Making sense of ESG data Martijn Groot is VP Marketing and Strategy at Alveo Until recently, environmental, social and governance (ESG) data management was at a low level of maturity across both the buy side and sell side. Although there have been reporting frameworks in place for decades including the principles for responsible investment (PRI) and global reporting initiative (GRI) standards, the absence of standard data collection, integration, and reporting solutions often required firms to create their own ‘ESG data hub’ to provision their own analysts, front office, and client reporting teams. This situation is rapidly changing. Financial services firms are recognising the key role ESG metrics play in decision-making across the investment management process. Not only does ESG data inform new product development, asset allocation and client reporting in an increasingly competitive market, but the regulatory push towards the disclosure of ESG information under the Sustainable Finance Disclosure Regulation (SFDR) means that asset managers are required to report on the ESG metrics of their portfolios. SFDR also requires proper documentation as to the sources or models behind the reported information. Data preparation processes need to withstand rigorous scrutiny, as regulators demand the ability to explain figures and are increasingly conscious of the issue of greenwashing This has far-reaching ramifications for financial services firms globally. Any firm that sells or distributes investment products into the European Union will have to follow the SFDR regulation. SFDR requires firms to report on mandatory Principal Adverse Impact (PAI) Indicators as well as some optional ones. Paradoxically, the reporting requirements for the publicly listed companies that asset managers invest in lag behind the SFDR timetable. This causes an information gap and the need to supplement corporate disclosures with third party ESG scores, expert opinion, as well as internal models to come to an overall assessment of ESG criteria. However, the regulatory environment for ESG data is far from the only factor driving growth in ESG data management. In recent Alveo research that polled the views of 300 asset owners and asset managers in the UK, US and Asia-Pacific, just 21% of the survey sample cited ‘regulatory reporting’ as a key driver of their use of ESG data. This indicates that beyond regulatory compliance enhancing their ESG data management is something firms see as a must do to boost the overall value of their business. Regulation has an important role, of course, but firms are increasingly investing in an ESG data management capability today because they understand the broad benefits that it will bring to their business rather than being forced to do so by the need to comply with the latest rules and stipulations. Need for enhanced ESG data management This growing need for ESG data will impact a vast array of financial services businesses worldwide. Asset management firms are increasingly concentrated on optimising their ESG data management and doing so quickly. The Alveo research found that well over nine out of ten (95%) of the sample are looking to improve their ESG data management. 32% are looking to do so in the next six months, with 80% in total looking to do so within a year. There is also a need for ESG-data for the banking industry. For instance, in corporate banking, ESG data is increasingly crucial to support customer onboarding and, in particular, Know Your Client (KYC) processes. On top of that, banks will have to report their ‘green asset ratio’ – in essence, the make-up of their loan book - in terms of the mix of business activities of the companies they lend to, categorised according to the EU Taxonomy. In the future, if a company signs up in order to obtain a loan from a bank as part of the screening criteria, it will be asked to disclose what kinds of business activities it is involved in and what kinds of sustainability benchmarks it has in place. Banks and other sell-side financial services firms will also frequently screen their suppliers, as part of a process called Know Your Third Party (KY3P). They will want to know who they are doing business with, so they can then report this in their own Annual Report. Banks will also want to climate stress test the products they hold in their trading book for their own investment against certain climate scenarios. The European Central Bank (ECB), the Monetary Authority of Singapore (MAS), as well as the Bank of England have all incorporated climate stress test scenarios in their overall stress testing programmes to gauge the solvency and resilience of banks. ESG data also has a role to play in the way banks manage their mortgage book as they are increasingly looking for geospatial and climate data, for example, to work out the flood risk of the properties they finance. This is information that was previously typically used by (re)insurance firms but that will now be used more broadly in the financial services industry. Both sell-side and buy-side financial services companies will also need to integrate ESG data with data from the more traditional pricing and reference providers to give a composite view, incorporating not just the prices of instruments and the terms and conditions but also the ESG characteristics. Scoping the challenge ESG data needs to be anchored across the organisation, integrating with all the different data sets to provide a composite picture, becoming a key source of intelligence, not just for the front office but also for workflows in risk, finance and operations. Given that need, it is perhaps unsurprising that the Alveo research finds that 80% of businesses are aiming to improve their ESG data management within the next year. However, for many firms, this may be easier said than done. Sourcing accurate ESG data and properly interpreting it is a particular challenge, as information needs to be gathered from a wide array of data sets including third party estimates, ratings, news and corporate disclosures. Corporate disclosures especially are still patchy and sometimes difficult to come by, while the withholding of relevant data means that records are frequently incomplete or held in silos. This inevitably impacts the effectiveness with which key data is distributed and disseminated to senior leaders and decision-makers. In some cases it is simply missing. Usability issues include the disparity in methodologies third-party firms use to estimate or score firms on ESG criteria. Rating firms have their own input sets, models and weights and often come to different conclusions. Compared to credit ratings, the correlation between the scores given to a firm by different rating agencies