Missing data is one of the biggest obstacles in Life Cycle Assessment — and one of the least discussed. No matter how experienced the team or how advanced the LCA software, gaps in supplier information, upstream processes, or regional specificity show up more often than most will admit. It’s frustrating, especially when timelines are tight and decisions depend on credible results. But data gaps don’t have to stall progress. In fact, they can lead to more thoughtful modeling, stronger supplier engagement, and a better understanding of the system — if approached the right way.
Some gaps can be filled with primary data. Others might need smart assumptions or proxy datasets. The trick is knowing which strategy fits the gap — and when to use it without skewing results. Transparency matters. So does judgment. Rushing to plug holes with guesswork can erode trust in the results, but thoughtful documentation and sensitivity testing can build confidence, even when not everything is known.
Every Life Cycle Assessment includes some uncertainty. But professionals who know how to manage that uncertainty don’t get stuck — they move forward with clarity.
In this blog post, learn how to identify, evaluate, and fill data gaps using practical strategies, real-world tools, and tested approaches used by experienced LCA teams.
Understanding data gaps in LCA
Data gaps show up more often than most practitioners would like to admit. They’re the missing links — the unavailable, incomplete, or outdated information that leaves blank spaces in a Life Cycle Assessment. Some gaps are obvious, like a supplier who can’t provide energy use data. Others hide in plain sight: generic background datasets that don’t reflect a specific region or production method.
These gaps usually trace back to a few sources. Suppliers may hesitate to share data for confidentiality reasons. Upstream processes, especially in complex global supply chains, often remain opaque. Sometimes the data simply doesn’t exist — especially when dealing with new materials or technologies. Also, let’s not forget time constraints once not every assessment can afford months of data collection.
Ignoring these gaps risks distorting the outcome. Understanding where and why they occur is the first step toward making a Life Cycle Assessment more grounded, honest, and useful for decision-making.
Identifying gaps in LCA data
Spotting data gaps early can save a project from spiraling into guesswork later. But identifying them isn’t always obvious — especially when the data seems “good enough.” Look more closely, and cracks start to show. Below is a practical list of signals to watch for, and smart ways professionals pinpoint what’s missing before it affects the results.
Reviewing supplier data to identify where assumptions replace actual measurements
Start by digging into supplier data. Are numbers based on metered values or broad averages? If it’s unclear where the data came from, or if your supplier simply couldn’t provide process-specific information, that’s a red flag. Substituting estimates for real-world values is one of the most common — and most overlooked — sources of uncertainty in industrial Life Cycle Assessments.
Comparing process inputs to known benchmarks and questioning the outliers
If material or energy inputs look too clean, too round, or too efficient — they probably are. Comparing your process data to regional or sectoral benchmarks can help flag inconsistencies. When one plant reports 30% lower emissions than any similar facility, that likely signals a data gap masked by optimism or outdated assumptions.
Scanning for geographical mismatches between datasets and actual supply chains
It’s easy to overlook geography when using background datasets, yet location matters — a lot. Pulling a European electricity mix for a Southeast Asian facility introduces more than just a carbon gap — it can misrepresent water stress, land use, and more. Always double-check that the country or region in your data matches where the activity actually happens.
Checking for time lags by tracing how old the background data really is
Even well-documented background data can quickly lose relevance. A dataset published in 2016 might not reflect a supplier’s 2023 upgrades — or regulatory shifts that changed energy sourcing. Trace publication dates, update cycles, and methodological notes. If your data is older than the company’s current sustainability report, it might be telling the wrong story.
Looking at system boundaries to see what’s missing outside your frame
A tight system boundary might seem tidy, but it can also hide emissions or inputs upstream. For example, if a chemical feedstock appears at the factory gate with no record of extraction, transport, or processing, you’re missing part of the picture. Data gaps often show up where boundaries were drawn too early — or too conveniently.
Mitigating risks of ignoring data gaps
Most Life Cycle Assessments contain data gaps — that’s not the issue. The issue is pretending those gaps don’t matter. Overlooking them can distort your results, derail sustainability claims, and erode stakeholder trust. Wondering what that looks like in practice? Check out this list of overlooked risks.
Inaccurate impact assessments
When impact results are built on shaky data, they tend to look better — or worse — than reality. That’s dangerous. A low carbon footprint based on placeholder values doesn’t mean the product is low impact. It just means the numbers are incomplete. This creates false confidence, or unwarranted alarm, and can send entire strategies down the wrong path. Accuracy matters more than appearance.
