At Etlia Data Engineering, we’ve partnered closely with our clients to develop efficient, automated data pipelines that streamline ESG reporting. As ESG reporting becomes a mandatory part of corporate responsibility, businesses face growing pressure to provide precise and transparent data. By leveraging Databricks for CO2 emissions reporting and Power BI for visualization, we create seamless solutions that offer valuable insights to support decision-making.
The Challenge: Moving away from manual processes
Carbon footprint reporting is becoming an essential part of every corporate ESG disclosure. However, for many organizations, the process is still labor-intensive, involving manual data collection, entry, and calculations. Automating this process significantly reduces errors, improves accuracy, and saves time, but it requires the right strategy and tools. Here’s how we tackled this challenge.
1. Defining your reporting targets:
Before you begin automating, it’s important to have a clear understanding of your reporting goals. At Etlia, we set up our clients’ systems to handle overall and granular-level CO2 calculations. This allows them to drill down into emissions from specific equipment components, logistics emissions, supplier emissions, or even individual processes, identifying the most impactful contributors to their overall carbon footprint.
2. Assessing your data and data sources:
The quality of your carbon footprint reporting is only as good as the data behind it. Therefore, evaluating your data sources is critical. In many cases, organizations need to pull data from multiple systems—ERP, Factory data, common coefficient external data, energy management systems and supplier data sources to get a full picture. To ensure data accuracy and reliability, we conduct a thorough assessment of your existing data sources, identifying potential gaps and inconsistencies. This assessment helps us determine the most appropriate data collection and integration methods to optimize your carbon footprint reporting.
3. Selecting the right technology stack:
Usually, it makes sense to follow your organizations’s architecture and technology guidelines for any new data domains. At Etlia we have experience of building data pipelines with most of the leading technologies.
In our experience e.g. Databricks is a good choice as the backbone of data processing due to its ability to handle large volumes of structured and unstructured data. Databricks gives the flexibility to model the complex hierarchical data structure using PySpark, helped to speed up the development of the pipeline
For visualization we usually recommend Power BI as the infrastructure is well fit within Azure framework commonly used by Finnish organizations. Once the data is processed and the carbon footprint contributors identified, Power BI enables clear, interactive dashboards that stakeholders can easily interpret and act upon.
4. Data modelling for CO2 calculation:
At the core of our solution is a hierarchical data model that supports multi-level CO2 emission calculations. This model allows for both high-level overviews and granular insights into specific emission sources. We integrate external datasets for CO2 emissions factors, ensuring that the data model could adjust automatically as new data was ingested. It is very likely that other tools may also be used in parallel, and our solution is designed to seamlessly integrate with these tools, providing a comprehensive and flexible approach to CO2 emission management.
5. Developing the solution: start with an MVP:
One of the key lessons we have learned is the importance of starting small and scaling over time. We usually begin by developing a Minimum Viable Product (MVP), focusing on automating a single reporting process. This helps us to identify the dependencies, missing data sources and required stakeholders to productionize the pipeline.
The MVP approach allows our clients to see immediate benefits of reduced manual workload and improved data accuracy while keeping the project manageable.
6. Continuous improvement and scaling the system:
Once your MVP is successful, you can work on gradually expanding the system’s capabilities. This includes integrating additional data sources, refining the data model, and enhancing the Power BI dashboards with more sophisticated analysis and forecasting capabilities. As the system scales, so do the benefits, enabling more comprehensive and actionable CO2 reporting.
Implementing automated carbon footprint reporting provides considerable long-term benefits, enabling organizations to fulfill their ESG commitments more efficiently while also saving time and minimizing errors. From our experience, modern tools like Databricks and Power BI significantly streamline and improve the reporting process. Whether you’re beginning or seeking to enhance your current system, automation is essential for effective and precise CO2 reporting.
Discover the benefits of automating your ESG data pipeline in our latest blog.
Interested in taking the next step? Contact us to discuss how we can help automate your ESG reporting processes.
