Data is the foundation of everything in sustainability. It’s what turns your targets into actual reductions: in energy costs, supply chain exposure, and climate-related risk. Without reliable, traceable, audit-ready data, everything else in your sustainability program is built on uncertain ground.

And yet, you’re probably managing sustainability data in a way that lags far behind the ambition it’s meant to support. The tools driving your financial reporting, HR operations, and supply chain management have matured over decades. But like many businesses, your sustainability data collection may still rely on spreadsheets, email chains, and a small team doing more with less.

Extra-financial data is increasingly held to the same standard as financial reporting. Investors, auditors, and regulators expect the same rigour, the same traceability, the same ability to defend every number. The problem is most organizations aren’t there yet.

According to Capgemini’s Sustainability in Action 2026 report:

• 80% of business leaders say their sustainability data is not comprehensive enough to inform sustainability strategy
• Only 16% describe their sustainability data as audit-ready
• 40% of organizations are still managing sustainability data through entirely manual processes 

These are not outlier results. They reflect the baseline reality for most sustainability teams today, regardless of industry, size, or geography.

Below are the seven data challenges that come up most in our research and in conversations with sustainability teams every day, and for each one, a concrete fix. Read on if you want to spend less time chasing numbers and more time doing the work that actually delivers impact.

1. Over-reliance on manual processes

The problem

Manual, spreadsheet-based data collection is the default for many sustainability teams. It gets the job done, until it does not.

• Data entry is slow, repetitive, and pulls focus away from strategy
• Files multiply, formulas break, and no one is certain which version is current
• Scaling across sites, business units, or reporting cycles compounds every inefficiency
• Manual processes make audit readiness nearly impossible to achieve consistently

The solution

Purpose-built data collection platforms replace the manual layer with structured, repeatable workflows. Data can be entered directly, uploaded in bulk, or pulled in automatically from existing systems. Consistency is built in from the start, not added later. Sustainability teams spend less time managing files and more time acting on insights.

Crucially, every data point is captured with complete data lineage: source, collection date, and contributor are recorded from the moment of entry, giving auditors a clear and defensible record.

2. Fragmented and decentralized data sources

The problem

Sustainability data rarely lives in one place, and that’s not a failure. It’s structural. Your sustainability footprint is the sum of everything your business does: what you buy, who you employ, how you operate, what you produce. That data already exists across finance, procurement, operations, and HR. The challenge is getting it to flow and harmonizing it.

• No single source of truth makes consolidation time-intensive and error-prone
• Different teams use different tools with no standardized format or process
• Multi-site and multi-country organizations face this challenge at significant scale
• Every reporting cycle starts with the same painful data-gathering exercise

The solution

A centralized data management platform creates a single location where all sustainability data, regardless of source or format, is stored, standardized, and accessible. By connecting to existing systems, the platform eliminates manual consolidation and ensures consistency across the organization.

3. Chasing data contributors and missed deadlines

The problem

For most sustainability teams, a significant portion of reporting time is spent not analyzing data, but hunting for it.

• Requests go out to dozens of internal and external contacts, often via email
• Reminders pile up, responses trickle in, and deadlines slip
• It is hard to know at any point what has been submitted, what is pending, and what is overdue
• Data arrives late, in the wrong format, or not at all

The solution

The solution is distributed accountability: structured data collection with the right outreach tools built in. With the right platform, you can assign ownership of specific data to the people who actually hold it, whether that’s site managers, procurement leads, regional teams, or supplier contacts. From there, your solution handles the follow-up. Stakeholders receive clear requests with defined deadlines, reminders go out automatically, and progress is tracked centrally. You can see exactly what’s in, what’s pending, and where to follow up. Your team stops chasing and starts operating.

Every submission, reminder, and approval is logged, so every action is traceable when auditors and regulators come asking.

4. Data quality issues

The problem

When data flows through multiple hands and systems manually, errors accumulate. The impact ranges from wasted time to material reporting mistakes.