Reputational risks in reporting
Every report tells a story — but data gaps can turn it into fiction. Stakeholders increasingly ask where numbers come from, how complete they are, and what’s been estimated. If companies can’t answer that confidently, credibility starts to slip. Regulators, investors, and customers all notice. Even unintentional overstated claims risk public scrutiny or worse — pulled products and legal blowback.
Impaired decision-making
Gaps in data don’t stay in spreadsheets — they ripple through product timelines, sourcing decisions, and regulatory filings. Without solid data, product teams may miss carbon hotspots. Procurement may pick suppliers without knowing their true impact. Compliance teams may submit reports with blind spots. One missing dataset can throw off months of planning, especially in heavily regulated or environmentally sensitive markets.
Reduced comparability across LCAs
One of the strongest uses of Life Cycle Assessment is comparing products, materials, or scenarios — but that only works when the data foundations are solid. If one assessment fills gaps with supplier data and another with generic proxies, the results become apples-to-oranges. That breaks comparability and weakens trust in the entire process. Consistency and transparency are what make comparisons useful.
Applying strategies for overcoming data gaps
Missing data doesn’t have to stall progress. There are practical ways to work around it without compromising the integrity of a Life Cycle Assessment. Curious where to start? Check this list of proven strategies — including some you might not expect — that help sustainability teams move forward with confidence, even when the data isn’t perfect or complete.
Primary data collection
Collecting site-specific data sounds simple — until it means asking a supplier located three time zones away and process data. But direct data is still the most reliable way to close gaps. Build relationships. Make requests clear and practical. Help suppliers understand why this matters. And prioritize what truly shifts results — not everything needs tracking.
Use of proxy data
No dataset? No problem — if you’re thoughtful. Proxy data can stand in, but only when selected carefully. Match based on function, geography, and scale. Use a similar process from a known source, and clearly document the reasoning behind your choice. It’s better to admit assumptions than to hide them. Proxy data isn’t perfect, but done well, it’s more than acceptable.
Leveraging LCA databases
Most assessments lean heavily on commercial and open-source databases — but knowing what’s in them isn’t enough. Understand how the data was gathered, when it was published, and how regionally relevant it is. Don’t mix datasets without checking for overlap or gaps. And always check whether a newer version exists — databases evolve quickly and silently.
Data estimation techniques
Sometimes, there’s no direct data and no proxy that fits. That’s when estimation matters. Engineers can model inputs based on production volumes, energy needs, or equipment specs. You can also use academic literature or consult experts. Just don’t just guess — make sure to document how you calculated it and why. A well-reasoned estimate beats a vague placeholder or blank cell.
Sensitivity analysis
When the data’s thin, the next step is asking: how much does it matter? Sensitivity analysis reveals how big the impact of your assumptions really is. If changes barely shift results, you can move forward. But if results flip with small tweaks, it’s a flag to dig deeper. Sensitivity helps separate what’s urgent from what’s simply incomplete.
Documentation and transparency
No one expects perfect data — but they do expect honesty. Document where the gaps were, how you filled them, what assumptions you made, and what impact they might have. Transparency protects credibility, even when your data isn’t flawless. And it builds trust with stakeholders who understand the process, not just the numbers.
Partnering up with suppliers
The first time you ask for emissions data, you might get silence. The second time, maybe a spreadsheet. Over time, suppliers get better — especially if they see it’s not a one-off ask. Build feedback loops, share outcomes, and recognize participation. Supply chain transparency improves slowly, but only if companies keep asking and keep showing why it matters.
Using tools and technologies for better LCA data
When data gaps show up, the right tools can make the difference between a weak model and a credible Life Cycle Assessment. Software and digital platforms can help streamline data gathering, improve quality, and support more consistent reporting. From AI-assisted estimation to blockchain traceability, the landscape is evolving fast. Curious what’s available? Check the list below to explore how different technologies can support stronger, more complete Life Cycle Assessments.
LCA software platforms designed to handle data gaps
Some LCA software platforms go far beyond number crunching — they now include smart features to flag, estimate, or annotate data gaps as part of the workflow. Think of this as a form of built-in quality control. Tools like P6 Technologies help practitioners track missing inputs, suggest proxy data, or even connect to shared databases. Not all platforms handle gaps the same way, so tool selection should reflect your industry’s complexity.
Emerging tools using machine learning for data estimation
AI in LCA isn’t science fiction — it’s happening. Some platforms now use machine learning to estimate missing process data based on thousands of previous datasets. Others can suggest impact profiles for novel materials or processes with no public data available. While these models require careful validation, they can be useful when working with innovative technologies or confidential inputs. The potential here is big — if assumptions are clearly stated and estimations are applied cautiously.
