Creating ‘Sustainability-Intelligent’ companies (post 3): data for reporting

So in our last blog post we listed 6 best practices regarding how to leverage data for Sustainability reporting. Last time we went deep into best practices (BP) 1, 2 and 3. And here we develop the other three.

Best Practice 4: Avoid Manual Data Entry As Much as Possible

The issue

Lots of software out there and even amazing industry initiatives are actually being fed via manual data entry, possibly through templated spreadsheets or emails, which inherently creates quality questions given the potential for human error and the lack of auditability. It’s always a journey, so no shame in starting there. 

Best practice

But you definitely want to have a roadmap to start moving your key data points, and the key ones from your suppliers, to automated scripts and ultimately well governerned fully automated data feeds with quality checks embedded in them.

The Benefits

This would also help solve the other major pain points in sustainability reporting: the high level of manual effort and the resources needed. Automation also becomes more and more crucial as you aim for higher data resolution and frequency.
Just to be clear though, as I’ve come across some healthy skepticism stemming from flawed automation. This can only be successful if paired up with data literacy (see BP2), and with some human sanity checks, and built-in features that help users track back specific assumptions. Otherwise you run the risk of having a black box system, sometimes contradicting expert-led estimates, with no one at hand to track down mistakes. So, like everything in data, there is no silver bullet. But done well automation is still the right direction.

‘Brain Puzzle’ 5: Supplier data headache: here I will keep the same initials but break from the best practice framework because my take is that this space is still quite immature. So we’ll talk of a brain puzzle, not a best practice, and try to outline an approach.

The need for high quality data at scale across a supply chain

At a high level, what matters here is that the source data be assured, automated and high enough quality, and then that the sharing of data meets all parties’ requirements.

On data quality, there is no shortcut. Companies need to embark their supply chain not just on the decarbonation journey, but also on the data quality journey. If everyone starts to follow the principles above, the whole ecosystem matures, data quality (and automation) improves across the supply chain. Initiatives and alliances that pool data across sectors should start including measures and incentives for quality in their models. 

Common pain points in sharing data

When it comes to sharing there are two common pain points: the many heterogeneous data asks that weigh on suppliers, and the sensitive nature of the data being shared. The first problem can be solved via more granular standards (like PACT) and/or via the right structuration of data to be able to extract all required data points, potentially complemented by an AI interface to fill in questionnaires. Humans could then review and edit instead of having to do it all. The sensitivity issue is harder, but has analogues and solutions that are used in other settings. Most prominently, the data clean rooms used in marketing that enable data processing across databases without having to physically ‘pool’ the data together, or the trust frameworks as developed by the Open Data Institute and Icebreaker One. We can’t go into detail here, but will definitely dive deeper in subsequent posts. 

Our recommendations

So in terms of recommendations in this space I would say: 

• Consider incorporating data Service Level Agreements (SLAs) into supplier contracts, basically mandating increasing levels of data access over time. Dimensions to consider include: granularity, frequency and automation of the data feed. Even with a templated spreadsheet via email you can automate processing. This practice is common in other areas (e.g. with marketing agencies), ensures access to high-quality data and also promotes accountability among suppliers. This may require a longer contract to justify the supplier’s investment in this.
   

• Consider leveraging industry-level initiatives, pushing your suppliers to join them, or leading one 

• Ecosystem initiatives like Perseus, or open data projects like the North Sea Transition one can bring huge value. Perseus has worked with electricity providers to make electricity footprint data available to every SME at a very granular level

• Initiatives like Manufacture 2030 are pooling data from common suppliers to then be used by their clients 

• PACT is defining more detailed data standards to help align methodology
  
  

• Consider potential tech solutions for direct integrations with strategic suppliers
  Concepts like data clean rooms (mentioned above) can offer innovative solutions. This AWS article gives an example of how this can be used to aggregate data.

BP6 :  Aim high but get there step by step, and bring your senior team and organization along with you. Data is always a (somewhat arduous) journey, with inflection points and acceleration once the people you need to involve start to ‘get it’. So cultural work is critical, to get the buy-in and keep your key stakeholders on board. Work on that as much as you work on technical roadmaps and capabilities. And then try to chew one big problem at a time, for example, get one large supplier right first while other conversations progress at a slower pace. Then you have a template for all to follow.
And stay positive along the way: the ecosystem will mature and make today’s pain points much smoother. 

Until next time

With that, we wrap up our first foray into ‘data for Sustainability reporting’. Stay tuned as we delve deeper into using data to drive faster action in the upcoming posts. If these topics resonate with you or if you have questions, we welcome your comments and engagement. Together, we can accelerate progress toward a more sustainable future.

