September, 2023

Build an Emissions Data Roadmap: a Four-Step Guide

Our take on making emissions data management less painful and more scalable.

This guide covers:

Aligning your sustainability data transformation strategy with your objectives

Establishing a strategic data transformation timeline

Building your data infrastructure effectively

Enhancing data collection across organisational and supply chain sources

4 key steps
Step 1
Align your vision & strategy
Step 2
Determine your timeline
Step 3
Set up your data infrastructure
Step 4
Optimise data gathering
the 4 steps to success
Step 1
Create a sustainability data transformation strategy that aligns with your business goals

Identify where you want to win the game: This will be defined by regulators, customers, and business capabilities. Prioritise issues and topics that you want to take your brand on and those that will make customers and consumers excited e.g., If you’re a beverage company, transitioning all PET bottles to environmentally conscious materials will be a compelling, noticeable initiative to push.

Identify where you want to play the game. Determine where you want to be respectable and hold your head high, but not necessarily be the leader.

pro tip

Look to your priority customers (B2B), consumers (B2C) and peers. Do your customers have any unique requirements (e.g., product-level data breakdowns), are there any topics that are particularly exciting for your consumers? Identify those and double down on them.

Step 2
Determine your timeline
The case for getting going:

No-regret moves: There are some no-regret, low-cost moves which likely won’t change with time e.g., switching to renewable energy (which holds a cost parity to non-renewable).

Data transformation timelines are notoriously unpredictable. Most overrun, are over-costed, and underdeliver. What you have right now is likely to be what you will have for the next couple of years.

The uncomfortable truth: supplier relationships don’t happen overnight. You need to invest time and effort into them.

Competitive value decreases. The value of taking a step ahead of the curb is likely going to disappear progressively with time. At this stage in time, you won’t be a first mover but you would certainly be a fast follower.

Wait for data visibility:

High-cost trade-offs need data insight. If you are putting meaningful money behind an initiative that requires fine-tuned trade-offs, it’s worth investing in a proper data system to ensure your investments pay off (in terms of impact and money).

Learn from first movers and observe best practice. Framing their systems and learning from their mistakes could reduce the need for full-scale investment - however,  this is risky and can often result in a deadlock.

Suppliers might be moving faster than you. Suppliers will have to figure out their side of the data problem; most of your supplier's data problem will also be your data problem. You can benefit from them putting in the effort and expense rather than you.

This tactic decreasingly holds water. Typically as a larger brand, you will face more scrutiny from stakeholders and regulators e.g., it is more likely that Tesco will take the wrath for not accurately reporting on supply chain emissions than an avocado farmer in Kenya.

Step 3
Set up your data infrastructure

View data gathering as an iterative, multiyear journey. There are a few key steps along this journey:


Identify the data owners within the business who are closest to the data e.g., a facility manager. Think of these data owners as your data collection swat team. These are important relationships to nurture as you will need to keep on going back to collect more data as you update your baseline.


Identify what needs to be automated. Identify the categories of data that are meaningful, going to be required frequently, and not too fragmented (doesn’t need too much effort to ingest).

A good candidate for automation: energy. Most markets only have a few big energy suppliers, and most energy invoices will look relatively similar. It is easier to digitise that sort of data. Energy is also a big driver of emissions and therefore a meaningful contributor to your inventory.

A bad candidate for automation: waste. In most organisations, waste is a small contributor to your emissions inventory (1-2%). The data is fragmented and rarely in a structured format for easy digitisation. It is not meaningful or easy enough to be worth the effort to automate that.


Identify what doesn’t need to be automated. Think about where your data comes from. Most of your vendors are likely to be pushing a particular platform/ software e.g., energy management platform, waste management platform, water platform. This can create a tech stack mess. You need to ensure that your data infrastructure can integrate into other platforms.

What to look for in a data management system

Flexibility: Having a flexible system early on will cater for evolving regulations and customer/consumer demands. Consider how the system is set up, what formats you can export & import in (e.g. JPEG, CSV, PDF), and how you can slice and dice data (e.g., by product, ingredient and location.)

Scalability: G-suite-based systems aren’t going to keep up as your company grows and regulations evolve. Prioritise a system that can easily ingest different data formats and engage multiple users across different teams.

Security:  As emissions data becomes increasingly accurate, it starts to represent a real cut of your intellectual property and should therefore be treated sensitively. Be aware of email-based requests for emissions data, it is not secure.

Step 4
Optimising data gathering
Internal data gathering:


Split the task. Reach out to the individuals in the business who are data owners. Get them engaged early on so they feel involved in the journey. Build ongoing data gathering into their workflow to avoid having to be the ‘data chaser’.


Recognise and celebrate internal contributions to energise people behind taking action.


Communicate the time investment. Data gathering is a long-term project, not a 3/4 month sprint.  As sustainability standards rapidly evolve, companies will need to continually work and report on data. Caveat this from the start to manage expectations.

Supply chain data gathering:

Even with the best of intentions, most suppliers have a deep-rooted aversion to sharing data with their customers unless the data is:

In their favour

Not business sensitive

To overcome this:


Build trust. Explain how the data is going to be used, and how it is not. Who will see it, and who will not. Sharing data with the whole business vs just the sustainability team might affect the willingness of your suppliers to share data. Emphasise that the sustainability journey is shared and that emission-intensive data won’t adversely impact the relationship.


Be pragmatic. Understand what is actually possible and realistic for your suppliers to do given their size, sophistication, and location. Get a sense of what you can realistically expect.


Emphasise commonality. Not just in vision but in expectations and standards across your company, as well as peers in the space.


Show value. Is there any upside? Demonstrate why it would make sense for them to share their data, even if the positive value is coming further down the line e.g., highlight if you are planning a big PR push on this topic. Positioning them as your supplier of choice would be a significant value add for them.


Co-create value. Go one step further. If you anticipate that your customers will pay a premium for a lower-emission product, consider how you can share the value. Even if you don’t have clarity on the numbers or magnitude yet, it makes sense to put this on the table and co-create thinking of what is possible in the space.