Companies most commonly miss data in four areas: double materiality assessments, Scope 3 emissions across the value chain, social and workforce metrics, and internal data governance. These are not minor oversights — they are the gaps that tend to delay reporting timelines, trigger audit findings, and leave sustainability teams scrambling. The sections below break down each gap and what you can do about it.
Which data gaps most frequently derail CSRD compliance?
The data gaps that most often derail CSRD compliance are double materiality evidence, Scope 3 emissions data from suppliers and customers, granular workforce metrics, and structured data governance processes. Most companies have some sustainability data: the problem is that CSRD reporting under the European Sustainability Reporting Standards (ESRS) demands a level of depth, traceability, and structure that existing data rarely meets.
Many organizations discover this the hard way, assuming their existing ESG reports or internal dashboards will transfer neatly into ESRS disclosures. They do not. CSRD requires specific data points tied to specific standards, and the revised ESRS adopted by the European Commission in July 2026, while significantly reducing the total number of mandatory data points by over 60%, still requires that the remaining data points be collected systematically and with clear documentation.
The most common pattern is that companies hold high-level data — total energy use, headcount, a rough emissions estimate — but lack the granularity, source documentation, or stakeholder input that CSRD reporting actually demands. That gap between having something and having what is required is where compliance tends to break down. Closing it requires a structured understanding of exactly which ESRS data points apply to your organization, what level of evidence is expected for each, and where your current data collection falls short against those specific requirements.
Without that mapping exercise completed early, companies routinely underestimate the volume of work involved and begin remediation too late to course-correct before their reporting deadline. The four gaps described in this article — double materiality assessment data, Scope 3 emissions data, social and workforce data, and data governance — are not equally difficult to close. Understanding which gaps are most urgent for your organization, based on your materiality assessment and reporting timeline, is the essential first step toward a manageable compliance process.
Of these four gaps, the double materiality assessment is typically the most foundational — and the most misunderstood. It is worth examining why it proves so difficult to get right.
Why is double materiality assessment data so hard to collect?
Double materiality assessment (DMA) data is hard to collect because it requires two distinct types of analysis — impact materiality and financial materiality — each drawing on different sources, stakeholders, and methodologies. Unlike a single financial metric, a DMA is a structured process that must be documented, justified, and defensible to auditors.
Impact materiality assesses what effect your company has on people and the environment, while financial materiality assesses what sustainability risks and opportunities affect your company’s financial position. Answering both questions requires gathering input from a wide range of internal and external stakeholders, mapping your value chain, and applying a consistent methodology to rank and document the results.
In practice, companies run into several recurring problems:
- Stakeholder engagement gaps: The DMA is not solely an internal exercise: it requires meaningful input from employees, suppliers, customers, and affected communities. Many companies underestimate how much time and structure this engagement requires to execute properly.
- Lack of documented methodology: Reaching conclusions about what is material is insufficient on its own. Companies must demonstrate how those conclusions were reached through a traceable process that an auditor can follow. Organizations often have informal discussions but no documented methodology to support their findings.
- Confusion between gross and net impacts: A key area of ongoing refinement in the ESRS framework is how companies should account for remediation, mitigation, and prevention actions when assessing impacts. Misapplying this distinction affects which topics fall within scope and which do not.
- No clear ownership: DMA processes typically involve sustainability, finance, legal, and operations teams simultaneously. Without clear ownership, the process stalls or produces inconsistent outputs.
What makes the DMA particularly challenging is that it is foundational: it determines which ESRS standards apply to your company and which data you need to collect across every other disclosure area. Errors at this stage produce knock-on effects that ripple through your entire report.
That is why many companies working on CSRD compliance find it worthwhile to engage a specialist CSRD reporting expert specifically for the DMA phase, rather than treating it as a task any sustainability generalist can handle. Once the DMA is in place, attention typically shifts to one of the most data-intensive areas it flags: Scope 3 emissions.
What Scope 3 emissions data do companies typically lack?
Companies typically lack reliable Scope 3 data for upstream supplier emissions (Category 1 purchased goods and services), downstream use-of-product emissions, and business travel and employee commuting. These categories are particularly difficult to measure because the data resides outside direct operations, with suppliers, customers, and third parties who may not track it at all.
Scope 3 emissions cover all indirect emissions in a company’s value chain, and for most organizations, they represent the largest share of total emissions. CSRD reporting under ESRS E1 requires disclosure of Scope 3 emissions where they are material, and for the majority of companies, they are.
The practical challenges tend to cluster around a few specific categories:
- Purchased goods and services (Category 1): This is generally the largest Scope 3 category and the most difficult to measure accurately. Obtaining actual emissions data from suppliers requires either primary data collection — through supplier surveys, invoices, or product-level data — or spend-based estimates, which are considerably less precise and more difficult to defend under audit.
- Use of sold products (Category 11): For companies selling products that consume energy during use — such as electronics, vehicles, or appliances — calculating downstream emissions requires assumptions about usage patterns, product lifetimes, and end-of-life treatment that are difficult to validate with consistency.
- Business travel and employee commuting (Categories 6 and 7): Many companies lack centralized booking systems or structured commuting surveys. The underlying data exists in fragments across expense reports, HR systems, and ad hoc employee surveys that were not designed to support emissions calculation.
- Upstream and downstream transportation (Categories 4 and 9): Logistics data is frequently held by third-party providers who do not report in emissions-compatible formats, making conversion and verification genuinely difficult without direct engagement with those providers.
Across all of these categories, the common challenge is consistent: the data exists somewhere, but it is fragmented, inconsistently formatted, and not owned by the sustainability team. Closing these gaps requires both a structured data collection strategy and a supplier engagement program, which is why Scope 3 work commonly involves specialists with specific experience in emissions accounting rather than general sustainability reporting.
