Sustainability reporting has progressed rapidly in many companies. Targets have been set, the materiality assessment has been carried out and the first CSRD-compliant reports have been written. As the reporting cycle approaches, however, companies notice that the figures and ESG data do get compiled, but no one necessarily knows exactly where they come from.
At this point, sustainability reporting turns into data work. The quality of reporting depends on how data is created, collected, processed and combined. Without a shared data foundation, ensuring an audit trail and data reliability becomes laborious.
A familiar situation: data flows through Excel
When a company has several business units, ESG data is typically collected from many different places. Each business unit has its own ERP, HSE and HR systems, and the figures taken from them are combined for reporting purposes in various Excel tools. Only after that does the data end up in group reporting for consolidation.
The structure is understandable: it has often emerged from the need to move forward with reporting quickly and efficiently. Problems start to appear when reporting is required to be reliable:
- The data is inconsistent. The same figure is calculated differently in different business units, and no one has jointly defined what it means.
- The audit trail is unclear. When the auditor asks where a figure comes from, finding the answer takes days.
- Knowledge sits with a few people. When the person who built the Excel moves to another role, the know-how leaves with them.
Architecture and master data first, then automation
Many companies dream of reporting directly from the data warehouse through Power BI, without juggling Excel files. The goal is good and entirely achievable once master data is in order and the system architecture has been properly modelled. It is important to define the content, source system and owner of key data attributes. This makes it possible to plan precisely which systems data should be collected from, and to ensure that the data is consistent, comprehensive and reliable.
For example, if item data is incomplete and it is unclear which systems data should be collected from, reporting may remain incomplete and its quality suffers. This is why the order matters. Once data flows, calculation logic and responsibilities have been described transparently, building automation is more straightforward and assurance goes smoothly without surprises. In addition, well-managed master data serves other needs as well, such as implementing the Digital Product Passport or the EU Packaging and Packaging Waste Regulation.
Getting ESG data in order, from foundation to target state
Getting ESG data in order doesn’t require a years-long project or a new system right away. We typically proceed in three phases.
- ESG data foundation. We map the systems where data is created across business units and chart the data flows from source to report for the most important data points. At the same time, we document the current controls and audit trail. We also create an ESG data standard that describes each data attribute so that all business units speak the same language.
- Pain points and development opportunities. We identify risks related to data flows and prioritise the findings by impact and effort. The priorities are reviewed with the company’s key people and mirrored against ESRS and assurance requirements.
- Target state. We define the target data flows along with their controls, roles and responsibilities. The calculation logic of each data point is documented. Finally, we build a roadmap of concrete, feasible actions that sustainability, IT and business can all commit to.
As a result, the company has architecture and data flow descriptions, a data standard, a list of controls, descriptions of calculation methods and a roadmap. These serve as the foundation for the next reporting cycle and future development projects.
If you’re wondering where to start, get in touch. We’re happy to help you take your ESG data from source to report in a way you can trust.