Creating Sustainability-Intelligent Companies (Post 2): Data for Reporting part 1

Introduction

When discussing data for sustainability, the focus often turns to carbon accounting software, reporting and supply chain management. Typically companies starting on the sustainability reporting journey will engage consultants to do a first one off calculation of carbon footprint, identify key areas of reductions and set targets. Then they will on-board a software solution, engage their suppliers via that software’s survey module, and hope they will be done. 

However, relying solely on specialized software isn't enough to address the complexities of sustainability data, as the many re-statements and data quality issues experienced in the industry attest to. To bridge this gap, integrating data best practices from other disciplines is crucial. 

In this post I will outline some best practices to up data trustworthiness and scalability. Basically spreadsheets, or even sustainability-specific software alone, cannot solve this. But they can in conjunction with data best practices and tools that are already used for more mature data use cases (e.g. in marketing and product).

Before diving into this I want to remind us all that, ultimately, the goal should be action, not ‘just’ reporting. Reporting remains useful though because the published data enables others to act: investors, customers and employees. 

Best Practice 1: Design for an outcome 

Here, you might say, I just need to comply with the regulation. But is that it? Some will definitely only want to avoid the risk of non-compliance. But some will want to be seen as leaders, even differentiated by the granularity and trustworthiness of the data they share. So they may have a reputation objective. Or an objective to truly guide investor capital allocation decisions.

In most cases, these objectives translate into a need for trustworthy data. Increasingly, given the amount of data points required by a regulation like CSRD, there will be a need for data to be produced at scale in an automated way to avoid the massive inefficiencies and lack of auditability that come with spreadsheets and emails alone. And by that we don’t just mean inputting data into a software, but actually automating a data flow to a centralized repository, whether in a specialized software or somewhere in your existing data stack.

To be a good actor in this system you also need to share enough data with your clients, especially any B2B clients, to enable them to do their own reporting, while not compromising any competitive information. We will dig into data sharing further in our ‘BP5’. But in this early phase the critical point is to clarify your goals and the outcomes you are designing for.

The chart below illustrates the chain of decisions (orange arrows) that can be made by different actors in response to information being shared (purple arrows).

Chart 2: Macro impact of sustainability data

Best Practice 2: Foster Data Education and Literacy

Building data literacy across your organization is a key element of successful ESG reporting. By this we mean familiarity with the concepts, terms and data sources used. This includes key terms definitions, worked out with stakeholders, as well as intuition about units & scales. You want to encourage discussions, comparisons, and benchmarks related to ESG units and quantities. Education can take various forms, from regular discussions to highlighting relevant resources or formal training. By instilling a sense of data fluency at all levels, you empower your organization to engage more effectively with ESG data and spot mistakes.

In one organization I have worked with, a mistake slipped in the carbon footprint calculation, treating a monthly number as an annual number, leading to a 12x re-statement. This would never happen with £ or $, or other units your business is highly familiar with. The same goes here. Everyone should get a sense of what the footprint or carbon intensity of a typical individual or activity is and be able to apply basic rules of thumb to sanity check numbers they come across. Metaphors can also be useful to gain an intuitive sense of scales. For example, on warming, I like to compare earth temperature to the body temperature. Suddenly 1.5 degrees of warming and 3 degrees (and that trip to the emergency room) do seem very different!

Best Practice 3: Establish Data Governance Early On 

One fundamental step in enhancing data-driven sustainability reporting is the early establishment of a robust data governance process. Ensure that both internal and external data sources are included in this process. While Chief Data Officers (CDOs) often oversee sensitive business data, sustainability data may not always fall within their purview. For example, a delivery company I spoke with reported that, while they usually dealt with direct business performance drivers, the systematic use of gender data from HR systems was newly spurred by their sustainability initiative. They had to get their heads around this data - and apply some simple governance rules - to guarantee its integrity. The same happened to an automotive company setting out to systematically use some of their workshop data for the first time.

Collaborate with your Data team to advocate for the inclusion of relevant sustainability datasets in the governance process. This entails a shift in the mindset, recognizing that data quality isn't a given but a continuous effort. Involve data creators as "data citizens," fostering a culture of data ownership and quality standards.

Navigating the ESG Intelligence Journey: A Roadmap for Sustainability and Data Leaders (Post 1)

Introduction

After the stark climate warnings of 2023, sustainability leaders are feeling an increasing sense of urgency. They must accelerate progress towards their sustainability goals, such as reducing emissions, enhancing circularity, or closing the gender pay gap. Moreover, mounting regulatory pressure, such as the UK's Transition Plan and Europe's Corporate Sustainability Reporting Directive (CSRD), demands greater transparency, in turn creating more public scrutiny of disclosed targets, plans, and progress against them. Meanwhile, boards are increasingly treating sustainability as a strategic shift in business models, forcing deeper thinking about competitiveness and market opportunities in addition to risk reduction and compliance.

