Sustainability Data Management White Paper

Best Practices for Sustainability Data Management

Updated on: October 21, 2025

Executive Summary As sustainability expectations increase across the food and agriculture sector, companies face rising pressure from stakeholders and regulators to collect, manage, and report reliable sustainability data. This white paper explores the drivers behind the demand for transparency, the common challenges businesses face in data collection, and best practices for sustainability data management.

With a focus on actionable solutions, we provide insights to help organizations address these obstacles, ensure compliance, strengthen stakeholder trust, and unlock opportunities for future growth.

Introduction For the food and ag sector, long-term success is often managed, in part, by building a robust sustainability strategy. Companies are increasingly expected to not only improve their sustainability practices but also to clearly report on their progress. Stakeholders, such as investors, customers, and local communities, are requesting detailed and trustworthy sustainability data. This data covers a broad spectrum of topics, from greenhouse gas emissions and biodiversity initiatives to sustainable packaging and ethical sourcing practices.

However, managing sustainability data is challenging. Many organizations struggle with scattered data, poor data quality, and limited analytics, which can affect their decision-making and accuracy when reporting on sustainability. Also, new regional and international regulations are introducing stricter requirements for sustainability reporting, highlighting the need for strong data management.

This white paper provides guidance to companies looking to improve their management of sustainability data. By tackling common challenges, companies can meet expectations for transparency and use data to support their sustainability efforts, ensure regulatory compliance, and help strengthen their market position.

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Best Practices for Sustainability Data Management

The Drivers for Sustainability Data Collection Companies are driven to collect sustainability data by several key factors that are reshaping the business landscape. Each of these drivers underscores the importance of collecting and effectively managing sustainability data and highlights the complexities involved in doing so. Understanding these drivers is crucial for companies looking to align their operations with the expectations of today’s market and regulatory environments.

Driver #1 - Stakeholder Requests

Ag industry customers, retailers, and restaurants are increasingly requesting supplier data on environmental and social impacts to support their own reporting. This may include information on greenhouse gas emissions, ethical sourcing, or the adoption of on-farm practices that support biodiversity and healthy soils.

Suppliers that provide this information and collaborate with their customers to reach joint sustainability goals can deepen their customer relationships and create new sales opportunities.

Driver #2 - Emerging Regulations

Domestic and international regulatory frameworks are rapidly evolving to require sustainability disclosures.

These regulations continue to push companies toward greater transparency and accountability for their business practice impacts.

Examples of emerging regulations requiring sustainability disclosures include: » California’s climate bills (SB 253 and SB 261), which require certain companies to disclose their GHG emissions and climate-related financial risks. (See here for more information on navigating California’s Climate Accountability Package.) » Extended producer responsibility (EPR) bills, which require companies to take responsibility for the entire lifecycle of their products by funding and participating in statewide collection and recycling systems for post-consumer waste. » The European Union’s Corporate Sustainability Reporting Directive (CSRD), which requires sustainability disclosures for certain companies, including information about how their activities impact people and the environment, as well as how environmental and social issues affect their businesses.

Driver #3 - Company Commitments

Many companies are setting their own ambitious sustainability targets, such as reductions in greenhouse gas emissions or water use. These voluntary commitments are often part of a broader strategy to reduce impacts, enhance corporate reputation, align with stakeholder values, and prepare for anticipated regulatory changes. Tracking sustainability data helps these companies identify opportunities to make progress towards reaching their targets.

Driver #4 - Marketing Claims

In today’s competitive market, sustainability attributes increasingly serve as differentiators for products.

Many companies aim to make claims about the environmental and social impacts of their products to retain current customers and capture new markets. This approach requires collecting detailed, product-level sustainability data. Other companies track broader sustainability data to make claims about their companylevel impacts and demonstrate ongoing improvements.

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Building a Strong Sustainability Data Foundation As companies work to meet increasing expectations for detailed sustainability reporting, they often face challenges managing the necessary data. These challenges can come from outdated systems, company structures, and processes that weren’t designed for the complex needs of managing a diverse, nonfinancial dataset. Below are three of the most common challenges faced by companies and best practices for overcoming them.

Challenge #1 - Data Silos

Sustainability data is often stored across disconnected systems and departments.

This fragmentation can lead to several problems: » Difficulty in data aggregation: Pulling together data from multiple sources for sustainability calculations becomes a cumbersome and time-consuming task which places a heavy burden on sustainability managers and increases the risk of errors. » Collaboration barriers: Without a centralized system to facilitate data exchange and communication, collaboration on data entry and review is hindered, which can affect the accuracy and completeness of data. » Inconsistency in data handling: Each time data is retrieved to perform a calculation or respond to a sustainability request, the lack of standardized data handling methodologies increases the risk of errors.

