Reliability, Credibility & Manageability: How to Ensure your ESG Data is in Shape

Michael Sullivan, Senior Consultant

As the importance of ESG (Environmental, Social, and Governance) factors continues to grow due to regulation and client demand within the investment landscape, it is crucial for asset and wealth managers to ensure that their data is reliable, credible, and manageable. Reliability ensures informed decision-making and risk management can take place, credibility helps to bolster reputation with clients and regulatory bodies, and manageability enables an asset manager to scale and move agilely.

In this article we will explore how asset/wealth managers can best manage their ESG data prioritising the three pillars to ensure that investment decisions are made on a solid foundation, align with broader sustainability goals, and comply with regulatory standards.

Choosing the Right ESG Data Vendor

With a plethora of ESG data vendors, selecting the right vendor(s) is not a straightforward process. The choice of ESG vendor(s) is an important decision as it aligns you with their views, you will be adopting their approach and methodologies.

When selecting vendors, it’s crucial to consider these 7 key decision criteria:

1. Coverage – That the ESG data vendor has sufficient coverage from reported data, preferably not estimates of the investible universe. This would include coverage of asset classes, geographies, markets, size of companies (for instance by market capitalisation), theme or subtheme.
2. Data collection – This is usually completed via publicly available data sources. Methods such as qualitative, quantitative, and through using AI to scrape data from the web, informed by natural language processing. For private markets, the data collection approach continues to be more opaque, manual processes can be adopted such as questionnaires however scalability is critical to ensure value add.
3. Data quality – This is driven by the methodologies and approaches to data collection, deriving metrics and scores from the collected data. All recipients need reliable sources, any estimation /modelling methodologies used by the providers to fill data gaps would need to be consistent with agreed methods.
4. Alignment to reporting frameworks – The data ideally is aligned with the firm’s preferred or required reporting mechanisms. This would dictate how useful the data is, particularly when reporting to end clients or regulators in the future who require specific formats.
5. Data delivery mechanisms – Whether this be through SFTP, an API, via an aggregator, or cloud providers, such as Snowflake, each business will have its preferences.
6. Tooling – This includes mechanisms for provision of data, extraction of data and in some cases: data visualisation and subsequent regulatory and client reporting.
7. Client service –Stringent service level agreements and key performance indicators should ensure service remains aligned with expectations.

The above criteria may provide a helpful steer however every firm has bespoke requirements that typically are satisfied by multiple vendors. Survey indicates that on average, an asset/wealth manager subscribes to five data vendors.

The Importance of ESG Data Integration

ESG Data integration is a critical aspect for asset and wealth managers; this has been heightened by both the pressures from the regulators as well as an increase in demand from the market and investors. Asset/wealth managers must now demonstrate that their ESG methodology is governed within a controlled environment. Our recent article on the incoming Sustainability Disclosure Requirements covers this in more detail.

The setup for ESG data can be split into collection, processing, enrichment, and dissemination, Appropriate governance and controls need to be in place for each step. Each step is critical to support the end-to-end ESG activities for an asset or wealth manager.

Challenges in ESG Data Collection and Management

Collection of ESG data is a far more difficult task for an asset/wealth manager as this data is typically sourced from multiple external data providers.

This data will come into an asset/wealth management business from different ESG vendors in various file formats.  Typically, vendors continue to send this data through flat files using data grouping by sets of indicators as opposed to by issuer.

As asset/wealth managers increase the number of ESG vendors, this increases the volume and coverage of ESG Data; this in turn can cause issues downstream. Each ESG data vendor has their own mechanisms to make the data available, file formats, issuers tree structures, and issuer information sheets. The same issuer-level data can differ from one data vendor to another due to, for example, a delay in refreshing ESG data, different collection protocols and the data vendors use of proprietary estimation methodologies and/or models to extrapolate data when it is not public.

As a result, asset/wealth managers need to find solutions to properly assess the quality of the data they receive across vendors and to check for possible gaps in the vendor data before they can aggregate it and process it accordingly – exception management.

Oversight and Governance of Data Quality

The quality of ESG data has been steadily improving however it continues to remain a challenge. Asset/wealth managers need to have robust ESG data quality and governance control processes in place. This would involve a high level of operational data quality – this is exemplified by stringent oversight by:

• IT Team – Initially, oversight sits with those responsible for the data collection and storage, the IT team. You would expect the IT team to conduct consistency checks ensuring that data is received in the expected format, correct date, size of file, that it has been updated completely and accurately. Any anomalies should be flagged here.
• Data Management Teams – This is the next layer of oversight, this team are responsible for the data quality and availability, ensuring that the collected data is accurate at issuer level and is mapped accordingly following the correct issuer hierarchy. Any exceptions need to be flagged, investigated, and rectified
  Establishing oversight indicators enables asset/wealth managers to monitor the number of data quality issues, as well as measuring the capacity to fix these issues. High volumes of data, subject to extensive controls and potential overrides, coupled with queries from multiple stakeholders often marks the data value chain complex. Accordingly, asset/wealth managers must incorporate into their data management processes technology and tools that enable effective: (i) data model management, (ii) workflow management, (iii) quality controls and (iv) user interface for overrides
• ESG Analyst – This is the last level of oversight; they are responsible for interrogating the data, understanding the nuances, and improving the output through additional analysis as well as enriching overall insights using other selected data sources. They would focus on the relevance and suitability of the data set(s) in question and prioritise the usefulness/accuracy from an asset/wealth management perspective. 

Research teams, portfolio management teams, risk teams and marketing teams will use that ESG data very differently ranging between data consultation, modification and/or transformation.

The Need for One Golden Source

Meeting the above requirements amplifies the need for one ‘golden source of ESG data’. Multiple ESG data sets from various vendors should be stored within a single database to facilitate version control and centralized access, avoiding concerns over data consistency. Teams will then build pre-built or pre-formatted views using business intelligence tooling. This will enable users to query ESG data, allow them flexibility without compromising the golden source of ESG data.

How to Take Control of Your ESG Data

With the demand from both clients and regulators continuing to expand, the need to implement robust control environments is critical. Asset/wealth managers must be proactive and recognise these requirements and implement these processes and tools for data collection, integration, preparation, quality management and dissemination of ESG data within their ESG data integration programmes.

This means defining clear guiding principles, appropriately scope data requirements, and implement a value adding data functionality that offers traceability of analysis and aligns to ever increasing levels of transparency requirements.

This is where we can help.

At Liqueo, we believe in working collaboratively and iteratively with you to design and implement ESG solutions that work for your organisation. Solutions designed to be scalable and add value, therefore laying the foundations to continue to enrich and build out functionality, incrementally as the scope develops over-time. Please get in touch to discuss.