Best Available Sustainability Data Into Information & Action
Challenges with Sustainability data
In today's data-driven world, raw data alone often falls short in providing actionable insights. Transforming this data into meaningful information and decision support is invaluable, particularly when navigating the sophisticated connections between economic activities and nature.
Until recently, data was commonly perceived as being numerical and structured. However, in the realm of sustainability, data frequently appears unstructured and not necessarily numerical. Thus, we focus on capturing, aggregating, and analyzing unstructured, qualitative data. This shift demands innovative approaches and tools, such as advanced computing and machine learning.
The relationships between economic activities, involved stakeholders, value value chain partners, nature & climate are multifaceted and complex. This makes it essential to distill vast amounts of various kinds of data into clear, actionable information. This not only aids in informed decision-making but also empowers businesses to understand and address their environmental impact effectively.
From business activity code to Double Materiality Assessment
process overview
1.
The minimum amount of input is your business activity codes based on which a shortlist of impacts and dependencies is provided based on the trusted data
2.
The impacts and dependencies are then translated in to business risks that may negatively affect the financial performance of your company.
3.
Based on the IROs detected in previous steps a machine learning model is used to map these with ESRS topics to shortlist the most relevant ones
4.
Each ESRS topic is then analyzed from impact materiality perspective and financial materiality perspective in order to plot the Double Materiality graph
5.
You will then engage your stakeholders and into the process in order to make the result as company specific as it can possibly get.
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