You Are Not Alone: ESG Data Challenges Test the Chief Sustainability Office on Performance

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Liam Bossi Co-founder & Head of Consulting
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Climate action is taking center stage as momentum builds in the run up to the COP26 climate conference in November. Reaching a net-zero future will require firm commitments and action from a variety of stakeholders. At Good.Lab we believe the private sector possesses enormous potential to drive climate action and be a force for good. That’s why we’ve made it our central focus to support companies in setting and achieving ESG performance targets by harnessing the power of their ESG data.

As any Chief Sustainability Officer (CSO) will tell you, reaching their long-term ESG aspirations requires a significant investment of time, energy, and capital. However, long-term aspirations without near-term goals are insufficient, and interim climate targets are essential markers of accountability. Ultimately, achieving these goals depends upon a quantitative data foundation, one that is time consuming to gather and resource intensive to decode, not the mention the numerous reporting frameworks and surveys, each requiring a plethora of unique data points, limited data visibility, and necessity to focus to generate the desired outcomes. 

But what if there was a way to make ESG data management a smooth and straightforward process, so that companies can prioritize, scale-up successful projects, and develop data-backed ESG strategies that achieve impact and realize ROI instead of just conducting a box ticking exercise of “ESG accounting”? With this objective in mind, we interviewed CSOs from 30 companies to learn from the best in the field and made a $3,000 donation to Project Drawdown in exchange for their valuable time. I’d like to share some takeaways from what we heard in those interviews with the hope you find it useful for the work that lies ahead. 

Top 6 ESG Data Challenges for Chief Sustainability Officers

1. Defining ESG Metrics & Materiality  

As ESG has exploded in popularity so too has the number of reporting frameworks companies are expected to report into. Whether it’s the GRI, SASB, CDP, or GRESB companies expressed difficult in keeping track of all the metrics they need to report on and decide which ones to prioritize. 

At the same time, materiality invites businesses to take a deep look inward to determine what ESG issues matter most from a financial standpoint to focus their strategy and data collection. Even before the ESG data collection begins, it is crucial to determine whether the goal is to respond to a given reporting request or rather because the company has identified the issues that are most material to their business and to drive performance. A few CSOs interviewed expressed regret in not fully addressing materiality prior to launching into the data collection process.

CSOs also expressed a challenge around determining which metrics their company was willing to make public as opposed to keeping confidential. This is not an easy task and often requires negotiations with high-level stakeholders, requiring CSOs to keep up on industry best practices and benchmarks that have been set to ensure they are among the sector’s leaders.

2. Internal ESG Data Collection 

The CSOs we interviewed expressed that moving from no system of record, i.e., spreadsheets to a centralized, ESG data platform required tremendous coordination and management. Collecting ESG data from across their organization was cited as the most time-intensive process along with the sheer volume of requests for this data. 

“My team spends endless hours collecting ESG data from across our organization, meanwhile, I am drowning in data requests.” 

Chief Sustainability Officer, Global Hotel Chain

Finding carbon equivalents for the operations side and not just the products side is a major challenge. For some, tracking down data from facilities or getting it from property owners can be an arduous process, since it requires going beyond just the buildings and electricity a company uses to include things like different refrigeration chemicals, equipment, freezing agents, and gas used. Meanwhile on the social side, tracking metrics for volunteer hours proved to be a labor-intensive data crunching exercise. Additionally, attaching ESG data to the financial system or ERP to ensure it is auditable is yet another layer of the challenge that requires specialty knowledge of these systems and cross-departmental coordination.

Some lamented that their sustainability teams are small, siloed, or have a limited budget. According to the Weinreb Group Sustainability Recruiting’s latest CSO research, while the issues of a sustainability team are constantly evolving and the functions’ internal visibility heightening, the size of these teams has remained static over the past ten years. Often, companies recognize their best path forward may be to hire experienced consultants to advise on their ESG projects, implement software applications, or outsource ESG data collection.  

3. External ESG Data Collection 

Collecting ESG data from the supply chain and other Scope 3 operational nodes also proved to be a significant hurdle for companies, especially for fast-moving consumer goods with complicated supply chains. Many CSOs are working to align their suppliers to commit to completing ESG disclosure. Others mentioned that convincing suppliers to sign up for ESG software (to measure and track emissions and performance) is a costly proposition and met with resistance. A streamlined approach could see suppliers inputting their ESG data quarterly with companies then reviewing the ESG data as needed for their own reporting cycles and is something many commented they are working towards with their business partners.

4. Sharing Data with Rating Agencies  

ESG ratings are still wildly inconsistent stemming from ratings agencies having vastly different methodologies. Despite the inconsistency in ESG ratings across reporting frameworks ratings are widely sought after by CSOs, yet a poor rating could harm their company reputation. 

Several pitfalls exist in sharing ESG data with rating agencies that CSOs expressed they must be careful to avoid. For instance, the reporting process does not include much leeway for data errors, with many reporting frameworks only allowing 30 days to correct any issues. Additionally, while some rating agencies send notifications on changes in ratings, it is an opaque process that does not clearly inform companies on what issue is causing the ESG rating change leaving CSOs guessing. Until more standardization is brought to these ratings, they’ll continue to be difficult to navigate and often leave companies without clarity on how to improve their rating.