The same dynamic — data that exists in principle but is not structured for reporting — applies equally to social and workforce metrics, which represent the next major gap companies encounter.
What social and workforce data is missing most often?
The social and workforce data most often missing for CSRD reporting includes pay gap metrics disaggregated by gender and employment category, data on non-employee workers in the value chain, health and safety rates for contractors, and supplier-level social risk assessments. Most HR systems capture headcount and basic employment data, but ESRS S1 through S4 go significantly further.
Companies are often surprised by how specific the social data requirements are. Having a diversity policy or conducting employee surveys is insufficient — CSRD reporting requires structured, quantitative disclosures that most HR departments have not historically tracked in a reportable format.
The gaps that arise most consistently include:
- Disaggregated workforce data: ESRS S1 requires data broken down by gender, employment type, region, and in some cases age group. Many companies hold this data in principle but not in a format that allows straightforward extraction and aggregation for reporting purposes.
- Non-employee worker coverage: Contractors, agency workers, and freelancers are frequently excluded from internal HR data systems entirely. CSRD reporting requires companies to account for these workers, particularly with regard to health and safety and working conditions.
- Value chain social risks: ESRS S2 addresses workers in the value chain, including suppliers and subcontractors. Collecting reliable data on labor conditions, fair wages, and working hours across a global supply chain is a substantial undertaking that most companies have not yet begun.
- Community impact data: ESRS S3 addresses affected communities. This requires identifying which communities are impacted by your operations and gathering evidence of actual or potential impacts — a scope that extends well beyond standard stakeholder communications.
The social data challenge is partly a systems problem and partly a scope problem. Existing systems were not built for this kind of reporting, and the scope extends considerably beyond what most companies have historically considered their responsibility to track.
Closing these gaps typically requires updating HR systems, launching new data collection processes, and engaging suppliers — all of which depend on something that many organizations have not yet put in place: a robust data governance framework. That brings us to the fourth and most cross-cutting gap of all.
How does poor data governance affect CSRD reporting quality?
Poor data governance directly undermines CSRD reporting quality by producing inconsistent data, unclear ownership, and an inability to demonstrate how figures were calculated — all of which become significant problems when sustainability disclosures are subject to third-party assurance. CSRD requires limited assurance on sustainability disclosures, which means auditors will assess where figures originated and how they were verified.
Data governance refers to the policies, processes, systems, and responsibilities that determine how data is collected, stored, validated, and used. In a financial reporting context, most large companies have this well established — but in sustainability reporting, it is often ad hoc at best.
The practical consequences of weak data governance manifest in several ways. Different teams apply different methodologies to the same metric: one business unit calculates energy intensity one way, another does it differently, and no central standard exists to reconcile the difference. When these figures are consolidated, the result is unreliable. There is also frequently no audit trail — when an auditor asks how a particular figure was calculated, the answer is often that someone entered it into a spreadsheet, rather than a documented, traceable process. And when no individual or team is specifically accountable for a data point, it either goes uncollected or is collected inconsistently across reporting periods.
The revised ESRS framework and the assurance requirements that accompany CSRD compliance effectively require companies to treat sustainability data with the same rigor applied to financial data. That represents a significant operational shift for most organizations, and it does not happen by default — it requires deliberate investment in systems, processes, and internal accountability structures well before reporting season arrives.
Given how much groundwork is needed across all four of these areas, the most pressing question for most organizations is not what to fix, but when to start.
When should companies start closing these CSRD data gaps?
Companies should begin closing CSRD data gaps at least 12 to 18 months before their first reporting deadline, and ideally earlier. The reason is straightforward: most of the gaps described above cannot be resolved quickly, and supplier data collection, stakeholder engagement for the DMA, and HR system updates all require time to design, implement, and validate.
Under the current regulatory framework following the Omnibus I directive (EU 2026/470), the CSRD now applies to companies with more than 1,000 employees and more than 450 million euros in net turnover. The revised scope applies to financial years beginning on or after January 1, 2027, with first reports due in 2028 — meaning the data collection processes that feed those reports must be in place well before the reporting year begins.
Companies already reporting under wave 1 are managing these gaps now. Companies preparing for 2028 reporting have a window to get ahead of the problem, but that window closes faster than it appears. Building a DMA from scratch, establishing supplier engagement programs, updating HR data systems, and implementing data governance structures are all multi-month projects — beginning them in the year before reporting is due means starting under pressure, with limited time to course-correct if issues emerge.
The practical starting point is a gap assessment: map the ESRS data requirements against what you currently collect, identify where the most significant shortfalls are, and prioritize based on materiality. From there, you can build a realistic data collection roadmap that accounts for the time required to align suppliers, HR teams, and finance functions. Companies that find CSRD reporting manageable are generally those that treated data readiness as a multi-year project rather than a pre-deadline sprint.
Ready to close your CSRD data gaps?
CSRD compliance does not have to mean months of internal confusion and last-minute scrambling. At Dazzle, we connect you with pre-screened CSRD specialists who know exactly where the data gaps tend to arise and how to address them efficiently. Whether you need someone to lead your double materiality assessment, build out your Scope 3 data collection process, or get your data governance in shape before your first reporting deadline, we can match you with the right expert for the job.
Our network of 150+ sustainability freelancers is available on a project or interim basis, so you get the specific expertise you need without committing to a long-term engagement. And because our matching process is built for speed, you can be working with a qualified expert within 48 hours of reaching out. If you are ready to get started, get in touch with our team and we will find the right fit for your challenge.
Working on your CSRD report?
The free CSRD Evidence Register lists all 14 documents, what each costs you and where teams get stuck, from a panel with Dazzle clients PostNL and Samskip.
