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Seamlessly navigate ESG reporting to untangle data complexities, and foster a broader pursuit of sustainability.
Investors now weigh ESG performance alongside financial factors, driven not only by ethics but also by economics. Companies with higher ESG ratings often demonstrate superior resilience, growth, and long-term profitability.
Carbon emissions
Energy consumption
Waste management
Water usage
Pollution prevention
...
Labor practices
Diversity and inclusion
Employee well-being
Community engagement
Customer satisfaction
...
Board composition
Executive compensation
Shareholder rights
Risk management
Regulation Compliance
...
INERCONNECTED FRAMEWORKS, KPIS AND SUBJECTS
Modeling reporting issues and framework structures in a graph database enables companies to effectively manage and navigate their ESG landscape.
Without a graph, comprehending the potential impact of changes in one metric on the others can be challenging, if not impossible.
FRAGMENTED DATA AND DATA SOURCES
Data underlies a trustworthy ESG reporting originates from diverse sources, spans internal and external operations. Recognizing the interdependencies between data is crucial.
A graph-based approach provides a powerful, intuitive, and holistic method for aggregating and analyzing contribution data for ESG reporting.
The linking of Artificial Intelligence (AI) techniques and ESG applications opens up a wealth of opportunities for companies to dig into the gold mine of ESG data for strategic planning and reporting.
Transform unstructured ESG data into clear and accessible format; enable users to easily engage with the information through natual language interface.
Go beyond assessing current ESG performance; simulate diverse scenarios to forecast potential outcomes of various sustainability initiatives.
Encode graph metadata and topologies into low-dimensional vectors; provide optimal inputs for downstream machine learning models to perform different tasks.
