Simplifying ESG
Connecting Industry ESG via Generative AI
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ESG Challenges
ESG Scope - Accessing data against the vast scope of ESG for most organisations is representing a somewhat insurmountable challenge, due to the requirement to access and track information changes in real-time within their industry value chain. Scope may expand from Tier 1 all the way to Tier 9 and beyond in a supply chain context.
Cost of ESG Compliance- If and when the data is accessible across the industry value chain, expensive and elongated Data Science Projects are established to map the relationships between raw data sets and ESG Standards. Maintaining the currency of data sets is equally as challenging on an ongoing basis.
Data Identification - Simply identifying the right data sources for ESG is a significant challenge due to the large amount of data sets required to report against the ESG Standards.
Compliance Measurement - Data Measurement requires the access and analysis of multiple data pipelines from a multitude of data sources, complicating the ability to even comply with ESG Standards, let alone derive insight value from the data.
Industry Relevance - Whilst ESG Materiality Assessments are applied at an Industry level, the Data Science Project is designed to meet the ESG Compliance requirements, but does not provide insight across the Industry Value Chain on where ESG & Non-ESG improvements maybe made, which can be a valuable bi-product of such a large data ecosystem.
Introducing...ESGCONNECT.AI
Data Identification: The ESGConnect.ai AI Hub's data discovery capabilities, including AI-driven classification, identify relevant ESG data sources by linking all relevant public and private data sources, and facilitate the management of features and metrics to effectively analyze target ESG measures.
Data Capture: The AI Hub's data integration capabilities form the backbone of any framework for effective management of time-series data. The platform provides a unified and holistic view of connected data sources, including internal systems, third-party databases, IoT devices, and public data repositories, ensuring seamless access to necessary ESG data.
Data Translation: The platform's ESG Industry Unified Semantic Layer standardizes and maps relationships between disparate data sources and ESG metrics, ensuring accurate alignment with reporting requirements. Robust pipelines, active metadata management, and built-in business rules further ensure that data quality is consistently maintained.
Time to Value
Data Measurement: The platform facilitates advanced analytics and machine learning models to measure and generate insights from ESG data, such as calculating GHG emissions, water usage, or energy efficiency, with real-time monitoring and reporting capabilities. Analytical pipelines are sharable and extendable, ensuring transparency and reusability for accurate and efficient analyses.
Industry Data Model: The platform embeds industry data models that are enhanced by its interoperability with established ESG and risk frameworks such as GRI, SASB, CDSB, and the EU taxonomy for sustainable activities. Through flexible pipelines and extensibility of the Unified Semantic Layer, the platform enables the creation of a comprehensive, standardised, and industry-specific value chain data model that supports robust ESG reporting and risk management.
How it Works
Prioritise ESG Improvements via AI Predictive Modelling
Aggregate Any Data from Internal & External Sources onto a single table
ESG Reporting Summarised via AI
Build & Maintain your Large Language Model with ESG Standards & Your Data Sources
Maintain Data Relationships against ESG Standards via AI
ESGCONNECT.AI enables you to ingest internal and external data sources in ANY format and brings them together into a SINGLE table to enable data manipulation
Your ESG Large Language Model includes data from defined external sources (ESG Standards) as well as your internal and external data sources to enable data querying
Due to the simplicity of having all of your data in a SINGLE table, you can quickly map your data relationships with the assistance of Artificial Intelligence
ESG Reporting is automated against the ESG Taxonomy relevant to your region and Generative AI summarises reporting insights to speed up the reporting process
Generative AI analyses the areas that will make the most impact on your ESG Metrics by providing Predictive Modelling to inform your ESG initiative roadmap
Return on Investment
Reduce Set-up time by 70%
Reduce ESG reporting establishment by 70% by avoiding large costs associated with ingesting disparate data sources, integrating the data sets and mapping the data relationships
Reduce Reporting Time by 100x
Generative AI is leveraged to reduce the time it takes to prepare reports and is able to provide your business and your supply ecosystem with valuable insights from the data collected to improve ESG Performance
Reduce ESG Upkeep by 30x
Maintaining such a complex ecosystem is costing businesses exponentially more than is has to. Reduce your ability to maintain ESG data through connectivity across all of your data-sets in real time
Contact Us
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