Aligning ESG reporting frameworks
One of the biggest contributors to ESG data inconsistency is the lack of uniform reporting frameworks across regions and regulatory bodies. Without alignment, data coverage becomes fragmented and incomplete.
We work closely with clients, standard setters, and regulatory bodies to understand their reporting obligations and craft datasets that satisfy all their requirements. In addition, we automate the matching between frameworks to ensure no single data point needs to be reported twice. Even if a client wants to ask a question in their own language, we can maintain the automated link with relevant reporting frameworks.
We then simplify the data collection process by reaching out to relevant parties, supporting them through each step, and offering flexible solutions like our bureau service. This allows clients to share pre-existing documentation directly with us, while we complete the dataset on their behalf.
To ensure long-term compliance, the questionnaires are regularly updated to meet evolving requirements. These updates allow clients to complete consistent year-on-year assessments, further enhancing data coverage.
Engaging stakeholders effectively
Stakeholder engagement is a fundamental pillar in tackling the data coverage challenge. We partner with clients to identify material issues and gaps in their ESG data and work collaboratively to solve them. Providing training to those directly involved in the data collection process is a key approach we take to improve data coverage.
But engagement doesn’t stop there. Engaging with those in charge of setting reporting requirements helps to highlight our clients’ concerns. By working collaboratively with these stakeholders, we help to increase data coverage in the long-term by reducing points of friction. Increasingly we also support clients to take an incentive-based approach, such as ESG margin ratchets, to drive reporting by rewarding transparency and engagement.
Leveraging AI for smarter ESG solutions
Our use of artificial intelligence has emerged as a potential game-changer in ESG data processing. Large Language Models can be used to help in the following ways:
- Extracting data from complex sources: ESG insights are often hidden in sprawling, unstructured datasets. Through natural language processing, AI can pull out relevant ESG indicators from websites, bills, emails, and more.
- Scaling validation and analysis: AI dramatically speeds up data review, allowing analysts to validate findings rather than spend hours searching for them.
- Streamlining stakeholder collaboration: Gathering ESG data can require suppliers or portfolio companies to complete lengthy questionnaires. This burden can be eased as AI can extract key information from pre-existing sources, supporting the company to gather and report data much more efficiently.
By focusing on consistent frameworks, stakeholder engagement, and AI-powered data processing, we help enhance ESG data coverage for all our clients.
