How Technology Powers a Connected ESG Operating Model

Improving data quality in Environmental, Social, and Governance (ESG) reporting is essential for enabling credible, actionable, and transparent sustainability practices. Why? Because ESG data underpins:

• Strategic decision-making
• Regulatory compliance and external assurance
• Stakeholder trust
• Mitigation of greenwashing risks

Our multi-layered approach considers all of the above, along with People & Culture, Processes & Governance, and Technology & Data. Most Life Sciences companies hope to solve the technology challenge by ordering a new piece of software, but most likely this will not do the trick and will often further complicate an already complex dilemma.

In the ESG blog series so far, we have touched upon our approach, establishing clear governance and frameworks, and the roles that help Life Sciences companies transform ESG from a reporting exercise into a strategic business capability. In this final entry, we look at the critical role of technology in powering a connected and automated ESG operating model, and how our experts have played their part in leveraging these technical capabilities for our clients.

The importance of architecture and technical capabilities 

Usually, Life Sciences companies identify the most relevant KPIs and data points set under the Corporate Sustainability Reporting Directive (CSRD) from the European Union and other frameworks. Typically, companies begin with a materiality assessment, a process used to identify, evaluate, and prioritize the most relevant ESG topics that are essential for an organization’s success.

Similarly, before introducing new technology capabilities, it is essential to evaluate the digital maturity of existing systems. Gap analyses help align technology investments and shape a prioritized strategic roadmap. This should never be left out of the evaluation process, as gap analyses also highlight where investment is critical for reporting success.

Tenthpin applying a gap analysis successfully

To avoid redundancies and improve organizational efficiency, sustainability reporting processes can be orchestrated through a Community of Practice. These are groups of people who share a common profession, passion, or interest (in this case, Life Sciences and ESG). Often, they come together regularly to solve inherent problems, share critical knowledge, and continuously improve their skills through social interaction and collaborative learning.

As an example, we leveraged a gap analysis to help one of our clients to utilize and build on their existing technology stack without introducing redundancies. All customers we worked with already had reporting and dashboarding capabilities that we extended to incorporate additional internal or external data, such as estimated carbon emissions, while maintaining audit-proof traceability and reporting.

The role of Unified Data Models and data governance in the Sustainability Data Hub

Effective sustainability reporting depends on trusted, well-governed data. As ESG requirements grow, organizations need to bring together information from HR, finance, operations, procurement, and other functions without creating duplicate data sources or parallel reporting processes. A Unified Data Model (UDM), supported by strong data governance, provides a single, consistent foundation for sustainability reporting while enabling existing business data to be reused across multiple reporting requirements.

Some of our clients have considered a dedicated sustainability reporting solution. We highlighted the risk of redundant data and duplication in collection processes. For example, employee data is often already reported by the HR function and recollecting it for sustainability purposes can increase complexity and costs; while risking user acceptance when faced with multiple (often varying) versions of the same data.

We also worked with our partners to enhance the existing reporting landscape with a virtual Sustainability Data Hub concept based on a unified data model, enabling efficient integration, consistency, and scalability across the organization.

Tenthpin leveraging advantages of a UDM 

Our customers have seen significant advantages from adopting a UDM. By integrating disparate data sources into a single, virtualized, and consistent structure, they achieve a reliable foundation for data-driven operations and overcame data silos.

The UDM has enabled our partners to enforce consistent definitions and standards at the transactional level, ensuring homogenous structures and metadata across datasets while data remains anchored to and owned by its respective teams. By creating standardized entities, attributes, and the relationships provided in the form of productionized Data Products, the UDM became a central source for accurate analytics, business intelligence insights, and AI/ML model training across systems.

Customers also report reduced redundancy and improved efficiency, as multiple data extractions and storage efforts are minimized. Data is now more accessible, consistent, and interoperable across functions. Importantly, the UDM ensures that data ownership remains with the function generating it, preserving accountability while streamlining reporting and analysis.

Tenthpin implementing a Sustainability Data Hub

For one of our clients[CL1] , we implemented a Sustainability Data Hub with a structured, multi-zone data architecture for managing ESG data effectively. By organizing data into distinct zones, our customers can ensure traceability, quality, and accessibility while supporting analytics, reporting, and decision-making. Similar to the data engineering Medallion Architecture, each zone served a specific purpose in transforming raw data into high-value insights and actionable information.

Here’s how each of the zones worked:

• Zone one: Data is ingested in the RAW DATA Zone in its source system format as a snapshot. This serves as the staging layer allowing traceability of origin for all data points, while establishing a data history. Basic data validation checks (e.g. a count check, mandatory fields) are performed to make sure all extracted data has landed as expected. Data can be stored in original source format, or in parquet files, and audit fields may be added as required.
• Zone two: Once the data is ingested, it’s then abstracted into the ENRICH Zone in order to identify and address data quality across systems. Data is cleansed, unified, and combined to create common data models and file types into meaningful business objects, rather than raw tables and fields.
• Zone three: The CURATE zone is where data from the ENRICH zone is integrated across business domains and is curated to include descriptive, prescriptive, predictive analytics metrics. Data tables are created for data consumption and designed for multiple stakeholder groups and tools, allowing easy and flexible access for business users.
• Zone four: The PROCESS Zone consists primarily of Analytics and AI/ML tools or calculation engines for sustainability KPIs with some data storage to support functionality. This zone’s entities can have their own databases, utilized to load new data and store outputs created as part of the exploratory process.
• Zone five: The SERVE Zone houses unique use-case-specific data delivered as a product that ensures its high data quality, semantic richness and compliance, ready to be used and analyzed.
• Zone six: These products are detailed and made available through a business-friendly data catalog and available for consumption by a variety of reporting and visualization tools in the PUBLISHING Zone.

By implementing a zoned data architecture, organizations can move from raw, fragmented data to trusted, actionable insights. Each zone—RAW, ENRICH, CURATE, PROCESS, and SERVE—adds value by ensuring data quality, consistency, and accessibility while supporting analytics, reporting, and stakeholder needs.

Conclusion 

Unified data models and strong data governance deliver significant value in their own right, whether supporting HR, finance, supply chain, quality, or other business functions. Sustainability reporting provides a compelling catalyst to extend these capabilities across the enterprise, creating a shared foundation for both operational and ESG reporting. Rather than building parallel sustainability data structures, Life Sciences organizations can leverage the same trusted data assets to serve multiple business objectives, increasing consistency, reducing duplication, and improving decision-making across functions.

When sustainability and operational reporting are built on the same unified data foundation, the value extends far beyond regulatory compliance. Organizations benefit from centralized governance, streamlined access management, faster data availability, improved data quality, and common data standards that support analytics and future digital initiatives. In many cases, these long-term enterprise benefits provide a stronger return on investment than implementing a standalone sustainability reporting solution, enabling faster scalability as reporting requirements continue to evolve.

 *The authors would like to thank Yvonne Kirner, Partner at Tenthpin, for her support and insights that were critical in the writing of this article.