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The right type of data for you

When it comes to tracking progress, the type of data you use matters. 

Chapter Overview

The right data can make tracking progress easier and more accurate. It has enough detail to provide your company with an accurate picture of your progress, but not so much that it creates unnecessary complexity and cost without adding value. What constitutes the right data, however, will vary depending on the company and circumstances.


In this chapter, we’ll look at the different types of data for tracking progress, how to determine which is best suited for your company, and when and how to make the switch from secondary to primary data.

Primary vs. Secondary Data

There are two types of data used for tracking progress: primary and secondary. 


Primary data:

Primary data is data collected directly by a company or its partners along the value chain. It can be obtained in a number of ways, most commonly through field measurement, using techniques such as remote sensing tools or various sampling approaches. Examples of primary data include the exact amount of energy consumed by a supplier at a plant you source from, or the amount of fertilizer used by the farmers you source from.


Primary data is often more costly and resource-intensive to gather than secondary data, so it’s important to understand whether or not the benefits you’ll gain from using primary data will outweigh the burden of collecting it before deciding to make the switch. We’ve outlined questions below to help you determine if primary data is required to help your organization achieve its goals.


Secondary data:

Secondary data, on the other hand, is not company or value chain specific. Obtained from databases, it’s derived from regional, country or global-level practices and trends or statistical information, and is typically used to model emissions in databases like ecoinvent or GaBi. Examples of secondary data include energy grid mixes, average fertilizer use in a given region, deforestation at the country level, or average truck fuel consumption for a certain model. Secondary data is efficient for assessing high-level emissions and is widely used in footprinting.


Though not company or value chain specific, it can provide useful information about the impacts of different practices or products by region. This can help you draw conclusions about your own impacts, including where the main sources of emissions lie in your company’s value chain, and where you should focus your efforts to get the best results. Secondary data may be preferable for companies that:


  • Lack the financial or human resources necessary to obtain primary data,
  • Won’t derive more benefits from more granular data or additional primary data,
  • Trust the data housed in databases to be representative of their activities,
  • Are unaware of the levels of uncertainty behind secondary data,


More precise and accurate data may become necessary the further a company advances along its transformation journey,  particularly for reporting on annual progress towards emissions targets. Primary data, which by nature is more accurate and granular, may reveal that a company’s impact is more significant than previously believed. Though companies may be concerned that this could put them at a competitive disadvantage with peers using secondary data, the additional knowledge afforded by primary data will better enable them to effectively tackle impacts and focus resources where they’ll count.

A note on emissions factors 

Primary and secondary data should not be confused with emissions factors. Primary and secondary data refer to the input data used to model emissions factors. Emissions factors, by contrast, are coefficients that convert activity data into GHG emissions data (i.e., kg CO2 emitted per liter of fuel consumed or per kilometer traveled). Primary emissions factors are often built using a mix of primary and secondary data, whereas, secondary emissions factors, sometimes referred to as generic emissions factors, do not rely on primary data.

Choosing the right data for you

Collecting primary data is a resource-intensive process, so it’s important to understand if and where it is likely to add value before making the switch. Consider the following questions to identify where collecting primary data may be worth the extra investment to improve data accuracy:  


  • Is the data you are planning to collect related to a major driver of your emissions? 
  • Do you plan to report progress on efforts to decrease emissions from this source? 
  • How relevant is existing secondary data compared to your activities?  
  • How difficult would it be to obtain the primary data you’re seeking (i.e., is it related to owned operations or scope 3)? Do you have a sufficient level of supply chain traceability to support the switch? 
  • Do Product Environmental Footprint Category Rules exist for these products? If so, refer to the data needs matrix to help you make the right decision for your company.

There are, however, two major situations where companies should strongly consider transitioning to primary data:

Accounting for removals

If you plan to account for emissions reductions and removals, you’ll need to collect primary data, which is mandatory under the draft GHG Protocol for Land Sector and Removals.

