How can you effectively manage data in circular economy reporting?

Companies subject to the Corporate Sustainability Reporting Directive (CSRD) will have to report according to the European Sustainability Reporting Standards (ESRS) developed by EFRAG. If resource use and circular economy turn out to be material according to a double materiality assessment, they would need to follow ESRS E5 which includes metrics and targets for both resource inflows and outflows. It is worth mentioning that arguably the main think tank behind the circular economy (CE), the Ellen MacArthur Foundation, decided to step away from its CE measurement tool (Circulytics) and encouraged organizations to disclose their CE performance as outlined in this ESRS (in good alignment with Circulytics).

In my option, one key metric that can be used to measure circular economy is waste (the less the better). Another metric that can be used is the % of recycled materials used in products. If you offer repair services this is also a metric that could be measured in terms of nb of items repaired or total monetary value of repair services.

Establish Data Collection Procedures: Identify Data Sources: Determine the sources of data relevant to circular economy reporting, such as internal operational data, external supplier data, and customer behavior data. 2. Organize and Store Data: Data Architecture: Design a data architecture that effectively stores and manages the collected data. This may involve using data warehouses, data lakes, or cloud-based data storage solutions. 3. Analyze and Interpret Data: Data Analysis Tools: Utilize data analysis tools and techniques to extract insights from the collected data. 3. Communicate Data-Driven Insights:Transform complex data into clear and understandable visualizations,

This phase is crucial for extracting meaningful insights and formulating actionable recommendations. We would like our data analysis to be robust, reliable, and valuable for decision-making. To tackle various questions and hypotheses, you may need to employ a range of analytical techniques: Descriptive Analytics: Use this to summarize and understand the current data. It helps in identifying trends and patterns that can shape initial insights. Inferential Analytics: This technique helps in making predictions or inferences about a larger population using a sample of data. It's vital for testing hypotheses about data relationships. Predictive Analytics: Employ this for forecasting future trends based on historical data.

After analysing your data and getting deeper insights, communication to your relevant internal and external stakeholders is crucial. Communicate results transparently using diverse channels like reports, websites, social media that are the right ones for your audience. It's not "just" about communication of data tables but more about sharing achievements, challenges, and also future plans using tailored data storytelling. Make sure you are reliable and credible for your stakeholders.