5. Data Validation & Third-Party Assurance  

ESG performance depends upon accurate data. When a company reports, accuracy of statements is everything and requires verifiable data. Without quantified data, companies’ risk being accused of greenwashing or reporting false or misleading information which for public companies can have legal consequences and draw SEC scrutiny. It’s rarely due to negligence, for example in cases where companies have buildings that are leased, data on energy intensity simply might not be available. Likewise, manually entering inputs leaves room for human error during the process of ESG data entry. There is also the challenge posed by the fact that most companies use different measurement software than their utility provider, making it difficult for CSOs to monitor data in real-time. 

While difficulties with tracking ESG data are widespread, there is also the issue that some software does not include audit trails. To avoid scrutiny, companies are now requiring that ESG disclosures are verified or assured by a third-party. This added step can identify issues in reporting methodology and underlying data, which despite being time intensive have the added benefits of requiring a strong data foundation and can result in substantial improvements to a company’s broader ESG program. 

6. Unactionable Data Insights 

Missing from some of our interviews was clarity on how companies are leveraging their trove of ESG data once they have gone through the work of collecting it. Data collection and reporting aren’t the end goals, yet CSOs expressed being too time pressed to act on their data or only having the resources for the reporting processes versus funds to implement the required changes for ESG progress into their operations. Data insights can allow companies to be more responsive, and data with event-driven data processing tools and analysis makes it so that timely insights can be put to immediate use. Businesses that put in place the correct organizational processes and accountability systems can respond more efficiently to opportunities and threats and drive toward improved ESG performance.

How to build the right ESG data environment 

There will always be some friction in the ESG data process, however, with the right strategy in place not only will reduce the effort needed to manage ESG data, but it will create clear opportunities for companies to spend more time on value-added activities. When your company is ready to build its own ESG data environment, following are five essential tips to keep in mind.

Early intention should inform your data environment 

It is important to identify what you want to achieve before you start building the data environment. A company publishing an annual report will have different needs than a company that wants daily visibility into the performance of their carbon emissions improvement projects. Companies taking the long view of truly improving their ESG performance and strengthening the long-term viability of their business need to prioritize materiality and identify which issues and data matter most to their business. It is important to benchmark historical performance from the start. This allows a company to identify what metrics matter most and then to consequently implement an improved way of gathering the right ESG data for their goals.

A robust data environment ensures sustainability performance

An ESG data environment requires data integrity built on reliable, trustable, and secure data. Assess what systems are currently in place and where data is warehoused. Given the volume of ESG data, it is crucial that the right An ESG data environment requires data integrity built on reliable, trustable, and secure data. Assess what systems are currently in place and where data is warehoused. Given the volume of ESG data, it is crucial that the right technology is implemented to quickly extract and ingest data. To ensure that the ESG data is trustable, a system that prioritizes data integrity is essential. If a typo occurs during manual data entry, your system should flag any value that seems out of place given the parameters of the company. Finally, ensuring the security of your data is crucial so that it is not cross matched with another organizations, whether it is stored externally or on a server.

ESG data solutions should be easy to use 

A strong ESG data environment needs to be simple and easy to use. It should use the ETL principles of extract, transform, load to copy data from one or more sources into a destination system which represents the ESG data differently from the source or in a different context than the source. The system should also have a quick setup, wherein data entry is simple and can be accessed easily across departments. Similarly, the system should be highly dynamic to incorporate data from different departments. Usability is also a critical component for a successful ESG data environment and should have a friendly user interface that is easily deployable.

scalable ESG data solution enables growth

ESG is a nascent industry that is rapidly evolving, which means companies need a system where new ESG data or criteria can be added easily without the system being locked into the metrics for any one framework. Having scalability allows companies to expand the scope of metrics they capture data on over time and allows for new suppliers or facilities to be easily added as a company grows. Scalability also provides companies with the ability to conduct drill-down analysis by looking back in time at historical data and to forecast future performance. 

future looking solution prepares you for what comes next 

It is best for companies to start with a foundation that can be built on to easily and quickly. Utilizing technology such as artificial intelligence and natural language processing is one effective way of doing this. The key to a successful future looking ESG data solution is that there is a database with a wide set of information to enable companies to track and uncover areas to improve performance and drive business decisions. 

Harnessing ESG data to supercharge performance

Only one-fifth (roughly 400) of the Forbes Global 2000 companies have net-zero goals. Among those with net-zero commitments, only 30 percent have goals for the years leading up to 2030. The private sector has an essential role to play in solving the climate conundrum and companies are increasingly stepping up to the plate and strengthening their ESG credentials. To achieve meaningful targets, companies need to have the right solutions to drive performance. 

Good. Lab’s experienced ESG data experts have the experience working with Fortune 2000 ESG leaders who have developed an ESG data environment that fosters top ESG performance. If you’re ready to put your ESG data to work, get in touch with our team today to discuss the steps companies should take to build successful ESG strategies and a path toward achieving their goals.

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