September 2026 Summaries

APAC retail and consumer packaged goods teams increasingly rely on automated systems and AI for inventory allocation, reordering, and promotions, making trustworthy data essential as fragmented sources, legacy ERPs, spreadsheets, and regional systems often produce inconsistent information. While centralization brings data together, dbt is presented as the layer that makes it reliable through automated tests that detect missing, duplicate, outdated, or disconnected data; governed semantic definitions that ensure metrics such as days of supply and promotional lift remain consistent across dashboards, analysts, and AI agents; and end-to-end lineage that traces decisions back to original sources. The piece argues that ungoverned data can turn automation into costly overstock, stockouts, missed promotions, and public operational failures, whereas combining Fivetran for centralization with dbt for testing, governance, and traceability enables teams to activate data more confidently across the business.

Ecommerce analytics becomes more difficult as businesses grow because transactional, marketing, web, finance, CRM, and operational data are often separated across platforms, making attribution, customer lifecycle analysis, and consistent reporting reliant on manual spreadsheets or fragile pipelines. The proposed approach is to automatically centralize data from sources such as Shopify, Google Analytics, advertising platforms, finance systems, and CRMs in a cloud data platform, then analyze it through business intelligence tools. Combining these datasets can support revenue-based attribution, a shared source of truth for metrics, analysis of the full customer journey and retention, and customer segmentation methods such as RFM analysis. Examples involving Koh, Sleeping Duck, Papier, Pet Circle, Ritual, Carwow, and Westwing illustrate reported benefits including improved targeting, clearer revenue and margin reporting, stronger retention analysis, and reduced engineering maintenance. The piece recommends evaluating integration tools based on source coverage, maintenance requirements, data completeness and freshness, and scalability, positioning Fivetran as a platform designed to automate these data connections.

Open Data Infrastructure (ODI) is presented as an open, standards-based data architecture designed to reduce friction in analytics and AI workflows by giving organizations greater control, interoperability, and access to AI-ready data. It aims to counter vendor restrictions on data exports and APIs, allowing companies to access and reuse their own data without repeatedly seeking permission or rebuilding pipelines. By storing data once in low-cost object storage using open table formats such as Apache Iceberg or Delta Lake, ODI separates storage from compute, enabling teams to select appropriate tools for business intelligence, data science, operations, and AI without duplicating data. Its shared data layer can also reduce redundant pipelines, reconciliation efforts, and inconsistent business definitions by providing a common source of governed data, metadata, lineage, and metric definitions. The approach emphasizes that openness does not mean weaker security, as standardized access policies, classifications, and lineage can travel with data across systems, helping organizations maintain controls, simplify audits, and support reliable autonomous AI agents.