Data integration & standardization on Google cloud improves time to insight by 25%

Sigmoid developed a centralized and scalable data warehouse on Google Cloud to optimize data-driven decision-making. A single source of truth for multiple data sources was created to address legacy data warehouse challenges of data discoverability, governance, and security.

Business Challenge

The client is a technology-based digital ecosystem company located in Indonesia, and is one of the five companies with the largest market capitalization on the Indonesia Stock Exchange. They cater to a wide range of services including transport, payments, food delivery, and logistics. Their legacy data warehouse was facing challenges in handling data from various systems belonging to the above-mentioned business divisions and also exhibited data administration, quality, and security issues. They wanted to improve data processing speed, reduce storage costs, and make data more accessible for enterprise BI reports that can be effectively used by various business units.

Sigmoid Solution

Sigmoid provided a comprehensive approach to data management by implementing efficient, reusable, and scalable data ingestion pipelines on the new data warehouse architecture. The solution was completed in two phases. In phase 1, we identified the dependent source tables that needed to be migrated to the new data warehouse and created the necessary schemas and tables. For enhanced querying capabilities, queries and views were refactored to access tables, followed by unit and regression testing. Phase 2 was an extension of phase 1, with the migration of 100+ tables, 45+ views, and 68+ Tableau views. The solution was implemented by validating the test results and comparing the outputs of the legacy and the modern data warehouses’ queries/views.

Business Impact

A consolidated and standardized data warehouse helped the client store, retrieve, and process data from different business divisions such as transport, payments, and food delivery across multiple locations in the Asia Pacific region. Query optimization improved the data processing and performance of the updated data warehouse. Additionally, the migration of data to the new GCP architecture resulted in significant savings in storage costs. Faster availability of data for users enabled them to perform analytics and generate actionable insights from the reports.


savings in storage costs


faster availability of the data


improvement in DWH performance

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