Summary
Key results
Faster report and analysis preparation through automated ETL
Higher data quality via standardization and consistent naming conventions
Dispersed data slowed growth
Rapid access to reliable data is critical for sales planning and marketing activities. In the company’s Polish division, data was spread across multiple systems, forcing time-consuming manual integrations and causing reporting errors. The lack of shared standards and naming conventions hampered cross-team collaboration and drove up maintenance costs. To ensure consistency and speed up decision-making, management tasked Sii Poland with designing and deploying a modern, cloud-based data warehouse.
A single source of truth in the cloud
The goal was to create a scalable, secure cloud analytics environment to replace fragmented legacy solutions. The project was delivered in Agile by Sii Poland’s multidisciplinary team, responsible for design, implementation, and documentation.
Scope of work included:
- Building secure infrastructure for data storage and processing on AWS Cloud and Amazon S3
- Creating integration pipelines and automating multi-source data loading using Matillion ETL
- Launching a modern data warehouse for fast, scalable data access with Snowflake
- Standardisation and transparency in reporting using the Kimball approach data model
With the new environment, data became accessible, consistent, and ready for real-time analysis.
Scalability and competitive edge through modern analytics
The new data warehouse became a strategic platform supporting Sales, Marketing, Distribution, and the company’s analytics teams in Poland. A unified architecture increased trust in reporting and enabled managers to make decisions based on up-to-date information. The Snowflake- and AWS-based solution provides flexibility and easy scaling as the organization grows – across both user counts and data sources. ETL automation shortened report preparation time and reduced the workload on analytics teams, allowing them to focus on insights instead of manual data integration.