Are You Relying on ESG Data That’s Self-Reported—And Self-Filtered?

Nearly 75% of M&A practitioners say ESG is part of their investment strategy—but only 51% fully understand how to evaluate it. And most ESG due diligence still relies on self-reported disclosures that can lack depth, accuracy, or objectivity.

Using external data—from sentiment analysis to geospatial monitoring—helps uncover ESG risks and opportunities traditional processes miss, especially in private markets.

Why External Data Gives You the Edge

✅ Identify ESG red flags that don’t show up in self-reported reports
✅ Uncover reputational risks hidden in social media, news, or consumer sentiment
✅ Translate intangible ESG performance into financial insights
✅ Benchmark ESG metrics across companies and markets
✅ Support defensible decision-making in M&A and investment evaluation

How It Works

🔹 Run sentiment analysis on global media to assess brand reputation and stakeholder concerns
🔹 Use geospatial data to detect environmental risk around operations (e.g., pollution, deforestation)
🔹 Summarize large volumes of ESG documents using NLP for rapid insight
🔹 Track social, governance, and environmental controversies in real-time
🔹 Benchmark ESG performance against industry peers using third-party indices and ratings

By integrating these signals into your diligence workflows, you move from surface-level reporting to deep ESG intelligence—without adding delay.

Real-World Impact: Scaling ESG Screening in Private Markets

Environmental Resources Management (ERM) uses external ESG data to conduct large-scale due diligence on private companies. With SESAMm’s NLP engine, they now scan 25 billion documents from 4 million sources—enabling faster, higher-quality reports even in data-scarce private markets.

Ditch the ESG Black Box—And Start Making Transparent Decisions

ESG due diligence shouldn’t be based on what a company wants you to see. External data delivers what you actually need to know.

📩 Want to uncover ESG risks hiding in plain sight?

👉 Contact Blue Street Data to power your diligence with data that goes beyond the PDF.

Live Webinar

Is “Quality” Killing Your AI? Defining Data Fit for Strategic Success

February 18th, 2026 / 1:00 PM EST

Every data investment carries risk unless you know how to measure its “fit” for the mission. Many organizations assume that “high-quality” data is sufficient for AI and analytics, only to discover too late that data fit is the real determinant of success. In this live session from Blue Street Data’s Building with Better Data series, Andy Hannah and Malcolm Hawker unpack why data that works for BI can be dangerous for AI, leading to model failure, wasted spend, and lost trust. You’ll learn how to define, measure, and validate data fit so your models deliver reliable, business-aligned outcomes. Reserve your spot today!