ESG Data Harmonisation

The output is a documented data architecture and process design. Implementation — connecting the model to systems, training teams, and building collection templates — is included in Realize and Extension packages.

Gap and Overlap Analysis

• Assessment of data redundancies: same underlying data collected in multiple ways
• Identification of missing data fields required by applicable frameworks
• Inconsistency mapping between outputs — discrepancies that create assurance risk

Unified Data Model Design

• Regulation-agnostic data architecture: defined fields, units, definitions, and collection frequency
• Single source of truth design — collect once, output many
• Data ownership assignments: who is responsible for each data field

Framework Mapping Configuration

• Field-level mapping from unified model to each required output: CSRD/ESRS, ISSB, GRI, CBAM, and customer templates
• Gap documentation: fields required by frameworks that are not yet in the unified model
• Prioritisation of gaps by regulatory obligation and reporting deadline

Process Design and Workflows

• Internal data collection process design: who collects what, when, in what format, and via what channel
• Validation rules per data field
• Reporting calendar aligned to your regulatory and voluntary disclosure deadlines

Implementation and Training (Realize and Extension)

• Collection template build (Excel or equivalent, implementation format confirmed with client)
• Training sessions for internal teams responsible for data collection
• Test run with initial data population and output validation

ESG reporting software — whether a dedicated platform or a module within an ERP system — requires clean, structured, consistently collected data to function. The most common reason ESG software implementations fail to deliver expected value is that the underlying data collection process was not redesigned before the tool was deployed.

This service addresses the data structure problem directly. It is not a software recommendation or implementation service. It is the design work that makes implementation succeed — and that gives organisations without current software investments the foundation to evaluate which tools, if any, they actually need.

Our Approach

We begin with a data landscape audit: what data you currently collect, how, by whom, and for which outputs. We then map the landscape against your reporting obligations to identify where the same underlying data is being collected in multiple ways, where gaps exist, and where inconsistencies between outputs create risk.

From that analysis, we design a unified data model — a single, documented set of fields from which every required output can be generated. We map each field to the relevant disclosure requirements across your applicable frameworks and design the internal process for collecting and maintaining that data. The Realize package includes building the collection templates and training the people who use them.

The output is a documented, maintainable data architecture — not a software subscription or an ongoing engagement.

What's Included — All Packages

All engagements include:

• Lead ESG data specialist as primary contact

• Data landscape audit

• Gap and overlap analysis

• Expert consultation sessions

• Email support throughout engagement