• Unit conversion errors are among the most common and damaging data quality failures
• Duplicate entries go undetected across large data sets
• Inconsistent methodologies across teams or geographies produce incomparable figures
• Errors identified late in the process require full rework and can restart audit cycles

The solution

Defining quality rules manually, for every unit, every methodology, every entity, would be a project in itself. Domain-specific AI handles it instead: catching anomalies, flagging inconsistencies, mapping units, and surfacing issues before they reach a report. 

Every check is auditable, every correction is logged, and you stay in the loop at every decision point, with full data lineage ensuring 100% auditability. Multi-level approval workflows add a second layer of accountability, fully aligned with your governance structure, so the right people sign off on the right data before it ever feeds a disclosure.

5. Lack of real-time visibility and tracking

The problem

Not knowing where data collection stands is one of the most common sources of reporting delays. Many sustainability teams are working without a clear picture until the deadline arrives.

• Collection happens once a year, leaving no way to identify issues early
• It is difficult to distinguish between data that has been submitted and data that has been reviewed and approved
• Without visibility into individual sites or entities, gaps are only discovered late
• Senior leaders and auditors have no way to access progress without manual status updates

The solution

Real-time dashboards and progress monitoring tools allow sustainability teams and stakeholders to see exactly where data collection stands at any point during the cycle. Reporting frequency increases from annual to continuous, enabling earlier identification of gaps and faster course correction.

6. Scope 3 and supply chain data complexity

The problem

Scope 3 emissions, covering everything that happens upstream and downstream in your value chain, are the hardest category to measure accurately and the most significant for most businesses.

• Suppliers operate at varying levels of reporting maturity, making primary data collection inconsistent
• Without primary data, most organizations default to spend-based estimates that are difficult to improve over time
• Engaging hundreds or thousands of suppliers requires coordination at a scale manual processes cannot handle
• Scope 3 data collection often sits in a separate workflow from the rest of ESG reporting, creating additional silos

The solution

Not every supplier will respond, and not every team has the capacity to engage thousands of supply chain partners at once. That’s the reality. The right starting point is reliable third-party data, built into your solution from day one: industry-verified emission factors, supplier-declared data from sources like S&P, and spend-based estimates drawn from independent, peer-validated databases. This gives you a credible, defensible first layer of Scope 3 coverage without depending on supplier engagement to be perfect.

From there, you progressively replace estimates with primary data where supplier relationships and maturity allow, without rebuilding your reporting structure each time. Dedicated supplier engagement tools make this practical at scale: surveys go out to thousands of partners with built-in guidance tailored to different maturity levels. And to make participation easier, your suppliers get access to tools that help them respond, build their own sustainability datasets, and track their indicators over time.

7. Same data, different formats: the multi-framework problem

The problem

The same underlying sustainability data often needs to serve multiple reporting purposes simultaneously: regulatory frameworks, voluntary disclosures, investor questionnaires, and internal strategy reviews. Managing this across separate tools creates significant duplication.

• Data is entered multiple times in different formats for different audiences
• Framework-specific templates create siloed structures that are hard to reuse
• When data changes, it must be updated in multiple places, increasing the risk of inconsistency
• Teams spend disproportionate time on formatting and reformatting rather than analysis

The solution

A single source of truth means your data is connected once and flows across every framework from there. CSRD, GRI, GHG Protocol, CDP… The same dataset, surfaced in the structure each framework requires. When requirements change (and they do, for example the ESRS changed recently), your solution should update automatically. 

You don’t rebuild. You don’t re-enter. The data doesn’t move. The framework layer adapts around it with complete data lineage maintained throughout: every data point carries its source, collection date, reviewer, and transformation history no matter which framework it feeds.

What good looks like and how to get there

The data challenges outlined in this guide are common, but they don’t have to be permanent. 

Organizations that invest now in structured, centralized, and automated data collection will not just meet their reporting obligations more efficiently. They will build the data foundation that turns sustainability from a compliance exercise into a genuine driver of strategy. 

The technology exists. The frameworks are converging. The question is no longer whether to modernize your data infrastructure, but how quickly you can do it.