{"id":1786,"date":"2021-04-20T14:17:00","date_gmt":"2021-04-20T14:17:00","guid":{"rendered":"https:\/\/digiterrebestg.wpengine.com\/?p=1786"},"modified":"2024-05-30T14:07:42","modified_gmt":"2024-05-30T14:07:42","slug":"esg-moves-from-narrative-to-action-with-advanced-technology","status":"publish","type":"post","link":"https:\/\/www.digiterre.com\/us\/2021\/04\/20\/esg-moves-from-narrative-to-action-with-advanced-technology\/","title":{"rendered":"ESG moves from narrative to action with advanced technology"},"content":{"rendered":"
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ESG moves from narrative to action with advanced technology<\/h1>\n\n

ESG is under the spotlight in financial services. Asset managers and owners increasingly demand the integration of ESG factors into the investment process and governments and companies are benefiting from lower borrowing costs associated with sustainable businesses and assets.<\/p>\n \n <\/div>\n<\/section>\n\n\n

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Data management for ESG is a newly emerging and dynamic area. We have acquired strong expertise in this field that builds on our two decades\u2019 of experience solving complex data and software challenges in the financial services industry and the public sector.<\/p>\n\n\n\n

We have learned that moving from intent to action in ESG is challenging. Data management is one of the most intractable areas to get right and one of the toughest obstacles to the implementation and widespread adoption of ESG.  Without advanced data transformation solutions \u2013 for data silos, volume, value, fidelity and timeliness \u2013 ESG cannot be implemented at scale.<\/p>\n\n\n\n

For example, data transparency is necessary to ensure data quality and coverage and to render data palatable and easy to use for end users.  Multiple ESG data providers serve up a moveable feast of different and changeable scoring systems. This means that a data solution must alter the configuration of incoming ESG data sources and transform them to customer preferences. With data, preferences and investment propositions all evolving, a solution needs to be flexible and built to accommodate these changes. Ultimately, data transparency ensures data quality which end users can trust.<\/p>\n\n\n\n

Data management for ESG should be rooted in a strong data foundation and a scalable data and technology architecture. Software and data engineering capabilities need to be applied to the full data lifecycle which spans data capture, curation, consumption and governance. These capabilities help develop a common language across the data, encompassing architecture, user-interface development, server-side development, and quality assurance.<\/p>\n\n\n\n

The ideal software development process would use an Agile methodology, ensure knowledge-sharing with development partners, and leverage strong financial services domain expertise.  By adopting this approach technology teams, working with trading desks, quant teams and investment managers, can help build ESG functionality into the construction, analysis, monitoring and reporting of trades or investment portfolios. The approach makes it possible to perform ESG data load and transformation, ESG client preference capture and portfolio ESG modelling and reporting.  It\u2019s highly effective as it can be applied to pragmatically revisit and iterate plans, and build in flexibility and contingency. For example, by identifying the most urgent and important tasks for focused, immediate execution or eliminating waste and non-essential tasks altogether.<\/p>\n\n\n\n

Technology is a crucial enabler of ESG implementation.\u00a0 It allows organizations to develop business propositions ahead of changing market demand, open up new opportunities and accelerate business development.\u00a0 Without these capabilities, ESG engagement cannot truly get off the ground and move from narrative to action.<\/p>\n<\/div>\n<\/div>\n\n\n\n

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About the Author<\/h3>\n
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Digiterre<\/h4>\n\n \n \n <\/div> \n <\/div>\n
\n Date: 20\/04\/21<\/strong><\/span>\n Category: Tech Insights<\/strong><\/span>\n <\/div>\n <\/div>\n<\/section>\n\n\n
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