{"id":22121,"date":"2022-03-28T06:54:29","date_gmt":"2022-03-28T05:54:29","guid":{"rendered":"https:\/\/davies-group.com\/consulting\/blog\/esg-data-confusion-2\/"},"modified":"2026-01-23T13:06:02","modified_gmt":"2026-01-23T13:06:02","slug":"esg-data-confusion-2","status":"publish","type":"blog","link":"https:\/\/davies-group.com\/consulting\/blog\/esg-data-confusion-2\/","title":{"rendered":"ESG data confusion?"},"content":{"rendered":"<p>With well over 100 to choose from, selecting the right ESG data vendor, or vendors is not straightforward. It reflects your investment strategies and your internal ESG beliefs, as these govern which data is material in making investment decisions and reporting to your clients.<\/p>\n<p>Any choice you make effectively aligns you with that particular vendor\u2019s point of view on ESG, as you will be adopting their approaches and methodologies. This can be a minefield, particularly as this data is presently unaudited and falls outside regulatory scope \u2013 although this is being considered by the FCA.<\/p>\n<p>Meanwhile, and in any case, when selecting and reviewing vendors it&#8217;s important to consider these key decision criteria:<\/p>\n<ul>\n<li><strong>Coverage<\/strong> \u2013 if a data vendor does not have adequate coverage (preferably from reported data, although estimated may also be sufficient) of the investible universe, then irrespective of other qualities, they will not be appropriate. This includes coverage of asset classes, geographies, markets, size of companies (for instance by market capitalisation), theme or subtheme.<\/li>\n<li><strong>Data collection <\/strong>\u2013 this is usually done via publicly available data sources, although some vendors engage the target companies to extract unreported data or leverage news sources. A number of methods are used including qualitative, quantitative, and through using AI to scrape data from the web, informed by natural language processing. The more technologically advanced methods are more scalable, although some prefer human intervention, given that AI\/quantitative models are limited by the build quality of the technologies that support them. For private markets, the data collection approach relies on more manual processes, such as questionnaires and direct outreach, meaning these must be as scalable as possible and ensure a degree of value add to justify not bringing them in-house.<\/li>\n<li><strong>Data quality <\/strong>\u2013 driven by the methodologies and approaches to data collection, deriving metrics and scores from the collected data, and in some instances estimating data. Some may lean towards objective data as they do not want to be influenced by an ESG data provider\u2019s view on the subject, whereas others will want more supporting analysis. All users will want reliable sources and will have their own views of the appropriateness of any estimation\/modelling methodologies used by the providers to fill data gaps.<\/li>\n<li><strong>Alignment to reporting frameworks <\/strong>\u2013 whether the data is\/can be aligned with the firm\u2019s preferred or required reporting mechanisms will dictate how useful the data is, particularly when reporting to end clients or regulators in the future who require specific formats, such as the prescriptive nature of the upcoming SFDR Level 2 disclosure requirements.<\/li>\n<li><strong>Data delivery mechanisms <\/strong>\u2013 whether this be through SFTP, an API, via an aggregator, such as RIMES, or cloud providers, such as Snowflake, each business will have its preferences.<\/li>\n<li><strong>Tooling<\/strong> \u2013 includes mechanisms for provision of data, extraction of data and in some cases: data visualisation and subsequent regulatory and client reporting. Were these tools to be clunky or inflexible firms are likely to consider alternative vendors.<\/li>\n<li><strong>Client service <\/strong>\u2013 a number of the vendors have scaled at pace, whether that be through organic growth or acquisition, this may impact their abilities to service their clients. Stringent service level agreements and key performance indicators should ensure service remains aligned with expectations.<\/li>\n<\/ul>\n<p>While these criteria provide a helpful steer, every firm has bespoke requirements that typically cannot be satisfied by a single vendor, with the average asset manager subscribing to five data vendors.<\/p>\n<p>Davies maintains a database of over 60 data providers in the ESG space, ranging from niche to generalist. We have extensive and detailed experience in the selection, implementation, and ongoing review of providers.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>We explore how to navigate the sea of data vendors<\/p>\n","protected":false},"featured_media":21385,"parent":0,"menu_order":0,"template":"","format":"standard","meta":{"_acf_changed":false},"practices":[70],"industries":[103],"capabilities":[112],"class_list":["post-22121","blog","type-blog","status-publish","format-standard","has-post-thumbnail","hentry"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v25.8 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>ESG data confusion? - Davies<\/title>\n<meta name=\"description\" content=\"Davies asset management experts discuss the key elements of how to select the right ESG data vendor to reflect your investment strategies.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/davies-group.com\/consulting\/blog\/esg-data-confusion-2\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"ESG data confusion? 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