This can also make audits harder to perform. » Underutilization of data: Given the significant effort and time required to utilize sustainability data, many companies miss the opportunity to analyze it for strategic decision-making. This prevents the company from obtaining the most value from its sustainability-related work.

Best Practices: » Create one central system to store all sustainability data so everyone can work from the same version.

This could be as simple as an Excel sheet or as powerful as a cloud database. Creating this “single source of truth” makes it easier to review data collection progress and share consistent updates across the organization. » Identify where each dataset comes from and who is responsible for updating it. Making responsibilities clear reduces the chance of missing or incorrect details. » Set up a regular routine for moving information into your central system so it stays current and complete. This helps maintain quality by avoiding outdated details and ensures decisions are based on reliable information. » Provide shared reports and summaries so teams can see the same information and use it to make informed decisions. This helps your company identify trends and plan ahead instead of being reactive.

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Best Practices for Sustainability Data Management

Challenge #2 - Poor Data Quality

In the absence of a centralized sustainability data repository, many companies struggle with ensuring the reliability of their data. When data is sporadically collected and not regularly reviewed, it becomes difficult to trust the information to make critical business decisions. This lack of reliable data can undermine efforts to accurately report on sustainability and to use data-driven insights to drive strategy.

Best Practices » Collect the right data at the right time and level of detail by matching what you gather to the needs of the business. Examples of these business needs include meeting regulations, answering stakeholder questions, or tracking company goals. This ensures that your sustainability data is sufficient to meet business requirements while avoiding unnecessary complexity. » Assign responsibility for checking the accuracy and completeness of each dataset to the people who are closest to its source and know it best. After the information has been reviewed, it should be secured to prevent changes without proper authorization. This ensures reports and decisions are based on accurate data. » Organize and clean all information so it follows the same rules for names, units, and time periods.

This makes analysis much easier because information from different sources can be directly combined and compared. » Document all calculations and assumptions so everyone can understand the logic behind the results.

This builds trust in your data and prepares your company for potential audits or assurance requirements.

Challenge #3 - Inadequate Analytics

Without dedicated systems for analyzing sustainability data, companies face significant challenges in identifying trends and outliers, as well as creating projections. Many companies find themselves in a reactive stance, primarily responding to external requests and regulations without the ability to effectively use historical data to influence future sustainability performance. With more robust analytics tools, companies can take a more proactive stance on their sustainability strategy, unlocking the full value of their sustainability data.

Best Practices » Choose a single, reliable business intelligence software to provide summaries and visuals throughout your company. This ensures the same logic is applied to all reports and creates a common view of performance. » Focus on business needs by identifying the decisions each group must make and designing reports to provide the information they need. This is essential for clarifying which details matter most and avoiding confusion. » Train all report users so they understand how to interpret and act on the results. This will help you maximize the value of your data by encouraging its use in making critical business decisions. » Set a regular schedule for reviewing key performance metrics, looking at trends and unusual changes, and deciding on next steps. This supports a shared understanding of performance across teams and drives consistent, data-driven improvement.

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Guiding Questions for Improving Data Management Companies can improve their sustainability data management practices by critically evaluating their current systems and processes and asking key questions to identify areas for improvement and strengthen their strategy. Following are some examples: Data Collection

» What is driving sustainability data collection at your company, and what specific data is necessary to meet those needs? Understanding the core motivations, such as regulatory compliance, stakeholder demands, or internal targets, will clarify which data points are essential. » Where is the necessary data currently located? Identifying the sources of data, including the storage location and who controls it, is crucial for determining the methods required for effective data collection. » Who is most knowledgeable about each dataset? These individuals can serve as internal champions who ensure the accuracy of data collected. » How can data be transferred to a central location most efficiently and on a regular basis? Streamlining the process of data transfer minimizes the risk of data becoming outdated and irrelevant, thereby increasing its utility. » Is the data collection frequency and granularity sufficient to support the intended use cases? Ensuring that data is collected with the appropriate level of detail and at necessary intervals is vital for making informed business decisions.

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Best Practices for Sustainability Data Management

What is a data model?

A data model is a structured framework that organizes data elements and defines how they relate to one another. In practice, it often consists of multiple data tables that are linked by common fields (keys) to represent relationships between them. For example, one table might list facilities while another lists the products produced at those facilities, both joined by the facility ID.

