Blog / Insights / Alternative data can rise to it's potential within ESG

Alternative data can rise to it's potential within ESG

The use of alternative ESG data by the institutional investment world is gathering momentum. Factset estimate that some 70% of global investment companies are in the early exploratory stages of understanding how to best utilize the growing opportunity set in alternative ESG data. Most of these professional investors are exploring the value of ESG and sentiment analysis information due to their growing awareness of the value this data can deliver their clients.

Non traditional data = Alternative data

Alternative data is data which is not available through traditional channels such as financial intermediaries, and includes information about companies and economies which helps investors improve their investment decisions. For example, investors can access alternative data which uses natural language processing of corporate news flow to provide more in-depth ESG insights than would be available to the broader market.

Alt data adds value through differentiated insights

As with most investment themes, it pays to be ahead of the curve on key investment factors, so it’s worth delving into how alternative ESG data does add value, specifically:

  • It helps analyse behavioural flows.
  • It helps make sense of economic attributes that may impact foreign exchange movements.
  • It allows investors to use sentiment to augment fundamental analysis, and to monitor changes over time.
  • It helps investors better identify stock specific risks through high frequency news flow.

It’s clear why the growth in alternative data demand is escalating as shown below.

Institutional investor spending on alternative data

How professional investors are using alternative ESG data

To create the highest value with alternative ESG data, professional investors are generally using it in conjunction with traditional ESG data which is often far more subjective and opinion-based. By combining the two types of ESG data together, investors can leverage the strengths of both strategies while mitigating the risk of using each strategy in isolation.

With this in mind, forward-thinking professional investors who are already integrating alternative ESG data into their investment decision-making process are focused on three key angles:

  1. Identifying the business processes, operations and outcomes they are aiming to achieve.
  2. Understanding the scope of the project and the underlying datasets they need. For example, they may target data management for a particular business vertical.
  3. Leveraging the most appropriate technology and infrastructure to ensure the best available data is being utilized. There are many options for investors to consider here, ranging from in-house to outsourced solutions, and there is growing demand for cloud based solutions. The key for investors is to invest in data management solutions which reinforce their strengths and identity.

Once investors have addressed these issues, they're generally ready to integrate alternative ESG data as a core strategy.

AI to play a key role in opening up the alternative data market

AI is emerging as the key driver for the alternative ESG data market because it delivers what investors want and need: investment edge beyond what is available in the mainstream ESG data market. In addition, the professional investment world is increasingly investing in an ecosystem of data solutions that talk to each other as seamlessly as possible. AI derived solutions tick this box.

ESG Analytics is one of a new breed of ESG data providers aiming to create the value the institutional investment world wants and needs. ESG Analytics’ AI technology scans the world of unstructured media to understand data about companies, flags analysts may have missed, or issues which have happened more recently than the information analysts are relying upon.

This AI-focused approach creates a master view of the ESG world based on ESG frameworks and knowledge, which in turn generates the most powerful, high value-add data for investors. Importantly, this is data which incorporates not only what companies say about themselves, but also what the rest of the world says about them. It’s objective data, particularly valuable as an investment tool, especially in combination with analyst based research.

ESG Analytics’ mission is to democratize the availability of high quality data for the market at large. We have moved beyond the time when only the world’s largest hedge funds have access to the best available investment data.

Savvy professional investors are becoming more sophisticated in the way they manage their investments, and are becoming more aware of the tools which could help them better manage investment risk, and potentially increase their alpha. This quest for tools which enable better investment outcomes is leading to escalating demand for alternative data sources.

Alternative data can rise to it's potential within ESG

The use of alternative ESG data by the institutional investment world is gathering momentum. Factset estimate that some 70% of global investment companies are in the early exploratory stages of understanding how to best utilize the growing opportunity set in alternative ESG data. Most of these professional investors are exploring the value of ESG and sentiment analysis information due to their growing awareness of the value this data can deliver their clients.

Non traditional data = Alternative data

Alternative data is data which is not available through traditional channels such as financial intermediaries, and includes information about companies and economies which helps investors improve their investment decisions. For example, investors can access alternative data which uses natural language processing of corporate news flow to provide more in-depth ESG insights than would be available to the broader market.

Alt data adds value through differentiated insights

As with most investment themes, it pays to be ahead of the curve on key investment factors, so it’s worth delving into how alternative ESG data does add value, specifically:

  • It helps analyse behavioural flows.
  • It helps make sense of economic attributes that may impact foreign exchange movements.
  • It allows investors to use sentiment to augment fundamental analysis, and to monitor changes over time.
  • It helps investors better identify stock specific risks through high frequency news flow.

It’s clear why the growth in alternative data demand is escalating as shown below.

Institutional investor spending on alternative data

How professional investors are using alternative ESG data

To create the highest value with alternative ESG data, professional investors are generally using it in conjunction with traditional ESG data which is often far more subjective and opinion-based. By combining the two types of ESG data together, investors can leverage the strengths of both strategies while mitigating the risk of using each strategy in isolation.

With this in mind, forward-thinking professional investors who are already integrating alternative ESG data into their investment decision-making process are focused on three key angles:

  1. Identifying the business processes, operations and outcomes they are aiming to achieve.
  2. Understanding the scope of the project and the underlying datasets they need. For example, they may target data management for a particular business vertical.
  3. Leveraging the most appropriate technology and infrastructure to ensure the best available data is being utilized. There are many options for investors to consider here, ranging from in-house to outsourced solutions, and there is growing demand for cloud based solutions. The key for investors is to invest in data management solutions which reinforce their strengths and identity.

Once investors have addressed these issues, they're generally ready to integrate alternative ESG data as a core strategy.

AI to play a key role in opening up the alternative data market

AI is emerging as the key driver for the alternative ESG data market because it delivers what investors want and need: investment edge beyond what is available in the mainstream ESG data market. In addition, the professional investment world is increasingly investing in an ecosystem of data solutions that talk to each other as seamlessly as possible. AI derived solutions tick this box.

ESG Analytics is one of a new breed of ESG data providers aiming to create the value the institutional investment world wants and needs. ESG Analytics’ AI technology scans the world of unstructured media to understand data about companies, flags analysts may have missed, or issues which have happened more recently than the information analysts are relying upon.

This AI-focused approach creates a master view of the ESG world based on ESG frameworks and knowledge, which in turn generates the most powerful, high value-add data for investors. Importantly, this is data which incorporates not only what companies say about themselves, but also what the rest of the world says about them. It’s objective data, particularly valuable as an investment tool, especially in combination with analyst based research.

ESG Analytics’ mission is to democratize the availability of high quality data for the market at large. We have moved beyond the time when only the world’s largest hedge funds have access to the best available investment data.

Savvy professional investors are becoming more sophisticated in the way they manage their investments, and are becoming more aware of the tools which could help them better manage investment risk, and potentially increase their alpha. This quest for tools which enable better investment outcomes is leading to escalating demand for alternative data sources.

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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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