Quality of Work Life: an evolving definition
ESG Data is inevitable in the financial sector, it has become more and more important over the years and became the reference for sustainable investment decisions.
Coming from the demand for ESG-related financial products, and the need for extra-financial tranparency required by customers and regulators, ESG Data are essential and key. However, the access to such data is still a challenge, even a strategic element, for all financial institutions as the market is arising and the regulatory requirements are not yet fixed. Challenges are multiple to make its way around : identify data needs, define a data acquisition method, select the right provider or develop internal methodologies, and even leverage on new technologies.
Based on identified and emerging needs, and taking strongly into account regulatory requirements, we developed a strong methodology to help our customers define their ESG data strategy. Our framework allows our clients to mix different ESG data acquisition solutions in order to match their particular cases but also market guidelines.
Sia Partners also offers to support its clients in implementing internal solutions to structure or scrap data into available counterparties' sources leveraging on AI solutions or in the definition of sector-specific questionaries integrated to counterparties onboarding processes. It may include potential needs for IT systems adaptation such as opening new data streams, as many ESG disclosures also require financial data.
Sia Partners proposes its expertise concerning ESG data suppliers’ selection processes. In order to offer our customers a holistic view, we produced a benchmark of ESG data providers and aggregators. We evaluated their offers, scope covering and information made available to their clients regarding several ESG use cases.
Finally, concerning specific data for which no reliable sources yet exist, Sia Partners helps its clients build and set up appropriate proxies, as well as defining next steps to move toward more sustainable solutions whilst new opportunities arise.
-Build an ESG data dictionary including external data (ESG data providers - Sustainalytics, Vigeo...) and internal data (internal assessment framework, data matching)
-Identify and qualify critical ESG data, according to use cases (ESG dashboard, ESG assessment, portfolio management with regard to 2°C objectives and Group commitments)
-Build an ESG nomenclature and data classification
-Define and implement ESG data quality controls (completeness, validity, uniqueness, consistency, freshness, etc
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