Decision-Grade ESG Data: A Product Operating Model for Enterprise Sustainability Data Foundations at Retail Scale
DOI:
https://doi.org/10.63282/3050-9262.IJAIDSML-V6I2P129Keywords:
ESG Data Foundations, Data Product Management, Scope 3 Emissions, Data Lineage, Assurance Readiness, Domain Maturity, Decision IntelligenceAbstract
Enterprise sustainability reporting has outgrown the project-based delivery models that produced it. Most large organizations still assemble ESG disclosures from fragmented merchant extracts, spreadsheet calculations, and manual reconciliation a pipeline that cannot support regulatory readiness, third-party assurance, or self-service analytics at scale. The literature treats ESG data either as an accounting and assurance problem or as a data-engineering problem; neither frames it as a product management problem with lifecycle, roadmap, and adoption mechanics. This paper proposes a Sustainability Data Foundation and Intelligence (SDFI) operating model that treats the ESG data estate as a managed product portfolio. The model defines explicit product accountability across a six-stage lifecycle source ingestion, modeling, transformation, governance and quality, lineage and controls, and intelligence-ready consumption and maps that lifecycle onto six sustainability domains: Climate, Circular Economy, Packaging, Nature, Animal Welfare, and sustainable sourcing. We introduce a quantitative domain-maturity rubric scoring each domain on source reliability, transformation determinism, control coverage, and consumption readiness, and use its weighted composite to sequence a multi-year roadmap under fixed engineering capacity. We operationalize a decision-grade gate: no data product publishes unless lineage completeness, reconciliation variance, and control coverage simultaneously clear defined thresholds. Evaluated against a project-based baseline across an estate spanning 14.4 MMT CO₂e Scope 1 and 2 emissions, an estimated 635 MMT CO₂e Scope 3 inventory, and 76.2 million acres of nature-related engagement, the model raises mean domain maturity from 2.14 to 3.61 (of 5), reduces disclosure cycle time by 47.8%, cuts restatement events by 71.4%, and lowers marginal cost per new consumption use case by 62.9% (p < 0.01 across all endpoints).
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