ESG

The ESG missions of companies can only be carried out with the collaboration of Data and AI teams, simply because it is necessary to measure phenomena (and therefore collect precise and high-quality data, expose it to business units) and understand these phenomena (explain, predict, prescribe improvement solutions) which are very complex, requiring the use of data science algorithms, machine learning, AI, and even generative AI.

Main Missions of a Data/AI Manager or Head for ESG

a. Governance and Collection of ESG Data

• Data Centralization
  • Collect, clean, structure, and govern ESG data from various sources (CSR reports, internal databases, external suppliers, regulations, etc.).
  • Implement governance processes to ensure data quality, consistency, and traceability.
• Key Indicators
  • Define and monitor ESG KPIs (e.g., carbon footprint, team diversity, regulatory compliance).
  • Collaborate with business teams to validate and prioritize indicators.

b. Reporting and Visualization

• Dashboards
  • Design visualization tools (Power BI, Tableau, Python) to make ESG data accessible to stakeholders.
  • Adapt dashboards to the specific needs of different business units (HR, procurement, management).
• Report Automation
  • Use AI to generate ESG reports compliant with standards (GRI, SASB, TCFD, etc.).
  • Automate data collection and aggregation to reduce errors and save time.

c. Modeling

• Development of Models
  • Create AI or machine learning algorithms to analyze ESG trends, predict risks (e.g., climate risks, non-compliance, fraud).
  • Develop explanatory, predictive, and prescriptive models to propose improvement paths.
• Generative AI and NLP
  • Use generative AI or NLP algorithms (Natural Language Processing) to automate report generation.
  • Analyze texts (reports, articles, regulations) to extract relevant ESG information.

d. Cross-functional Collaboration

• Interface with Business Units
  • Work with operational teams (HR, procurement, legal) to integrate ESG data into decision-making processes.
  • Raise awareness and train business teams on the importance of ESG data.
• Regulatory Monitoring
  • Monitor the evolution of ESG regulations (e.g., CSRD in Europe, green taxonomy) and adapt data tools accordingly.
  • Collaborate with legal teams to ensure data and report compliance.

Concrete Project Examples

• Carbon Footprint Analysis
  • Model scope 1, 2, and 3 emissions of a company using internal and external data.
  • Use algorithms to identify levers for reducing emissions.
• Greenwashing Detection
  • Use NLP to analyze a company’s communications and identify inconsistencies with its actual actions.
• Supply Chain Optimization
  • Cross-reference social data (child labor, working conditions) with logistics data to identify risks.
• Automated ESG Scoring
  • Develop a tool to score suppliers or investments based on ESG criteria.