Digital product passports and blockchain for traceability
Digital product passports promise more than marketing transparency — they can become a living data source for Life Cycle Assessment. When combined with blockchain systems, they offer verified, traceable, and often real-time information about a product’s origin, materials, and process history. For supply chains with complex tiers, this matters. But adoption is uneven. It’s gaining momentum in sectors like electronics and fashion, but still emerging elsewhere. Keep an eye on regulatory pushes shaping this space.
Cloud-connected supply chain platforms with integrated LCA modules
Some supply chain platforms now include integrated LCA features — enabling companies to link procurement, logistics, and production data directly with environmental modeling. These systems help track real-time data and reduce manual effort in data collection. When paired with supplier engagement tools, they also support more accurate upstream inventories. Companies with mature digital infrastructure may already have the bones in place — it’s about plugging in the right modules and training teams to use them well.
Collaborative databases built through industry partnerships
Shared datasets created by industry consortia are helping fill long-standing data gaps in Life Cycle Assessment. These collaborative efforts allow companies to anonymously contribute process data, which then becomes part of a verified, sector-specific library. The renewable chemical industry, for instance, has seen promising progress through groups like Cefic’s LCA platform. While these databases take time to mature, they create lasting value.
Building long-term solutions for closing data gaps
Data gaps don’t vanish on their own — they shrink with commitment, structure, and foresight. If your team is tired of one-off workarounds, it’s time to start building systems that prevent the gaps in the first place. Below are long-term approaches worth investing in. Explore the list and consider which strategies suit your industry, suppliers, and sustainability goals.
Building supplier relationships and educating upstream partners
No dataset is more useful than one tied to your actual supply chain. Yet most suppliers still don’t know what information LCA specialists need — or why it matters. Closing that loop starts with education. Share why certain flows, inputs, and boundaries are critical. Help them understand how their transparency shapes your climate and compliance outcomes. Real collaboration starts with clarity.
Collaborating with industry peers to co-develop shared datasets
Some sustainability challenges are too big to solve alone — and data coverage is one of them. When manufacturers in the same sector compare notes, the result is often a shared dataset that fills in hard-to-reach gaps. Sector-specific databases, consortium-led efforts, and public-private collaborations are becoming more common. These projects require participation, not perfection. Even partial contributions move the field forward.
Investing in data quality through iterative collection and feedback loops
Data quality improves over time — but only if someone is paying attention. Rather than chasing perfect data once per assessment, shift toward an iterative model. Build recurring check-ins with procurement teams, operations, and suppliers. Document where assumptions were made. Ask what’s changed since last year. Gaps shrink when data gets reviewed and refined continuously, not in isolation every few years.
Responding to policy trends that are pushing material transparency
The regulatory environment is not standing still. From CSRD to product passport requirements, transparency is becoming less optional and more expected. That’s not a threat — it’s a signal. Companies that start closing data gaps now will be far better prepared when disclosure is no longer voluntary. Better yet, they’ll have a defensible footprint, built on evidence rather than estimates.
Building internal ownership structures that prioritize data collection
One reason data gaps persist is that no one owns the problem. Responsibility bounces between departments — operations, compliance, procurement — and gets dropped. Build clear accountability structures. Appoint data stewards, not just LCA users. Incentivize ownership of upstream data flows as part of broader ESG performance. Long-term improvement only happens when someone is tracking, questioning, and improving data practices over time.
Data gaps in Life Cycle Assessment
Data gaps are part of the Life Cycle Assessment process — not a flaw, but a signal. They point to missing supplier data, outdated sources, or incomplete boundaries. But knowing how to spot and handle them is what separates a passable assessment from a reliable one. From flagging mismatches in regional datasets to tracing assumptions in supplier inputs, early detection helps prevent costly reruns and questionable results.
The good news? Gaps can be managed. Primary data, proxy datasets, estimation techniques, and sensitivity checks all have a place — as long as they’re applied with judgment and transparency. Technology can help, too. The right LCA software doesn’t just calculate impact — it helps track, fill, and document gaps, streamlining the work that usually takes weeks into a few clicks.
No assessment has perfect data. But the ones built on honest modeling, clear documentation, and smart tool use come closest. Request demo to see how the LCA software powered by P6 Technologies can support filling the gaps in LCA data — and help your team move forward with confidence, not guesswork.
