated processes minimize errors, speed up reporting, and enable real-time, informed decisions.

“By investing in robust sustainability data management systems and leveraging technology, organizations can drive value creation and strengthen their competitive advantage in the market.”
– Hanish Patel, Managing Director at Deloitte Consulting LLP

High-quality data is the key to successful ESG and AI strategies. With growing regulatory requirements and increasing expectations for transparency, clean, connected data forms the foundation for sustainable growth. Companies can better manage risks, strengthen their brands, and make smarter decisions. Those who act now secure the best opportunities for the future.

FAQs

Why are high-quality and connected data indispensable for successful ESG and AI strategies?

Why High-Quality and Connected Data Are Essential

Data is at the heart of precise analysis and reliable decision-making—especially in the areas of ESG (Environmental, Social, and Governance) and Artificial Intelligence. Without a solid data foundation, projects risk being built on incomplete or faulty information. This can significantly undermine the validity of results and the effectiveness of actions. High-quality data, on the other hand, provides the necessary transparency to understand complex relationships, identify risks early, and implement targeted sustainable strategies.

Connected data also plays a central role when it comes to efficiently processing large volumes of data—a must for ESG reports and AI applications. They enable efficient digitalization, strengthen competitiveness, and help companies meet increasing demands for sustainability and transparency. Especially in Germany, where innovation is a key economic driver, a strong data infrastructure forms the foundation for long-term success and progress.

What obstacles exist when integrating ESG data into business processes?

Challenges in Integrating ESG Data

Companies face several tricky hurdles when using ESG data. A major problem is lack of standardization. Different data formats and lack of comparability make it difficult to create clear analyses. There is also often insufficient data quality or simply a lack of relevant information. This not only makes sound decision-making harder, but also complicates the creation of transparent reports.

Another issue is the integration of ESG data into existing business strategies. It’s not enough to just collect the data—they must be meaningfully integrated into business processes. Then there are governance issues: lack of transparency, missing control mechanisms, or the risk of greenwashing can burden the entire process.

To tackle these challenges, companies need well-thought-out data management strategies and suitable tools that help bring order to the chaos and make data truly usable.

How can companies ensure their ESG reporting meets current legal requirements?

ESG Reporting: Legal Requirements in Germany

As of January 1, 2024, companies with at least 250 employees in Germany must implement the requirements of the EU CSRD directive. Key requirements include the systematic collection and reporting of greenhouse gas emissions, especially CO₂ emissions, which arise from mobility and business travel, among other sources.

A well-thought-out data management strategy is indispensable. It ensures that qualitative and quantitative ESG data are consistently collected and documented. This has two advantages: companies not only meet legal requirements but also create transparent and traceable reports. Using specialized tools and platforms for data integration can make these processes more efficient and significantly simplify compliance.