Three systems, three truths, and a day of lag
Product, quality, and manufacturing data lived in three separate enterprise systems: Oracle E-Business Suite (ERP), Oracle Agile (PLM), and LabWare LIMS. Each answered its own questions well — but cross-system analytics meant moving data between them, and joined views lagged well behind operations.
Analytics ran on a classic Oracle BI stack — OBIA and OBIEE over a dedicated Oracle data warehouse, loaded by nightly ETL. Decision-makers worked from yesterday’s data, and the stack carried significant recurring licensing costs.
The team needed one consistent, current picture across all three systems, without adding agents or extra load to the production databases.
One lakehouse, fed continuously
A2 deployed a centralized lakehouse and connected all three systems through oracdc change data capture, replacing nightly batch ETL with continuous ELT: changes land in the lakehouse within 30 seconds of being committed in the source, turning day-old reporting into near-real-time analytics.
The lakehouse pairs a relational data warehouse with a high-performance OLAP database, and a federated SQL engine queries across both. A BI visualization layer provides dashboards and self-service analytics, a semantic layer keeps business definitions consistent for every consumer, and an AI-assisted exploration system lets users ask questions of the data in natural language and surface insights.
The new platform fully replaced OBIA, OBIEE, and the Oracle data warehouse behind them — significantly reducing recurring licensing costs. Combined with near-real-time freshness, self-service analytics, and AI-driven insight, the returns compound across several fronts at once.
Client identity is withheld under a non-disclosure agreement. Engagement details can be discussed under NDA where appropriate.
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