Data Analysis

» What data preparation or cleaning is needed to standardize the data format and create a consistent data model? A standardized format facilitates more efficient data analysis and integration. » Which central system will store the data in a structured format? Whether it’s a sophisticated database or a simpler solution like a single Excel workbook, the choice will depend on the size and complexity of the company’s sustainability program. » Which business intelligence tool will be used to connect data sources and produce reports and dashboards? Selecting the right tools is essential for extracting actionable insights from the data.

Common dashboarding tools include Microsoft’s Power BI or Salesforce’s Tableau.

Reporting

» Who is trained to use the business intelligence software and analyze the data? Ensuring that employees are well-equipped to use the business intelligence tools and interpret the data is crucial for effective reporting. » What are the use cases for the data? Identifying specific business functions that could benefit from sustainability data insights can help improve the impact of reports and dashboards. » How are end users (executives, operations managers, salespeople, etc.) trained to understand and use the insights from the reports and dashboards? Training company decision-makers on how to use the insights generated from sustainability data is key to ensuring that the insights influence the company’s strategy and operations.

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Importance of Auditability and Analysis For companies eager to enhance their sustainability initiatives, it’s critical to ensure their data is auditable and analyzed effectively. This effort is about more than just fulfilling regulatory or stakeholder expectations; it’s about making data-driven decisions a core part of the organization.

By prioritizing these areas of data management, companies can not only tackle existing sustainability challenges more effectively but also anticipate and adjust to future trends. Adopting this proactive approach is essential for any company that aims to lead in sustainability and stay competitive in their industry.

Auditability

» Consistency and transparency in data entry: It’s critical for companies to have systems in place where data is entered regularly and directly by those who are closest to its source. This practice minimizes errors and ensures that the data reflects the actual conditions as accurately as possible. » Regular validation and locking of data: Data should be frequently validated by knowledgeable personnel to ensure its accuracy and integrity. Once validated, data should be locked to prevent unauthorized modifications, thereby maintaining its quality over time. » Transparent calculation logic: The methodologies used for calculating sustainability metrics should be consistent and transparent. This transparency is crucial for audits, as it allows external auditors to easily verify and trust the reported data. A clear, audit trail from data entry through reporting builds credibility with stakeholders and regulatory bodies.

Analysis

» Standardization and cleaning of data: Before analysis, it’s essential to clean and standardize data to ensure that it’s consistent (using common units, time periods, etc.). This process makes data from different sources directly comparable, enhancing the reliability of any insights gained. » Structured data storage: Storing data in a structured format such as databases or well-organized tables is essential for efficient data retrieval and manipulation. » Centralized data systems: Having a centralized system for data management allows analysts to build comprehensive data models. These models are crucial for comparing datasets from different sources and deriving insights about the company’s sustainability performance. » Deployment of business intelligence tools: To make data insights accessible to non-experts, companies should invest in business intelligence software. These tools can transform complex data sets into understandable reports and dashboards. By presenting data in a user-friendly format, such as charts and other visualizations, these tools help executives and managers make informed decisions.

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Best Practices for Sustainability Data Management

Conclusion Effectively managing data is essential to any company aiming to advance its sustainability goals. This white paper has outlined the main drivers for collecting sustainability data, the challenges that companies face in managing it, and key practices that can help improve data management.

For companies that feel overwhelmed by the complexities of updating their data management strategies, the guidance here is practical and actionable. By breaking down data silos, improving data quality, and strengthening data analytics, while also focusing on auditability, companies can drastically improve their data management and sustainability outcomes.

As the business environment continues to evolve, now is the time to act. Companies that positively contribute to global sustainability efforts, improve their operational effectiveness, and drive forward meaningful outcomes will be better positioned to meet stakeholder expectations and present themselves as leaders in a more resilient, transparent, and sustainable future.

Have questions? Contact a Pinion Sustainability Advisor today to further explore best practices for managing your sustainability data.

About Us

Pinion is a business advisory provider, ‘U.S. Top 100’ accounting firm, and global leader in food and agriculture consulting. With roots dating back to 1932, the firm aims to deliver increased value and growth for clients through its specialized advisory in the areas of sustainability, farm programs, land and water management, financial management, succession planning, government affairs, and business strategy.

Dedicated to sustainability program success — from small food and agriculture operations to large industry leaders — Pinion develops measurable and actionable sustainability programs that are scaled to satisfy the unique needs of each operation. Pinion’s sustainability advisors create customized solutions that meet the standards, reporting needs, and programmatic execution required to attain business goals and maximize sustainability impact.

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