How Asset Managers Can Leverage Unstructured Data

Asset Managers Need Data-led ESG Insights

Asset managers face a challenging environmental, social, and governance (ESG) investing landscape. They face a vast array of ESG data in the context of no universal reporting standards. What they seek are reliable ESG disclosures for ESG-themed impact investing needs.

Impact investments generate positive and measurable social and environmental outcomes with financial returns. Asset managers search for ESG integrated investments with impact investing approaches that contribute to outcomes like decarbonization, vaccines, etc. The key is quality data.

However, when we refer to data, most think about spreadsheets with names and numbers in a database, also known as structured data. Examples of structured data include Excel CSV files or SQL databases filled with financial transactions, phone numbers, or inventory information.

However, only 20% of data is considered structured, and that’s expected to decline further. One of the key hurdles to this is the data's qualitative nature and variability. Unlike other financial datasets, ESG measures must decode a company's actions and intentions.

Tackling the Complexity of Data with Advanced Technologies

The lack of standardized formats for capturing and processing ESG data is an industry-wide problem. The lack of comparable ESG metrics and associated ratings often makes understanding materially significant ESG allocation difficult for asset managers. They need support when gathering, processing, and interpreting ESG data reliably at scale.

Vast ESG data sets, composed mainly of unstructured data, require advanced data analytics and artificial intelligence (AI) to process and report ESG data quickly. Prompt reporting, in turn, will enable investors to verify progress reports with reliable data. ESG data solution activities begin at the collection stage and intensify during the analysis and verification stages.

However, many companies lack the technology and the tools to perform sophisticated data analytics and AI tasks on a large scale. Access to tools like machine learning (ML) and natural language processing (NLP)—ML/NLP—are necessary. Many ESG data and insights are trapped in documents, digital pictures and videos, audio files, web contents, social media chatter, climate sensors, etc. Leveraging unstructured data comes with its share of challenges.

Exhibit 1: Challenges confronting ESG unstructured data analysis

Source: Straive.

ESG data are usually updated only once a year, following annual company reports. However, daily tracking and a weekly update of critical events may be essential for monitoring a company’s performance across metrics. System alerts can be used for controversy tracking to quickly communicate material ESG changes to those supervising developments, for example, by using customized email alerts.

To that end, ML/NLP offers enormous potential for extracting information and mining insights from unstructured data.

Exhibit 2: The complex ESG data universe

Source: Straive.

Thus, effective ESG integration is made possible by an automated data extraction tool that can automatically identify, extract, and summarize meaningful information for better insights and faster decision-making. For instance, the Straive Data Platform (SDP) is a data extraction tool built on a microservices architecture leveraging ML/NLP algorithms to enable enterprises to get data faster, with better quality and scale. ML/NLP systems promise better verification through techniques, such as graph analytics, compared to voluntary disclosure and often opaque rating models.

The Emergence of Alternative Data

Often associated with financial analysis, alternative data is a term that typically refers to externally sourced information about a particular company to gain additional business insights. Common alternative data types include customer credit and debit card transactions, emails, geo-location (or foot traffic) metrics, mobile application usage, satellite or weather data, social and sentiment data, web-scraped data, and generic internet traffic, among others.

Exhibit 3: Major sources of alternative data

Source: Straive.

Because traditional self-reported ESG data, such as corporate social responsibility (CSR) reports, are released at particular times, they lack a real-time connection. But alternative data offer a quasi-real-time view of market sentiments using various routes, for example, social media chatter, news articles, satellite images, independent non-governmental organizations’ reports, and others.

Thus, ML/NLP platforms like the SDP are essential not only for asset managers but companies too. The Straive Data Platform (SDP) has been designed to serve industry use cases spanning text, public data, and visual intelligence. It’s an end-to-end ESG data engine, collecting data from news, annual reports, sustainability (CSR) reports, non-governmental organizational (NGO) reports, and other unstructured sources. The data is then transformed, structured with ESG concepts, and extracted, bringing meaning and insights to aggregated documents or news.

With ESG investing gaining prominence, a key industry issue for asset managers is not being able to trust company-reported ESG measures. Greenwashing is the deliberate practice of making a company appear more sustainable (or green) than it is. The preferred approach is to identify various ESG issues and weigh them for each industry to arrive at an overall ESG composite rating. Being prospective and dynamic by periodically adjusting industry-relevant points and weights can help keep the system looking forward and aid in understanding emerging risks and potential opportunities.

Because of greenwashing, investors seek methods to validate a company’s ESG credentials. Unstructured and alternative data sets are a critical element in the toolkit when developing a holistic and reliable ESG picture of a company. Moving beyond traditional data sets and including advanced and automated unstructured (including alternative ones) data analytics at scale is fundamental for effective risk management.

Closing the Gaps in Datasets

Asset managers need data sets spread across numerous operational systems, data warehouses, and other repositories. The data then must be cleaned and prepared for analysis. Cleaned, structured, and linked data are essential for efficient and beneficial ESG data analysis. Straive’s SDP ensures these qualities are obtained from unstructured and alternative data sets sourced from diverse sources. Furthermore, our robust quality checks and standardization processes deliver data that are superior in quality, clean, and fit our customers’ formatting requirements.

Thus, if you're looking to turn unstructured data into actionable ESG intelligence to meet your business objectives, you should contact us at: info@straive.com. Straive helps you create a more focused ESG and alternative data strategy.

¹ https://www.forbes.com/sites/forbestechcouncil/2017/06/05/the-big-unstructured-data-problem/?sh=7c60b184493a