ESG data provider comparison
Develop an informed approach to using external vendors to supplement in-house data and expertise.
Our analysis of more than 60 of the largest financial services firms across Europe and the US shows that most companies use between 2 and 5 different providers, while some even use up to 10 different third-party vendors to cover their ESG data needs. Clearly, there’s no single all-singing, all-dancing ESG data solution that fits all needs.
State of the ESG data provider market
To help clients integrate ESG into their processes and systems, EY teams undertake a regular analysis of the ESG data provider landscape, looking at over 100 data providers and their data service offerings. Here are the key takeaways from that analysis:
1. Support for key regulations varies
Some firms offer a suite of tools that cover multiple use cases, but many are focused on specific aspects of ESG. The table below outlines the number of providers and their support for key regulations such as the Sustainable Finance Disclosure Regulation (SFDR), the EU Taxonomy, and the Task Force on Climate-Related Financial Disclosures (TCFD). Also shown is data coverage across offerings like ESG scorings and indices, ESG raw data and sentiment analysis as well different climate relevant modelling capabilities:
2. All models apply simplified assumptions and therefore reduce the relevance of results
Off-the-shelf models do not accurately capture tail risk events and uncoordinated or delayed policy action. The key is to find a model which best represents the investment asset’s business, industry or market and enhance the results with adjustments to tailor it to each firm’s individual situation.
3. The aggregation of data is not always transparent
Understanding data inputs, assumptions and limitations is essential to understanding results. For example, some rating firms “overweight” particular ESG themes, and firms will need to determine whether these are material for them. Some rating firms also calibrate their score with sentiment analysis, while others do not.
4. There is a lack of correlation between ESG scores
Consistency of ESG data is a challenge where investors are trying to compare like-for-like. There is an argument that standardization of scoring methodologies is not always appropriate since different firms will face different materiality of risk. The only area where consistency is assured is regulatory reporting.
5. No solution can model all asset classes
Most data solutions cover equities and corporate debt, with property and infrastructure captured, in part, through physical risk modeling. This still leaves a large proportion of assets unaccounted for, particularly for private equity firms and banks who will have large portfolios of unlisted assets.
6. The lack of available ESG data is a major challenge for data providers and financial services firms alike
There is a disparity across industries, with better quality data available for higher carbon sectors, such as oil and gas, and a lack of data for other sectors, such as agriculture and forestry. The latter have not traditionally been heavily focused on CO2 output, but they must work to catch up in this new landscape. Data sets for less material sectors are being developed but are still immature.
Steps to success for using external ESG data providers
Four key steps will help financial services firms get the greatest value from external ESG data.
Using EY analysis and experience, we have developed four key steps that will help financial services firms get the greatest value from external ESG data:
Step 1: Internal and external ESG data assessment
- Pull together use cases from different workstreams to understand and centralize requirements
- Implement use case prioritization based on collected requirements
- Look at internal and external data sources to see what best fulfils data needs for prioritized use cases
- Select ESG data vendor assessment for compliance with data security and privacy requirements
Step 2: Data sourcing from select vendors/ internal sources and gap assessment
- Identify any gaps and supplemental data needs
- Identify additional internal/external data sources to fulfil supplemental data needs
- Identify potential data quality challenges
- Identify data transformation needs
Step 3: ESG data integration strategy and roadmap development
- Build an ESG data strategy in alignment with enterprise data management policy
- Develop a roadmap to integrate ESG data sources into existing data ecosystems for prioritized use cases
- ESG data integration strategy should align with Enterprise Data Management policy with a focus on data quality and transparency into the data supply chain
Step 4: ESG data integration execution
- Source and ingest ESG datasets from selected vendors for prioritized ESG use cases into the enterprise data ecosystem
- Use existing data ingestion patterns and tools as necessary for ESG data sourcing based on vendor infrastructure
- Define and apply preventative and detective controls to maintain enterprise data quality standards on data sourced from ESG vendors
- Utilize existing regulatory standards as guiding principles to meet evolving regulatory needs
- Transform data based on consumption use cases and to ensure compliance with data security and privacy requirements
- Consume ESG data from authorized provisioning points in a unified data layer across the enterprise to ensure consistency
ESG data will underpin success
The importance of ESG data will increase in importance as regulatory requirements and investor demands evolve. The challenges around utilizing external ESG data are clear – selecting the right vendors, ensuring consistent use across the business and developing the right data integration strategy and execution.
Our comparative ESG data provider analysis shows the fragmentation of the market in ESG data vendors demands a concerted and careful approach. Looking ahead, it’s possible that ESG data providers could fall within the regulatory perimeter set by the EU. That would fundamentally change how nonfinancial data is accessed and how investors’ growing appetite for better ESG information and risk transparency is met. In the meantime, the caveat emptor rule applies to the assessment of external ESG data: the burden is on financial services firms to examine the available ESG data options and make the right choice for the various use cases. EY clients can draw on the ESG data vendor study to ensure their significant investments in systems and specialists are supported by the right ESG data.
Having the right ESG data is key to transparent and meaningful sustainable finance. Faced with fragmented data from multiple sources, financial services firms have responded by purchasing multiple data sets, overlaying these with their own analysis and developing their in-house ESG data capabilities. Still, as the demands for ESG data integration increase, knowing how a firm’s unique data requirements align with the various ESG data solutions in the market is crucial. The EY ESG data provider analysis provides detailed insights that support strategic, value adding ESG integration.