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ESG dataBruk Tadesse continues his series of articles about data. Here he focuses on data-related challenges in ESG

ESG refers to Environmental, Social, and Governance. These are non-financial factors used to determine investment decisions for investors who want sustainable growth wealth creation.

Data-driven, ESG measurements are used to examine business operations and behavior. Finding investment value in these data requires a high degree of clarity.

Data about ESG are abundant and growing, but also unwieldy. Investors want their data reliable and evidence-driven. They want standardization, regulation, consistent methodologies, and transparency to bring validity to data.

ESG data cover a wide range of issues, including climate change, racial/gender equality, and corporate governance. All aspects of ESG are equally important and interrelated. It is one ecosystem – one precious planet.

Investors have been voicing concerns about sustainability for several decades but, until recently, only as a “nice to have”. The new generation of investors, millennials, are the driving force behind that change.

Who’s who in ESG investing

  • Companies hold the key to integrating ESG concerns and disclose material issues publicly.
  • Investors, buyers, and sellers determine capital allocations to ESG compliant assets.
  • Data providers (Refinitiv, Sustainalytics, MSCI, TruValue Labs, etc.) serve investors’ ESG data needs.
  • Catalysts (Sigwatch, InfluenceMap, etc.) reveal critical ESG issues as they rise up the political agenda.
  • Regulators (governments) set ESG rules and regulations to comply with taxonomies to create a level playing field.
  • Standard-makers (Independent agencies like SASB, GRI, TCFD, etc) develop standards for reporting ESG data.

Regulatory challenges

In an ideal world, ESG financial materiality data relies upon a company’s self-disclosure. In the real world, companies do not always objectively or self-critically report on how well they have met their ESG goals. 

On the other hand, some companies despite their best efforts, find it difficult to interpret ESG impacts. 

And although regulators can enforce rules and standards to facilitate frameworks, ESG requires global cooperation to be meaningful. 

Some, like the EU SFDR have been enforcing stronger regulations recently. The UK Green Finance Strategy matches the ambition of the EU’s Action Plan for financing sustainable growth. Switzerland aims to be a leading location for sustainable financial services. Driven by investors, the United States SEC has just created an ESG task force. In China, green growth progress is driven by top-down pressure. Similarly, other regions are developing self-suited regulatory standards. 

Disparate ESG standards, however, hinder comparability. Investors are concerned that differences in rules and reporting standards allow companies to “cherry-pick” data to paint themselves in a good light. Regulations and standards will, however, eventually strengthen and become more harmonised.

ESG data challenge

Data, used for the evaluation of a company’s ESG performance, plays a vital role in the investment decision-making process.  According to many investors, however, having actionable “data” remains a big obstacle. 

Moreover, investors need “data” that is standardised, interoperable across investment ecosystems, and objective. ESG issues, though, are very qualitative, unstructured, and subjective in nature. It is easier to quantify, for example, business transactions data based on objective accounting standards. 

On the other hand, using and measuring unstructured subjective data (such as emails, documents, customer feedback, social media opinions, web page, satellite imagery data, audio, or video) requires not only competent data science skillsets but also advanced technological capabilities.

The Strategies

Information providers’ strategies vary. On one side, well-established and experienced companies like Refinitiv tend to trust and rely on trained research analysts to process publicly available data using artificial intelligence (AI). In contrast, a fintech ecosystem such as TruValue Labs is aiming to go beyond public data and analysts’ brain power by harnessing an arsenal of new technologies and alternative data sources. In-between these two perspectives, there is a wide range of data providers, such as Sustainalytics, who seem to incorporate both human insights and the support of AI.

At the same time, investors’ views and what they need in order to place their trust in the research process are equally diverse. Some believe human intelligence is key to this process. Others think that advanced technology is needed to make sense of billions of data points.

Transparency, of the data source and the methodologies used to process Environmental, Social, and Governance factors, is very important for investors to build trust in the service. However, it does not matter if humans or machines do the job. Without transparency, unanswered concerns will always remain both from tech-savvy and analyst-oriented approaches. 

In conclusion, it is important to examine how data technologies and know-how can be applied further to ESG analysis. And this I will do in my next article.

Further reading:

The Basic Concepts of Data by 


Principles for Responsible Investment,

Data is the ‘biggest challenge currently to Environmental, Social, and Governance integration.’ Environmental Finance,



Bruk Tadesse Bruk Tadesse

As a Business Data Analyst, I make use of data to empower business decision making, mostly in the financial market data industry.

I help companies or organizations improve their data process systems and play a key role in communicating technical and business issues between stakeholders.

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