How to Manage Sustainability Data

HOW DO YOU GET IT RIGHT? THE DATA MANAGEMENT MATURITY-RISK CURVE

Addressing the why, where, and how foundational questions can reveal organizational gaps, ranging from grasping the application of specific regulatory directives to imperfect data collection, management and verification practices. This is where we move from conceptual to practical aspects of effective data management. To add perspective, it’s worth considering the real-world impacts of poor data management. Having complete and accurate sustainability data gives you the best chance of acting before issues become problems. For example, addressing the need for lower energy consumption at certain sites to meet annual targets. Failing to see your blind spots early can put you in an uncomfortable position, where previous disclosures must be walked back or explanations issued to senior management, investors, or your workforce. Regulators may also take a dim view of firms restating data given it can indicate poor governance and internal controls. Regulators may wonder what other data, such as financial disclosures, may be suspect. It’s a reminder that the risk isn’t theoretical. We consider best-practice data management to cover four core pillars, which together indicate maturity: process, people, governance, and technology. The general rule is that the above risks linked to sub-standard reporting reduce as you move up the maturity curve.

NASCENT Data is not complete and not accurate

MODERATE Data is partially complete and partially accurate • Process: Basic procedures for tracking scope boundaries and data inputs but lacking clear guidelines and assurance trail. • People: Some assigned roles and responsibilities, but data ownership remains unclear. • Governance: Limited quality checks and data validation. • Technology: Data stored in spreadsheets or siloed systems with some automation.

ADVANCED Data is on track to be complete and accurate • Process: Established procedures for regular updates to scope boundaries, utilities tracking, and supplier data management. • People: Clearly defined roles, responsibilities, and accountability across data owners. • Governance: Strong governance framework akin to financial processes with documented assurance processes. • Technology: Automated data feeds and a single platform for data retrieval and reporting.

• Process: No defined methods for tracking scope boundaries, utilities, or suppliers; no data management guidelines. • People: Unclear roles, responsibilities, and data ownership. • Governance: No verification, quality checks, or assurance measures. • Technology: Data stored in spreadsheets or scattered documents.

REPORTING RISK

DATA MATURITY