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What to know about ESG data across ERP, enterprise systems

No single enterprise systems holds the key to ESG reporting. Instead, data is spread across ERP and other software systems. Here's what IT and sustainability leaders should know.

Demand for corporate commitments to environmental, social and governance -- or ESG -- is growing. And that means CIOs and chief sustainability officers must understand the complexities around ESG data and reporting.

While there exists dedicated software for ESG data collection and reporting, many legacy enterprise systems -- such as ERP systems -- already contain information on an organization's sustainability performance. In a way, this is good news, but it also requires a disciplined approach to data collection.

"One of the challenges with ESG is that there are many systems that the data needs to come from, and there's often no single system of record," said John Mennel, sustainability strategy leader and managing director at Deloitte Consulting. "In fact, there's almost never a single system of record."

Because ESG data sits across a number of different enterprise software systems, Mennel and his team counsel organizations to map out where all of this information is generated and stored.

"That's always the first step, and it's usually fairly complicated because [the ESG data is] in a lot of different places," Mennel said.

But it's a critical step in a sustainability journey.

In order to reach their sustainability commitments, companies must have a handle on their ESG data, Mennel said.

"Data is the absolute foundation for ESG and sustainability," he said. "It's a critical challenge, [and] it's one of the first things we tell clients they need to spend some time on to get right."

Non ESG-specific enterprise systems that contain ESG data

A number of enterprise systems may contain ESG data. Here are some common ones:

  • ERP systems contain resource management data, as well as financial information about the costs related to an organization's sustainability initiatives.
  • Human resource management systems house metrics related to diversity, equity and inclusion.
  • Transportation and logistics systems log information on vehicle and fuel usage, as well as electrification of fleets.
  • Product lifecycle systems, such as those that handle IT asset management, may house important ESG information and potentially support circular development.
  • Standalone supplier management systems may track the scope 3 emissions of a company's suppliers.
  • Inventory and warehouse management systems may help organizations optimize stock management, and reduce their waste and environmental footprint.
  • Building management systems can track energy usage.

CRM systems can also be useful for a company's ESG endeavors, said Meenakshi Narayanan, senior analyst at Everest Group, a research and advisory firm headquartered in Dallas.

"[These] systems can be used to look at how consumers are responding to your sustainable products, and how they are reacting to your new policies," Narayanan said.

Infographic showing the driving forces for ESG.

Streamlining ESG data collection

Because multiple enterprise systems contain information about ESG, creating a data collection strategy is critical. Mennel suggests the following process:

  1. Map out what systems contain ESG information.
  2. Create a data lake that all of the data can flow into.
  3. Use "last-mile" reporting software that assembles all of the data into the required reports.

An orchestration or workflow data tool can help organizational teams understand data quality levels, as well as the controls that were placed on that data, Mennel said. This information can include the following:

  • Specifying where the data came from.
  • Identifying who has touched that data.
  • Tagging whether someone verified the data.

ESG teams should avoid manual data collection, such as soliciting data points from various stakeholders via email, Mennel said.

"That's going to be inaccurate and it's not going to be very timely," he said.

Adequate controls are also necessary to help ensure the data is accurate and can be reproduced for future reporting periods, Mennel said.

Like Mennel, Narayanan encourages companies to establish a central repository for ESG data.

Standardizing data formats, as well as developing naming conventions so that people can easily access the information they are seeking are also important, Narayanan said.

"You need to have the right metadata to ensure consistency, and to enable efficient searching and retrieval of that data," she said.

Gathering scope 3 data

With pressure growing to account for scope 3 emissions -- that is, the indirect emissions up and down the value chain -- companies need a strategy for gathering this challenging data type from suppliers.

Sustainability and IT teams should follow the 80/20 rule, which means they focus on their largest suppliers first, Mennel said. Supplier data related to ESG will come from both internal and external sources. IT can find internal data from the supplier management system. For example, the sustainability team can find external data sets -- such as those generated by organizations like S&P Global or the Carbon Disclosure Project -- can provide additional insight on supplier performance.

"You can use those two sources of data [internal and external] to start working with suppliers to make decisions on reductions in cases where you have to, or you can make choices about switching suppliers based on ESG and sustainability factors," Mennel said.

Although AI poses environmental problems, some are looking to the technology to help with sustainability efforts.

AI will become a key scope 3 reporting tool as it continues to evolve, said Natalie Henfrey, director of ESG consulting at GEP, a supply chain consulting and strategy firm headquartered in Clark, N.J.

"As AI develops and grows, you should be able to use AI tools and send them out to go through your suppliers' annual reports and bring the data back, because you can regard it as audited data rather than relying upon a supplier sending a spreadsheet," Henfrey said.

However, this requires companies to make an investment in AI, as well as in a repository or database that can receive that information, she said. It also requires companies to act on the data they capture, and that includes motivating suppliers to reduce their own scope 3 emissions.

"If you're going to meet your requirements to achieve your climate transition plan, you can't just sit on data and say it will look after itself," Henfrey said. "You actually need to then go through some processes of identifying which of your suppliers are driving the worst scope 3 emissions."

This can lead to some delicate conversations, Henfrey said.

"In activating your supply base to support this, you're fundamentally saying, 'you've got to work in a different way,'" she said. "I think AI will be really important in [collecting] that information, but you will still need some of that operational process support to actually drive the change."

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