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Unlocking the Power of Global News for ESG at Scale
Leverage unstructured data and advanced NLP to uncover ESG risks, trends, and opportunities, driving informed decisions at scale.
Every day, the world generates a staggering 2.5 quintillion bytes of data (a quintillion is a billion billions). Much of this is unstructured data, which makes up roughly 90% of all data globally according to some estimates and is growing at an astonishing rate of 55–65% annually. But what does this mean for organizations, particularly those focused on Environmental, Social, and Governance (ESG) objectives?
The distinction between structured and unstructured data is crucial. Structured data—think spreadsheets and databases—is easy to manage and analyze. Unstructured data, however, includes the sprawling world of documents, images, tables, and more, lacking a standardized format and posing significant challenges for traditional software.
Despite its challenges, unstructured data is a goldmine for actionable insights, especially in ESG efforts.
Why unstructured data is a game changer for ESG
Early adopters in the financial sector quickly recognized the potential of unstructured data. For example, simple models once tracked how often CEOs mentioned "growth" in earnings calls. Fast forward to today, and ESG analysts are sifting through vast datasets—annual reports, sustainability disclosures, news articles, blogs, NGO publications—to uncover risks, trends, and opportunities.
This evolution reflects a growing recognition: ESG is not just a buzzword but a critical factor in corporate performance. Regulations like the Corporate Sustainability Due Diligence Directive (CSDDD), coming into effect in 2027, underscore this shift. Under CSDDD, companies must monitor human rights and environmental impacts across their supply chains—a monumental task when considering that large corporations may deal with thousands of third-tier suppliers.
The critical role of news in ESG
Among the many sources of unstructured data, news articles stand out for their breadth and immediacy. They capture real-time insights about companies, industries, and global events, often serving as early indicators of emerging risks or controversies.
However, news does not spread evenly. A local news story may take weeks to reach national or international prominence. Language barriers and editorial priorities can further delay its reach. This is why real-time monitoring of news at its source is vital. Pairing this with advanced taxonomies enables analysts to classify and prioritize stories aligned with specific ESG themes.
For asset managers, timely news monitoring can flag controversies, inform risk mitigation, and drive investment outperformance. For companies aiming to comply with regulations like CSDDD, news articles offer critical insights into supply chain risks, helping them stay ahead of potential crises.
Making sense of the data: NLP to the rescue
Thanks to advancements in Natural Language Processing (NLP), extracting meaningful insights from news articles is now scalable and efficient. Early models relied heavily on manual processes and vast amounts of labeled training data. Modern NLP, however, is far more intuitive and adaptive, enabling organizations to quickly identify patterns, entities, and risks without the cumbersome setup.
This technological leap has made it possible for Independent Software Vendors (ISVs)—like third-party risk management tools, supply chain platforms, and governance, risk, and compliance (GRC) solutions—to seamlessly incorporate news monitoring as a core feature.
Choosing the right solution for ESG monitoring
With so many providers offering news-based ESG solutions, how do you identify the right one? Quantexa News Intelligence (QNI) delivers the perfect balance of access to news providers with the required NLP capabilities to identify
Comprehensive News Ingestion: The solution should access at least 90,000 global news sources, and allow the ingestion of any paid, specialist data.
Multilingual Capabilities: Coverage across multiple languages ensures global reach and relevance.
Advanced Entity Recognition: NLP should identify entities with precision, e.g., distinguishing between Vodafone Group and Vodafone Egypt.
Corporate Hierarchy Mapping: Link mentions of subsidiaries (e.g., Walkers Crisps) to their parent companies (e.g., PepsiCo).
Customizable Taxonomies: Ensure the solution maps news to your ESG framework seamlessly.
Relevance Controls: Fine-tune the relevance of articles to your specific needs.
Event Clustering: Track the evolution of events over time for better context.
Geo-Location Data: Pinpoint the geographic origin of news for localized insights.
These features provide a strong foundation for organizations to derive actionable insights and maintain a competitive edge in ESG monitoring.
Embracing the future of ESG Monitoring
As technology continues to evolve, the ability to analyze visual-based data—such as images and videos—will further expand the scope of ESG insights. For now, organizations that embrace the potential of global news and advanced NLP are already well-positioned to lead in the ESG space.
The question is: Are you ready to harness the full power of unstructured data for your ESG goals? Contact us to find out more.
