Why ESG analytics data is becoming a competitive advantage

As the sustainability boom continues to accelerate, there’s a growing awareness across financial markets that high quality companies are generally embracing all things ESG as a high priority strategy. But there’s also a growing awareness amongst investors that accessing high quality ESG information is not always as simple as reading a company’s sustainability report. We discuss below why high quality ESG analytics data is fast becoming a competitive advantage.

Why ESG data is important

The long term data presents a compelling picture of the benefits of being focused upon ESG investment issues. Higher ESG performance has been strongly connected to a number of key investment factors including:

1. Higher profitability and lower volatility:

2. Being a good allocator of capital:

3. Being good at cash flow management:

4. And having higher valuations and generating better financial returns:

It’s a compelling list of investment advantages for ESG-focused investors. And it makes intuitive sense. Management teams who are good at managing their environmental, social and governance risks and opportunities generally create more value for investors in everything they do: success breeds success. So ESG performance matters to investors and this explains why demand for meaningful ESG data is on the rise. This is why ESG data is more important now than ever, and will only continue to be the case.

But…there’s a problem

There are two main ways investors have traditionally accessed ESG information and both present data quality challenges:

1. Company sustainability reports – Companies present their ESG performance in annual sustainability reports which include details of how they are performing versus their ESG goals along with whatever other ESG information management choose to disclose. There are a number of challenges for investors with this ESG information source. Firstly, the publication of sustainability reports is an annual event which includes outdated information from the preceding year. Secondly, some companies use their sustainability reports to “greenwash”, which is a polite way of presenting progress, without actually doing anything. And thirdly, by picking their own ESG goals to track in their sustainability reports companies are presenting biased and subjective information. 

2. Traditional ESG rating providers – The other traditional way investors have accessed ESG data is through the ESG rating agencies such as MSCI, Sustainalytics, Bloomberg and Thomson Reuters. However, the challenge with this ESG scoring approach is that each rating agency has a different scoring approach which is not disclosed to investors. So the various ESG rating agencies present very different ESG scores for the companies they are scoring — and that includes for the world’s largest and best understood companies as shown below. It’s a remarkably confusing picture for investors looking for clear guidance regarding ESG performance.

Source: https://cyanreef.com/too-many-cooks-in-the-kitchen-why-the-esg-scores-differ-so-much/

The upshot of these ESG data access challenges is that there is a scarcity of high quality useful ESG data available across the market at a time when demand for high quality ESG data is booming. 

The solution: AI-derived ESG data is now a competitive advantage

There’s a growing realization that high quality ESG data has to be objective and up-to-date if it’s to add significant value for investors. As a result, many investors are increasingly turning to AI-derived ESG data to address the above mentioned challenges. This is ESG data which is compiled by trawling through vast amounts of unstructured data in ways humans are not capable of. The reasons for turning to AI-derived data are compelling. AI delivers what investors want and need: investment edge beyond what is available in the mainstream ESG data market. AI-derived data allows investors to follow ESG sentiment over time, to identify stock specific ESG risks, and to augment their financial analysis with meaningful investment inputs. And most importantly, AI-derived data is unbiased and current, so investors can use to it gain an edge versus the vast majority of investors who still rely upon traditional ESG data sources. 

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This brings an 'outside-in' view to ESG analysis, resulting in a more complete picture, that is up to date, and relevant.

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ESG and Data Analytics

In recent years, there has been a growing interest in the relationship between Environmental, Social, and Governance (ESG) factors and financial performance. As companies and investors recognize the importance of sustainability and corporate responsibility, ESG investing has become more popular.

One way to analyze ESG factors is through data analytics. By leveraging big data, companies and investors can gain insights into the ESG performance of companies and make informed decisions. For example, data analytics can be used to track a company's carbon emissions, employee diversity, and board diversity.

Moreover, data analytics can help investors identify which companies are better positioned to manage ESG risks and opportunities. For instance, companies with strong ESG performance may have lower costs of capital and be more resilient to market fluctuations.

However, it is important to note that ESG data can be complex and difficult to measure. There is often no standardized way of measuring ESG factors, and data can be incomplete or inaccurate. Therefore, it is crucial that companies and investors use reliable sources of ESG data and ensure that the data is properly analyzed.

In conclusion, data analytics is a powerful tool for analyzing ESG factors and making informed decisions. As ESG investing continues to grow, data analytics will play an increasingly important role in assessing a company's sustainability and corporate responsibility.

Enter ESG Analytics

ESG Analytics is an alternative ESG data providers meeting this growing demand for alternative ESG data which can’t be accessed elsewhere. The company is tracking non-traditional data sources across the enormous world of unstructured media to measure corporate ESG strategy implementation. ESG Analytics’ data helps ensure capital can be allocated efficiently with a clear and objective view of where companies are heading with their all-important ESG strategies in the future.

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With ESG Analytics, you can research company and fund profiles, create collections and watchlists and track ESG Events on a daily basis, allowing you to effectively monitor ESG risk.

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Conclusion

With more and more investors connecting the dots between ESG performance and investment performance, access to high quality ESG data is becoming a source of competitive advantage. The best way to access objective and up-to-date ESG data which is unbiased is to use AI-derived ESG data from the new breed of data providers such as ESG Analytics. AI-derived data is fast defining the ESG opportunity by providing up-to-date objective information which is genuinely useful for investors.