Blog / Insights / Leading the Way in Real-Time ESG Scoring: An Overview of ESG Analytics

Leading the Way in Real-Time ESG Scoring: An Overview of ESG Analytics

While traditional ESG scoring provides a foundational understanding, the advent of real-time ESG scoring is revolutionizing the industry. ESG Analytics excels in this area, offering cutting-edge data and insights. This article will explore the groundbreaking methods of ESG Analytics in the realm of ESG scoring.

ESG Analytics: The Future of ESG Scoring

ESG Analytics employs advanced technology to swiftly detect and highlight ESG risks:

  • AI-Driven Instantaneous ESG Insights: ESGA Analytics employs AI to swiftly detect and highlight ESG risks, offering real-time ESG scores for companies worldwide.

  • In-Depth Analysis with the ESG AI Engine: Their proprietary engine gathers data from over 100,000 sources, processing vast amounts of data monthly, illuminating 26 ESG sectors and 16 SDGs.

  • The ESG Pulse Indicator: Leveraging Natural Language Processing, the ESG AI Engine detects ESG-centric signals and assesses them, culminating in the 'ESG Pulse' score—a real-time sentiment measure of a company's ESG performance.

ESG Analytics is pioneering the evolution of real-time ESG analysis, ensuring investors access the most up-to-date overview of a company's ESG attributes. For a holistic view of ESG risks and traditional scoring methods, you might also be interested in our article on Morningstar's Guide and Ratings.

Leading the Way in Real-Time ESG Scoring: An Overview of ESG Analytics

While traditional ESG scoring provides a foundational understanding, the advent of real-time ESG scoring is revolutionizing the industry. ESG Analytics excels in this area, offering cutting-edge data and insights. This article will explore the groundbreaking methods of ESG Analytics in the realm of ESG scoring.

ESG Analytics: The Future of ESG Scoring

ESG Analytics employs advanced technology to swiftly detect and highlight ESG risks:

  • AI-Driven Instantaneous ESG Insights: ESGA Analytics employs AI to swiftly detect and highlight ESG risks, offering real-time ESG scores for companies worldwide.

  • In-Depth Analysis with the ESG AI Engine: Their proprietary engine gathers data from over 100,000 sources, processing vast amounts of data monthly, illuminating 26 ESG sectors and 16 SDGs.

  • The ESG Pulse Indicator: Leveraging Natural Language Processing, the ESG AI Engine detects ESG-centric signals and assesses them, culminating in the 'ESG Pulse' score—a real-time sentiment measure of a company's ESG performance.

ESG Analytics is pioneering the evolution of real-time ESG analysis, ensuring investors access the most up-to-date overview of a company's ESG attributes. For a holistic view of ESG risks and traditional scoring methods, you might also be interested in our article on Morningstar's Guide and Ratings.

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The rich text element allows you to create and format headings, paragraphs, blockquotes, images, and video all in one place instead of having to add and format them individually. Just double-click and easily create content.

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The rich text element allows you to create and format headings, paragraphs, blockquotes, images, and video all in one place instead of having to add and format them individually. Just double-click and easily create content.

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The rich text element allows you to create and format headings, paragraphs, blockquotes, images, and video all in one place instead of having to add and format them individually. Just double-click and easily create content.

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Why is ESG data expensive?

The costs of collecting, analyzing and storing data are not cheap. And unlike financial data, there is no standardized process for determining ESG scores.The complexity of ESG data and the lack of standardization in the process for assessing environmental, social and governance factors also makes it difficult to compare companies on these metrics. Regulators are trying to make ESG information more transparent by mandating that companies disclose them alongside their financials, but this is still materializing globally. Traditional providers such as MSCI or Refinitiv employ armies of analysts to get this data from corporate disclosures (if it exists) and then normalize that data and provide it back to you. This is a very expenive process, with lots of quality control, and importantly - because this data is not disclosed very frequently (companies typically disclose ESG related data annually), there is less incentive to have a continuous subscription to a ESG data feed, along with risk of information leakage. All of this results in very expensive, and limited annual contracts.

Artificial Intelligence is changing the way we create and consume ESG data, which address many of the issues above - but that is a topic for another day.

Why is ESG data expensive? 6
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