Improving supplier performance

In a scope 3 supplier engagement context, primary data can be used to benchmark suppliers’ emissions performance and identify levers of improvement, which  could ultimately create opportunities to reduce your company’s scope 3 footprint.    

Key considerations for collecting primary data

If you determine that collecting primary data is the most appropriate course of action for your company, there are a few important things you’ll need to consider:

  • Data Quality: The quality of your data matters. It’s vital to take steps to ensure that the data you’re collecting is as high quality, relevant and consistent as possible, such as providing trainings for those responsible for gathering data and performing a deep quality review of the data once collected.  
  • Consistency: As pointed out in the chapter on rebaselining, data must be consistent across your footprint. So, if your company decides to plug a new primary data-generated emission factor into your footprint, you’ll need to check that the scope, underlying assumptions and  methodologies to determine if rebaselining is required.
  • Documentation: Companies should always document the overall data collection process as well as the source of the data in the event that any major changes occur.

Implications for tracking progress

When transitioning from secondary to primary data you should anticipate that there may be some immediate impacts on your footprint. You may see improvements, reductions or benefits.

However, the act of collecting data in itself doesn’t generate any true impact. Instead, these outcomes (improvements, reductions or benefits) are the result of a methodological change that enables companies to more closely capture the real impact of targeted activities and monitor progress made following these interventions. As such, they don’t count as actual emissions reductions. Making this distinction from the get-go is critical to ensure that progress tracking is as accurate as possible.


Keep in mind that if this methodological change represents more than 5% of total base-year emissions, rebaselining will be necessary.


The following practices will help you ensure that the transition from secondary to primary data is a smooth one.  

Recommendation 1

Determine if you really need to collect primary data.

Collecting extensive primary data can be an expensive and time-consuming exercise, especially if it doesn’t highlight improvements or bring additional insight. See above for the questions you can ask to determine whether it’s worth the effort. 

Recommendation 2

Manage expectations.

Communicate clearly, and as early as possible, to internal stakeholders, particularly those in the C-suite, that the numbers will evolve when data collection techniques are updated.

Recommendation 3

Establish a clear data collection and quality assurance process.

If you don’t outline best practices and processes for collecting data, you could end up with data that is inconsistent and unusable. Take the time to develop clear, concise guidelines that include information on identifying and clarifying data sources, standard operating procedures for data collection, data quality requirements and third-party quality assurance requirements.

Recommendation 4

Distinguish primary activity data from primary emissions factors. 

What you really want to collect is primary activity data, which will allow you to draw clear conclusions. However, you may receive primary emissions factors from a supplier. If this happens, you’ll need to review the scope and the underlying assumptions that went into the emissions factor. If there are variances with the generic emissions factors, which is likely, it’s important that you reach out to the source to understand why it varies from the numbers initially used.

Recommendation 5

Gain transparency over your supply chain.

Ultimately, your company’s ability to access (and benefit from) primary data is directly related to the level of transparency you have over your supply chain. Working to improve supply chain traceability and maintaining an up-to-date map of suppliers will put your company in a better position to (a) identify and assess the impact of its sourcing activities (energy consumption, land-use changes, etc.) and (b) switch progressively from secondary to primary data as your suppliers start implementing emission reduction initiatives.

Have questions?

We have answers. Get in touch with our team today and let us guide you through the solutions that might help you on your journey toward a sustainable supply chain.


Switching from secondary to primary data to measure the progress and changes generated by an intervention at a supplier level.

The above graphic illustrates that:

  • Switching from secondary to primary data is needed for the activities that are targeted as part of the intervention scope; 
  • The collection of primary data, in itself, does not generate any impact. It is a methodological change that more closely reflects the real impact of the targeted activities and enables you to track the progress resulting from the intervention.
  • In this particular example, a mix of primary and secondary data is the most relevant way to cover the system boundaries (in this case, a supplier’s footprint).

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