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Climate change and other broader environmental concerns are creating more risks and opportunities for banks and other financial institutions. Consequently, this requires an adapted strategy and new type of data: ESG data. Despite the short acronym, Environmental, Social, and Governance data cover a broad scope, which extends even further to all data related to the Sustainable Development Goals (SDGs).


Why is ESG data relevant?

Similar to traditional bank data, ESG data can provide valuable insights that inform decision making. However, unlike traditional bank data, ESG data has not been collected long-term, is more forward-looking, is less structured, tends to be more external than internal data, and most of all has many different interpretations.

By not including ESG data input, traditional models and insights are beginning to diverge from reality. As a result, it is crucial for an organisation’s longevity to start the process of setting up a sufficient ESG data framework and strategy.


Why is it necessary to consider ESG data?

New sustainability regulations and requirements are implemented at a rapid pace. The European Green Deal and The Sustainable Finance Action Plan (SFAP) are at the forefront of these regulations, consisting of many initiatives that ask both organisations and individuals to become more sustainable. The SFAP requires companies to report and disclose their green activities, and financial institutions their exposures to these green activities. Consequently, financial institutions must set up new data, reporting infrastructures, and frameworks.

Additionally, financial supervisors have issued requirements for banks, insurers, and pension funds. These are aimed at ensuring climate-related risks are better quantified and capital will be deployed towards more sustainable investments.

As a result, organisations, and in particular banks, need ESG data to reach objectives within three domains.


  1. Compliance and reporting
  2. Risk monitoring and management
  3. Strategy setting and executing




What challenges are associated with ESG data and metrics?

Presently, ESG data is very fragmented within financial institutions with a lack of alignment across different departments and strategies. Within the financial sector, the absence of reliable data is becoming a widespread theme as ESG data transitions from alternative to fundamental data that informs decision making.

The data is considered unreliable for three main reasons:


  1. the ESG data points are unprecedented or have not been recorded,
  2. it is unclear how organisations should interpret unconventional climate data, and
  3. there are no internal control frameworks in place, including clear governance and ownership.




How is the ecosystem responding to the data challenges?

One common theme across all sectors is the need for clear and transparent data to provide a full picture that allows for better decision making when addressing the challenges.

There are many ESG data providers trying to fill this gap; however, many of these provide a company’s ESG rating, as opposed to raw data, which is often inconsistent across different providers due to differing opinions on the weight each input factor should carry. Furthermore, these opinions and their associated weighting factors differ from one rating provider to another. Consequently, it is often difficult to interpret results or compare outcomes.

Compounded with these barriers is a proliferation of sustainability labels, standards, benchmarks, and carbon accounting methods. Changing this requires both explainable insights and courageous leadership.


How can ESG data science tools help reach climate objectives?

Data, advanced data analytics, and AI can provide valuable insights and act as critical enablers to achieve our climate goals. From a risk perspective, the development and management of data-driven tools and algorithms will help analyse climate-related impacts on organisations, communities, assets, and countries. The outcomes of which should not only inform decision makers, but also be integrated in traditional risk models within banks. These forward-looking metrics can be used to monitor exposure and alignment of financial products and portfolios.

From an investment perspective, climate-related impact metrics and financed emission analyses should provide better insights that will inform investment decisions. Moreover, providing this additional insight will steer capital towards investments that will have the greatest positive impact.

However, the solutions are only achievable if your data maturity and data architecture is up to par to develop trust in the products built and exercises executed. Additionally, there must also be a clear ESG data foundation and strategy, which involves not only data quality, but also data controlling, connectivity, and accessibility.  Only then a financial institution should start more advanced data science projects like automated taxonomy labelling (supported by NLP), automated customer selection for portfolio diversification, or developing a tailor-made ESG rating model.


How can ADC help improve your ESG data architecture?

Our team at ADC has extensive experience in supporting organisations in bringing their data maturity up to par. Through our proprietary data maturity scan that considers the data maturity of a company, we personalise a framework for building an ESG platform that best suits the needs of your organisation. It is necessary to set up a strong strategy that is informed by the regulatory agenda to help your organisation reach a higher data maturity level and achieve long-term durability.

In addition, ADC’s framework considers several categories to determine four distinct stages of data maturity. This framework leads to:


  • consistency of ESG metrics and insights across different departments,
  • more efficient communication between departments,
  • effective use of ESG data and tools to achieve a wide range of objectives,
  • streamlined reporting, and
  • quick onboarding of new ESG data sets for ad hoc analyses.

At ADC, our end-to-end approach addresses strategy, utilises data engineering, and builds infrastructure and advanced analytics tools. Above all, we can help you apply ESG data architecture to unleash your organisation’s full potential. We are ready for the future, are you?



Let's shape the future

This is Part Three in a series of articles. You can read Part One and Part Two to learn more.

Do you want to know more about how we can help your organisation set up its ESG data architecture? Please contact Julia van Huizen or check our contact page.

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