The Need for a Strategic Shift

Facing mounting pressures, Chief Sustainability Officers (CSOs) may be tempted to persist with their current strategies, steadily addressing sustainability issues with the tools at hand. However, I believe it is instead time to take a step back and develop a comprehensive plan that includes a holistic ESG data strategy. This strategy will position organizations to race towards net-zero and other sustainability objectives by 2030 and beyond.

The Role of Data in Sustainability

As a strategist and three-time Chief Data Officer I’ve had the privilege to help iconic media companies respond to disruption through digital transformation. Today, the sustainability revolution similarly requires profound changes, starting with re-redefining companies’ core missions and competitive advantage in line with evolving consumer, employee, and investors’ preferences. And then accelerating change and weaving sustainability in every aspect of what they do.  

To re-set their priorities, meet reporting challenges and achieve ambitious sustainability targets, organizations need trustworthy data, which in turn serves as a conduit for macro-level change via the myriads of decisions it enables (e.g. by investors, consumers and employees). To speed up, they will have to overcome inefficiencies coming from manual data entry, un-governed data sources, target metrics that may not effectively incentivize senior management, and emerging carbon data silos. And the need for higher granularity and frequency of data to fuel action and provide feedback loops will only grow, increasingly requiring automation. Much like our nervous system informs our actions through sensory inputs and commands, data serves as the nervous system for action within organizations. As such, data is central to strategy formation as well as operationalization of change. And those who understand that and develop a robust data approach will have an edge in moving towards ESG leadership.

Navigating the ESG Intelligence Journey

To truly embed sustainability in strategic thinking, organizations must ultimately evolve into ESG intelligent entities. This journey will be transformative, and those who embark on it now will lead the way in sustainability. But it is also a formidable challenge. 

To navigate this terrain effectively and grasp where data can add the most value, we have segmented the journey into three stages of ESG value creation, each offering distinct data opportunities, as depicted in the chart below:

• ESG performance: Measurement and Reporting
  ESG performance  is where most efforts focus today, driven by new disclosure requirements. Data plays a critical role in measurement and reporting, ensuring compliance, and building trust among consumers, clients, employees and investors.

• ESG Drivers: Actions and Leading Indicators
  Upstream from ESG performance, ESG drivers encompass actions and leading indicators that improve ESG performance. Examples include diversity in the recruitment pipeline, or the proportion of suppliers who have set emission targets. Data here focuses on understanding impactful actions, measuring progress and helping move the needle via predictive models and algorithms

• Business impact of ESG performance
  Downstream from ESG performance, this stage ascertains the business benefits of ESG improvements, such as risk reduction, efficiency gains, reputation enhancement, competitiveness improvement, or even lowered cost of capital. Data helps measure the impact of ESG progress and attribute it to specific elements of the strategy. 

While data for reporting and data for action will start to unlock value, ESG intelligence arises when fit-for-purpose data and insights capabilities support value creation across all three stages.

Conclusion

In this blog post series, we will explore those stages, beginning with best practices for ESG reporting data, moving on to how data supports ESG action and performance improvement, and ultimately envisioning ‘ESG intelligent’ organizations. 

If you are interested in those topics please comment, make sure to follow me and Shyftr, and stay tuned for more insights. Feel free to share questions or alternative perspectives. And to learn more about Shyftr, you can visit  our website. 

ln the meantime, we will leave you to ponder - and hopefully share your thoughts - on a couple of questions. Where do you see your organization on the path to ESG intelligence? What pain points have you encountered and how are you overcoming them?

Why Data and Sustainability?

Climate change fundamentally is a physical challenge, so how is data relevant to addressing the climate crisis? Perhaps the unpalatable, somewhat tired, metaphor of data as the new oil should have alerted us.

Yet, as we steer our way through the shifty waters of mitigating climate change and adapting to it, and try to solve fundamental collective action problems, the need for clarity, alignment, agility and speed is higher than ever. And those are indeed the benefits of data well designed and leveraged. Trusted data and thoughtful metrics can define the collective goal post and cascade goals down to all relevant contributors, making every contributor clear about their impact. This is how we have already gone from a global carbon budget, to country contributions, sector-level pathways and company-level science-based targets. Accessible, relevant, data in fast feedback loops can also increase agility in the actions we take to decarbonize, or in allocating capital in the right places. More generally, the ability to measure progress but also to act against data in human or automated ways is indeed fundamental for us to progress at pace. 

In fact, in its 2023 Impact Study Chapter Zero has found that, while most corporate boards now consider climate to be of strategic importance (about 90% say it presents opportunity and innovation, and only 15% view it as primarily regulation-driven), data availability is the second most cited barrier to taking action (24%), only second to the need to trade off long term resilience against short term commercial imperatives.