is lower. However, credit analysis is as old as the banking industry and the metric gauged (probability of default) is clear. It could be that, with increased global disclosure standards under IFRS, ESG scores will converge. Comparability issues in ESG are exacerbated by different standards, different reporting frequencies or calendars and also the lack of historical data to track progress and benchmark performance over a longer time period. The biggest challenge in many firms, however, is how to embed the ESG data in a range of different business processes to put users on a common footing. This requires the capability to quickly onboard new data sources, integrate, harmonise and vet that data, fill in the gaps where needed and provide it to users and business applications. Achieving all this is far from easy. The data management structure and model is not always clear and invariably siloed. It often still needs to be integrated into wider reporting, especially in finance and risk, which are typically the functions where all information flows necessarily come together. These firms are therefore focused on improving their ESG data management and are also prepared to invest to make that happen. Beyond pure data management, putting in place robust high-quality data governance processes and practices will also have an important role to play here in controlling access and ownership and ensuring that data usage is monitored efficiently. Finding a solution Accessing ESG data and ensuring it is of good quality, comparable with other ESG data sets and well-integrated within existing workflows can often be complicated. Whenever new data categories or risk metrics are introduced, data management practices typically start with improvisation through desk-level tools including spreadsheets, local databases and other workarounds. This is gradually streamlined, centralised, operationalised and ultimately embedded into core processes to become business-as-usual (BAU). Generally speaking, firms need to cross-reference to a comprehensive data model that covers regulatory ESG information and underlying data sets. In addition, they must achieve transparency as to which sources and what types of data are leveraged, the business rules used and any manual remediation. Comprehensive ESG data management A comprehensive approach to ESG data management is needed to provide consistent data to service multiple use cases. Yet, accessing ESG data and ensuring it is of good quality and well-integrated within existing workflows can be difficult. However, data management solutions and Data-as-a-Service offerings are now available to help firms acquire the ESG information they need, the capabilities to quality-check, supplement and enrich it with their own proprietary data or methods, and the integration functionality to place users and applications on a common footing. Achieving this demands that any challenges presented by the quality of data are dealt with from the outset. What organisations need is a process that seamlessly acquires, integrates and verifies ESG information. Additionally, historical data to run scenarios can help with adequate risk and performance assessment of ESG factors. A data management function should also facilitate the easy discoverability and explainability of information and effective integration into business user workflows. In short, data management should service users from the use case down, rather than from the technology and data sets up. Specific capabilities should include cross-referencing taxonomies and condensing information, for example to report on indicators that serve as performance KPIs, or that meet reporting mandates in the financial sector. Data derivation capabilities and business rules can spot gaps, highlight outliers, whether they are related to historical patterns, or outliers within a peer group, industry or portfolio; and provide estimates where needed. Additionally, historical data to run scenarios can help with adequate risk and performance assessment of ESG factors. The speed that the regulator has picked up with regard to enabling a sustainable economy not only confronts companies with a very tight implementation schedule, but also with major challenges regarding the sourcing, processing and quality assurance of large sets of frequently unstructured data. Mastering this data challenge is a prerequisite for successfully competing for new market offerings and sustainable products. Early operational readiness is key to staying ahead of the curve in adapting to the new ESG regime. The major decision points that need to be addressed right now are first, determining the target operating model and governance, second, designing the target data and system architecture and third, moving forward with a well-proven approach for a customised implementation. Once a data management system has been put in place within an effective operating model, there are many benefits: from efficient data onboarding and provisioning business users to securing data lineage and data cost and usage management. This significantly increases the return on any existing and future ESG data investments. Firm-wide availability will increase usage and, in turn, will benefit the whole organisation and ensure firms are optimising their data. Towards ESG Data-as-a-Service Because ESG data management capabilities should support a company’s compliance processes end-to-end, the Data-as-a-Service model where a supplier manages the sourcing and integration but also quality management of required ESG data emerges as the preferred service model. Research conducted among hedge funds, pension funds, insurance companies and other investment firms in the UK, US and Asia-Pacific found more than three-in-ten opted for this approach. Having capabilities in-house is good news for all stakeholders, but beyond this, drawing on the services of an expert solutions provider and adopting Data-as-a-Service models may prove to be the best route to address these challenges. ESG data management and quality challenges are very real and the inability to surmount them significantly impairs the ability to meet new and evolving standards, regulations and industry best practice protocols. Given the complexity and range of the challenges, there is a clear need for firms to draw on in-depth third-party expertise and use solutions that help collect, collate and validate data and offer a one-stop shop of ESG content as well as the integration of it into business workflows to put it to use.
ESG data management and quality challenges are very real and the inability to surmount them significantly impairs the ability to meet new and evolving standards, regulations and industry best practice protocols