Why automate your ESG data pipeline and how to do it?
While requirements for ESG reporting for businesses are tightening many organizations are still struggling with inefficient manual reporting processes that compromise the quality and assurance-readiness of ESG reporting.
It is not always easy to find actual data for ESG KPIs – hence manual data input and calculation logic based on e.g. emission factors, averages and standard rules will be reality for some parts of ESG reporting also in the near future.
Based on our experience, organizations can improve their reporting process significantly by gradually automating ESG data pipelines wherever possible – this brings immediate benefits by improving the efficiency of the reporting process as well as allowing better accuracy of your ESG reports and transparency into underlying data.
At Etlia Data Engineering we have successfully implemented automated ESG data pipelines for our clients and in this blog, we dissect our key learning points based on our experiences.
Why consider automating your ESG data pipeline?
Main benefits our customers have achieved by automating their ESG data pipeline:
- Transparency and assurance-readiness: Automating data pipeline from operative systems helps ensure ESG reports comply with regulatory requirements and provide audit trails for accountability and transparency.
- Cost optimization: Reducing the need for manual entry of ESG data, for example using Excel files lowers labor costs and minimizes the cost impact of errors and delays.
- More up-to-date ESG reports: Automation significantly reduces the time required to gather, process, and update data, enabling real-time or near-real-time reports allowing management to take action faster than with manual process.
- Superior data quality: Automated ESG data pipeline is remarkably less error-prone compared to manual processes.
- Scalability: An automated ESG data pipeline can scale-up and handle increasing volumes of data as the company grows, unlike manual processes that struggle to scale efficiently.
What are the biggest challenges?
The most common hurdles our clients are facing when building ESG data solutions:
- Inaccuracy and lack of transparency: In the worst-case manual data processes and calculations will cause your ESG reporting assurance to fail ➤ solution: Try to automate your ESG data pipeline whenever possible in order to ensure transparency and audit trails.
- Complexity of data: ESG data is usually stored in business process solutions that have been optimized for running daily operations instead of ESG reporting ➤ solution: find skilled enough partners who can help design, model and implement data architecture for ESG reporting.
- Internal data gaps: It is often difficult to find all the data needed e.g. for preparing a comprehensive emissions calculation ➤ solution: use designated ESG specific solutions or approved industry practices to complement your calculation process.
- Dependency on data provided by suppliers: Usually you need to get some data from your suppliers and often this becomes an issue when preparing ESG reporting ➤ solution: try to get the necessary data from your suppliers if possible. Sometimes a more viable solution is to use industry standard calculation rules or data ecosystems in order to fill in the gaps.
- Knowledge issues: internal politics and siloes can hinder finding an optimal solution if the stakeholders do not have needed understanding of the ESG requirements or interlinked data architectures ➤ solution: make sure to train your internal experts and to take care of internal knowledge sharing.
- ESG reporting solution not aligned with overall data strategy and architecture: This can happen for example in case the team in charge of ESG reporting is building their own solutions in isolation ➤ solution: tight coordination between ESG organization and business IT data solution owners/architects.
How to do it?
These are our recommended steps to automate your ESG data pipeline
- Get started: The sooner you start building automated data flow from operative systems the better it will be for managing the overall roadmap, as it will take time and substantial investments. It is best to get started and move away from manual processes gradually.
- Build your understanding: Understanding of the KPIs and ESG reporting requirements such as EU CSRD is crucial, as they help to define the data needed to build the ESG pipeline.
- Define targets: Define stakeholders’ targets and roadmap for your ESG reporting development.
- Assess your data and data sources: First, define the data you can get from internal sources and whether there is a need for external data. A good example in the case of the process industry could be that you need material information from suppliers and external data for the coefficient from other providers. The exercise of understanding source data and systems helps to determine if you could stay with existing data architecture or do you need a new one to support the ESG pipeline.
- Select technologies: Choosing the right platform for your ESG data is crucial considering the maintainability and complexity of data sources. You may be attracted to use tools that have fancy pre-defined templates but be aware, 1) this does not remove the need for having a proper data platform and 2) these tools might have other limitations such as very specific requirements for overall architecture that could be in conflict with your organization’s guidelines.
- Data modelling: Start with an analysis identifying how much data is available to build your ESG pipeline. Data modeling for ESG will require combining the data from your systems with reference data (for common data and coefficients) to calculate your emissions and other KPIs. You should expect the model could probably contain hierarchical traversing to calculate the emissions on all granularities to identify which is the major contributor, and this could also be a decider in choosing your architecture.
- Solution development: Ideally the development process should follow your organization’s common process for building data solutions. At Etlia Data Engineering we always recommend agile development methodologies.
- Gradual development: Start Small. Due to the complex nature and limited availability of the data it’s a good approach to proceed modularly and build your solution step by step automating one part of the data flow at a time.
– Raaju Srinivasa Raghavan & Mikko Koljonen
Are you ready for ESG data automation? If you have any questions or need support in your ESG data process don’t hesitate to reach out to us by booking a short meeting!