At a higher level, we at Shyftr see science-backed policy and technology as the two leading forces in terms of climate progress: as regulators clearly signal the direction of travel and key technologies mature, i.e. become able to scale and be cost-effective, change becomes possible. We also see finance and corporate policies as key transmission mechanisms, spreading change across portfolios and supply chains. And consumers and employees crucially act as catalysts as their preferences shift. But for that to happen trusted data flows need to serve as the nervous system for action, sharing signals for consumers, employees and investors to base decisions on, and enabling key actors within organizations to coordinate their actions. We will develop this theory of change in subsequent posts. 

But, for now, to stay practical, what uses of data are we talking about? With evolving regulation, reporting today remains at the heart of the ESG teams’ priorities and usually represents a large proportion (up to 80%) of their time and bandwidth. So the big vision of data driving alignment and agility may sound like just that: a lofty vision hard to put in practice.

However, consider this: companies will have to wrangle many data sources, including external ones, to be able to calculate even just one of their ESG metrics. Let’s take emissions as an example. Emissions are calculated in equivalent CO2 tons by applying emission factors to activity data. Excel buffs can think of it as a giant ‘sumproduct’. For example, electricity in the UK has an average emission factor or 0.143 kg of CO2e per kWh (as per number provided by the UK government here), so if you use 40,000 KWH of electricity in a year (as a midsize business would), that would generate an estimated 5.7 tons of CO2e, a bit more than half of the average UK resident’s personal footprint (around 10 tons of CO2e). You immediately can see how this seemingly simple calculation can get more complex as you try to incorporate the shifting electricity mix that will depend on time and location of your connection to the grid. In windy weather near the large UK wind farms, the emission factor may fall significantly. And as the grid greens (this 0.143 was close to 0.5 back in 2013, just a decade ago), and you go for greener utilities and robust green tariffs, then you want to capture that change in the emission factor. The complexity is why an initiative like Perseus by Icebreaker 1 is amazing, aiming to unlock access to this data, at a granular resolution, for all SMEs in the UK.  But overall, companies who want to calculate their emissions at a level of granularity that allows them to really see the impact of changes they make, still spend a lot of manual effort, spreadsheets, and custom data requests across departments that are not used to providing this specific data. And those data sources oftentimes haven’t been cleansed or governed to the same degree as more routinely used sources.

So, what does that mean for our lofty vision? Well, what it means is that even just helping to solve the reporting challenge by identifying core internal and external datasets needed, cleansing them, governing them and connecting them, will potentially save many man-days of work in most companies. This in turn will free up resources to really play the change champion role that ESG teams are yearning to play.

And if you do this smartly, you can ensure that this data can also be used to drive decisions at the next level down, to actually help improve your ESG performance. That is the approach that Julien Weyl took as Head of Sustainability at Stuart. His focus from day 1 was to deliver the maximum level of impact possible, by really scaling sustainability and making it part of the fabric of the organization. He defined strategic objectives for sustainability, and then used the OKR (Objectives and Key Results) approach already in place at Stuart to cascade goals down across the organization and create a framework where everyone could contribute, and be held accountable for that contribution. And he is very clear on how critical data was to that process: ‘First, we had to understand the data very well, then be able to communicate this data and make it visible. For example, we provided data about carbon emissions within the dashboards teams were already using, all the way up to board discussions. We had to provide the data side-by-side with other business-relevant data, within the decision structures that already existed. That was really important and not that easy to do.’

Businesses also have opportunities to integrate ESG in their views of customer preferences and of market opportunities. They may even be able to unlock access to green financing instruments that will lower their cost of capital if they reach their ESG targets. But all of that requires trusted, shared data. It is no coincidence that a company like Holcim, that has put climate at the heart of its strategy and issued a $100m sustainability-linked bond based on its 2030 CO2e target, also has 7 full pages of data tables in its 2022 Sustainability Performance report.

Of course, software platforms can be of tremendous help on the journey, and there are now several well-funded carbon accounting, as well as ESG data management, software players. But even when adopting one of those solutions, fully owning your ESG data strategy and taking ownership of the relevant internal datasets is a critical step that helps smooth implementation and improve trust in the data. Rachel Delacour, CEO at the helm of Sweep, a leading carbon management platform, said it well when we first met: “Carbon management is a data problem and a network problem at the core. And companies where data experts are involved are the ones where we see the fastest progress.” 

We will stop here for today, just short of revealing the alternative data metaphor(s) we favor. So follow us on LinkedIn to hear that, and more. This is a vast topic we will tackle over many (shorter) coming posts. We hope today’s write-up gives you a flavor of why we at Shyftr are so passionate about this space, and believe that data in service of ESG really can boost your business’s strategy and competitive advantage as we transition to a low carbon economy.