Across the value chain, one theme keeps coming up in conversations with brands, manufacturers, policymakers, and verifiers alike: data quality matters more than ever.
Whether a facility is tracking energy use, a brand is reporting Scope 3 emissions, or a regulator is evaluating compliance, decisions depend on credible, comparable data.
This is exactly why Cascale and Worldly have made data quality a top priority for the Higg Facility Environmental Module (Higg FEM), available exclusively on Worldly. Over the past several cadences, we’ve taken significant steps to strengthen the accuracy, consistency, and reliability of Higg FEM data — and in 2026, we’re going even further.
Why Data Quality Matters Now
The Higg FEM has become the industry’s foundation for environmental measurement at the facility level. Its role is broader than a single assessment: it feeds Scope 3 reporting, informs regulatory disclosures, and guides operational improvements inside factories. That means the quality of the underlying data isn’t just a technical question; it’s a credibility question.
The Higg FEM also continues to evolve. New data points, deeper quantitative detail, and alignment with reporting standards raise the bar for everyone — which is the right direction, but it also means we must continuously tune the system to support users and prevent errors.
What We’ve Improved — Why It Matters1. Better inputs: clearer content and stronger models
A major part of data quality is making sure staff at manufacturing facilities understand exactly what’s being asked and how to respond accurately. Based on feedback from Higg FEM users (yes, the manufacturers) and verifiers, we’ve:
- Rewritten and clarified questions that previously created confusion.
- Added new guidance and training materials in areas like chemical inventory management.
- Updated emissions models to align more closely with the Greenhouse Gas (GHG) Protocol.
- Introduced new validation rules to prevent impossible answers, such as inconsistent water usage or incorrect chemical data.
- “Get to Know the Higg FEM” webinar series and live Q&A sessions offering section-by-section deep dives to help manufacturers get answers and feel confident before submitting their assessments.
By preventing errors before they enter the system, we reduce the need for corrections later — and improve the clarity of data for manufacturers, brands, and regulators.
2. Smarter technology to catch issues early
We’re working closely with Worldly, making the most of its innovative platform as a central solution to elevate data accuracy.
- Automated outlier detection now flags anomalous data during self-assessment. In Higg FEM 2024, the system identified more than 12,000 potential outliers, with facilities correcting 41 percent of them.
- Facilities can now see their previous cadence values, helping them recognize when their inputs look unusual.
- The AI-powered Worldly Assistant provides immediate access to guidance and documentation, supporting users at the moment of data entry.
- Improvements to page load times, question structures, and table-based inputs reduce friction and accidental errors.
Members are already making the most of these dynamic updates; learn more on the Cascale Connect Higg FEM Learning Hub (members only). And, our own HowtoHigg website has also been enhanced to ensure easier access to information on how to use the tools and understand the methodology behind them.
3. A stronger, more consistent verification ecosystem
Verification is a core pillar of data quality. It’s also one of the most intensive areas of work within Higg FEM — and one where we’ve made major strides.
- Cascale has 570 trained verifiers across 73 Verifier Bodies as of November 14, supported by detailed protocols, QA checks, and annual performance reviews.
- In 2024 and 2025, our QA program significantly reduced avoidable verification errors through new consistency checks and verifier guidance.
- We increased the number of desktop reviews, counter verifications, and duplicate verifications, giving us clearer insight into verifier performance.
- New Verifier Body profiles and an expanded Quality Assurance Dashboard offer more transparency than ever before.
- Our automated VPM QA Rules cut the number of verified Higg FEMs containing clear errors by more than half.
Taken together, these improvements provide stronger confidence in verified Higg FEM data — which is essential for brands using these metrics in their reporting and decision-making.
4. Preparing for the next generation of Higg FEM data quality
Looking ahead, 2026 will be an important year. We’re piloting a new approach to more frequent and timely verification of key quantitative data — such as energy use, water consumption, and shipped volumes, in cases where such frequent or timely verification is warranted (in line with our recently published Principles on the Frequency of Environmental Data Reporting). Instead of reviewing these inputs once a year, we’ll be testing quarterly verification using remote methods.
The goal is simple:
- Catch issues earlier
- Reduce year-end corrections
- Provide more timely, fully verified data
- Support manufacturers’ own internal performance management
We are also deepening technical alignment with ZDHC on verification expectations — a step toward reducing duplication and strengthening cross-industry consistency.
A Continuous Improvement Mindset
Data quality is not a destination; it’s an ongoing discipline. Every cadence teaches us something new. Every round of user feedback highlights opportunities to clarify questions or strengthen checks. And every trend in the data helps us understand where the industry is improving — and where more support is needed.
The Higg FEM is the most widely used environmental assessment for consumer goods manufacturing. That reach gives us both the responsibility and the opportunity to continuously raise the bar, and to listen to the feedback our users are giving us.
By improving content, strengthening verification, and using technology more intelligently, we’re helping facilities, brands, and policymakers work with data they can trust — and ultimately enabling more credible environmental progress across the value chain.
