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Building product carbon footprints starts with good data. In lesson six, you'll learn:
Why this is relevant: Getting the right data is often the biggest challenge in creating PCFs. Understanding where to find reliable data and how to collect it systematically will save you time and improve accuracy. |
Finding PCF data in your organization
Good news: Most organizations already collect much of the data needed for product carbon footprints (PCFs). But it’s often spread across different departments.
While different facilities will have different processes and approaches to data storage and organization, here are some key data sources and where you may find them:
Key data sources for PCFs
- Material inputs and quantities
- Production volumes
- Energy consumption records (including utility bills)
- Process measurements from manufacturing
- Fuel consumption logs
- Transport and logistics data
- Waste generation data
- External sources, such as:
- Supplier-provided emissions data (e.g. through supplied PCFs)
- Third-party verified environmental data
- Industry-specific databases (as highlighted in Lesson 3)
Where to find it: Your internal data is typically found across your Enterprise Resource Planning (ERP) system and operational records. Work with your finance, operations, and sustainability teams to identify the best sources for each data type in your organization.
Not sure where to start? Contact the relevant heads of department for the main data manager in each area. These people can direct you to the right person.
A guide to collecting data
Getting good data is key to building product carbon footprints, but it's often the trickiest part. Here's a straightforward way to tackle it:
- Preparation work: Identify appropriate contact points and build structured response templates to support your data requests.
- Send team requests: Communicate the importance of data accuracy, completeness, and timeliness. Create a process to monitor responses.
- Organize data: Structure the data you receive so it’s ready for PCF calculations.
- Assess completeness: Check for any gaps that could impact the PCF score.
And here’s what this might look like in practice:
Step 1: Prepare (Week 1)
- List all required data points for your PCF
- Identify which departments hold this data
- Create a simple data collection template
- Set realistic deadlines for data submission
Step 2: Request (Week 2)
- Email department heads with clear requirements (here’s a template)
- Include your template and deadline
- Explain why this data matters
- Set up follow-up reminders
Step 3: Track (Weeks 2-4)
- Create a tracking sheet for data requests
- Monitor incoming responses
- Send gentle reminders as needed
- Document any access issues
Step 4: Review (Week 4)
- Check data completeness by:
- Verifying units and measurements
- Flagging any inconsistencies
- Identifying data gaps
Step 5: Fill Gaps (Week 5)
- Find alternative data sources for gaps
- Use industry averages if needed
- Document any assumptions
- Note areas for improvement
Hint: Start with a single product or product line to test this process before scaling up to your full product range.
Key considerations to ensure data quality
As you found out in Lesson 2, activity data is one of the key inputs for the calculation of greenhouse gas (GHG) emissions. It refers to the data associated with the activities that generate GHG emissions. Examples include energy consumption, distance travelled, or quantities of materials purchased.
Collecting activity data is often the biggest hurdle when building product carbon footprints. Getting a robust, repeatable process in place to ensure data quality is a good step as you build out your PCFs. Here are three key aspects to focus on:
1. Consistency
Before using any data, check:
- Time periods align
- Values make sense compared to previous years
2. Completeness
Your data should include:
- All significant emission sources
- Full production cycles
- All relevant facilities
3. Representativeness
Verify your data:
- Covers normal operating conditions
- Includes seasonal variations if relevant
- Reflects actual production volumes
- Matches your reporting period
Quality check: For each dataset, document:
- Data source and collection method
- Time period covered
- Any gaps or assumptions
- Who provided the data