Managing ESG data can feel like building a LEGO masterpiece without all the pieces. Like LEGO bricks scattered across the house, ESG data is often dispersed across multiple systems and supply chains, making it challenging to organise and report accurately. With regulations such as CSRD placing mandatory reporting requirements on organisations, the need for comprehensive and traceable data has never been more critical. This guide explores how businesses can effectively manage ESG data, ensuring compliance and supporting long-term sustainability goals.
Is ESG data like LEGO across your house?
Storing LEGO can be a nightmare. Pieces get mixed up, scattered under the sofa, lost in drawers, or hidden in kids’ pockets. Building a model that matches the instructions becomes almost impossible, often requiring hours to find and sort the pieces into colours and shapes. Inevitably, some key pieces go missing and the Hogwarts Castle remains a dream without buying new bricks or starting over.
Similarly, managing ESG data can feel like building a LEGO masterpiece without all the pieces. Businesses need to report on many metrics across environmental and social domains but the data is often dispersed across multiple systems and the supply chain, or it may not even exist at all.
The challenge of being “assurance-ready”
This fragmented data collection and reporting approach poses significant challenges, especially as regulations like CSRD, UK SDS, and ISSB impose mandatory external reporting and auditing requirements. Auditors must trace data back to the source, demanding comprehensive and accurate records.
Much like organising a LEGO clean-up, preparing ESG data for reporting can revert to chaos without an enduring capability and the right systems to manage it efficiently.
A one-off clear-up isn’t enough – entropy is inevitable
‘Entropy’ is a scientific term that refers to the universe’s trends towards disorder. LEGO is a classic example. Even if you did neatly organise all the pieces to build Hogwarts, they end up scattered and mixed up again after a few weeks.
It’s the same with ESG data. You’ll put hard work into preparing your ESG data for CSRD reporting as a one-off exercise. But without an enduring capability and the right systems to manage this efficiently, the data will revert to entropy.
Top five priorities to set up your robust ESG data management
1. Be clear on compliance and assurance requirements
If someone checks your LEGO model, you must find out what they’ll look for. Will it be sample-based? Is it ok if the pieces are right but the colours don’t match? The same goes for ESG data.
Use your materiality assessment and engage your audit upfront to agree on priority data points and determine their sampling approach—how deep will they go? How many sites will they examine? What constitutes a “pass”?
2. Organise your data and flush out your gaps
Lay out the ESG data you do have. This isn’t straightforward, as CSRD requires a combination of numeric and narrative metrics, so it won’t be obvious where this information lies. Once you’ve found it, create a ‘data lineage’ map, which visualises where data is and how it flows between systems.
Figure out what you need. Regulations like CSRD require a mix of numeric data, policies and narrative. Are the data definitions clear? Do you need it for all products/sites or just a sample? Which are mandatory versus voluntary? It’s important to share key data gaps and the plan to address them with leaders.
3. Create an appropriate data process before implementing systems
Getting a system can feel like the answer – but if your data isn’t accurate, then a data platform won’t be the answer.
Before investing in ESG data platforms, you must have a clear process for collecting and checking data, with clear roles and responsibilities. Who is responsible for providing and maintaining data for specific use cases?
We suggest running workshops to map out data processes and then overlaying source systems, data owners and status. This provides the basis for any system requirements and design.
4. Generate an automation roadmap
AI presents an opportunity to automate many things in data processes and you should consider automating manual efforts to extract, transform and load ESG data.
To do this, you need a roadmap to determine which data use cases are suitable for automation, which are the highest priority, and which should be used for proof-of-concept. This exercise should involve collaboration between data, technology and sustainability teams.
5. Visualise and share your creations
You’ve built your LEGO model – now it’s time to share your creation! Pulling all this data together should add significant value to the business, and not just be a reporting exercise. Consider visualising the key insights in creative and engaging ways. For example, you could create leaderboards or infographics on the number of recycled materials used in different products, or the energy efficiency of different operational sites.
Exposing data to the business will also encourage data providers to maintain data quality on an ongoing basis, not just for reporting deadlines.
For your sustainable future
To ensure long-term compliance and efficiency, businesses must start to implement these robust data management practices for ESG. By doing so, companies can meet regulatory demands and enhance sustainability and operational performance, transforming chaos into order and turning ESG data management into a strategic advantage.
We’re committed to helping organisations meet their ESG challenges. If you’re in need of support on your ESG data, get in touch.
