Forest-IQ

The ForestIQ beta brings together policy, disclosure and commitment data for over 500 organisations with links to the commodity supply chains linked to tropical deforestation including, soy, beef, leather, palm oil, timber and pulp/paper.

James Roche · 12 Apr 2023 · 7 min read

Client Introduction

Global Canopy is a data-driven not-for-profit organization that aims to address the market forces contributing to the destruction of nature. Their mission is to improve transparency and accountability by providing innovative open-access data, clear metrics, and actionable insights to leading companies, financial institutions, governments, and campaigning organizations worldwide.

The Challenge

The financial industry faced challenges in effectively screening portfolios for exposure to deforestation-related issues due to fragmented policy and commitment data. Multiple, overlapping sources of data made it time-consuming and difficult for financial institutions (FIs) to match and integrate them with their financial datasets and internal systems. Minor methodological differences and data gathering practices further complicated the understanding of similar data from different sources. There was a pressing need to consolidate these diverse data sources into a single, accessible resource to simplify integration and accelerate decision-making processes.

Our Solution

Global Canopy recognized the significance of partnering with a trusted entity familiar with deforestation data and possessing expertise in software and data engineering. They collaborated with Neural Alpha for an ambitious data integration project. The two organizations worked closely together, understanding complex datasets and potential problem areas. Applying best practices in data engineering, they profiled, modeled, prototyped, tested, integrated, and deployed the ForestIQ beta dataset.

Neural Alpha and Global Canopy jointly developed unifying indicators and metrics that covered deforestation commitments, policy and exposure, production volumes, financial materiality, and human rights. The collaborative approach ensured comprehensive data coverage and an accurate representation of forest risk companies.

Outcomes

The outcome of this collaboration was the ForestIQ beta dataset, which addressed the challenges faced by FIs effectively. The key achievements were as follows:

  1. Consolidation of Data: Over 500 forest risk companies were covered by unifying data from five different and overlapping datasets, eliminating duplication and simplifying integration.
  2. Unified Scoring and Indicators: A cohesive scoring and indicator mechanism was built, enabling FIs to comprehensively assess corporate performance on deforestation, ecosystem conversion, and human rights abuses.
  3. Entity De-duplication: Legal entities were de-duplicated and matched, ensuring accurate data representation and reducing confusion arising from multiple references to the same entity.
  4. Enhanced Financial Industry Integration: The dataset included improved coverage of financial industry identifiers, facilitating seamless integration into financial institutions' tools and workflows.

Successful Project Delivery

The ForestIQ beta dataset was delivered on time and within budget, receiving positive feedback from financial institutions participating in the ForestIQ working group. This project demonstrated the technical capabilities of Neural Alpha, combined with a customer-centric approach to collaborating with Global Canopy to meet complex requirements efficiently.

Conclusion

The ForestIQ beta dataset stands as a testament to the power of collaboration between Global Canopy and Neural Alpha. By combining data expertise and software engineering, the two organizations successfully addressed the challenge of fragmented data and empowered financial institutions with valuable insights for making informed decisions concerning nature, forests, and people. This case study highlights the importance of partnerships in driving positive change for the environment and sustainable financial practices.

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