A global investment manager, asset manager, and service provider contacted Imaginet to help them enhance their data quality and processes. They had numerous data quality issues and relied on a manual approach (using an on-premises system and Microsoft Excel) to clean, check, move, and report on their data. Sometimes it could take up to three weeks to generate a single report, causing a strain on time and resources.
Our team used Azure Data Factory to move and transform the data from several sources in their on-premises databases into a cleaner and more digestible data warehouse (using Visual Studio to create and Git to store database schemas) and tabular model. We gave unstructured data a home using Microsoft SQL Server Master Data Services (MDS) and integrated it with the data warehouses using automated builds and release pipelines in Azure DevOps. Finally, we used SSRS to store and produce reports.
The project gave the client a reason to review business rules and brought errors and inefficiencies to light, especially regarding reporting. By moving the data to the cloud and taking advantage of Azure DevOps release pipelines, we improved and refined the condition of the client’s data and practices, by which they develop reports based on their data. Putting everything in source control ensured that any team member could support and maintain the system. While it used to take multiple weeks to create a single report, now the client can deliver almost 70 monthly reports in just a few simple steps; for a much more automated, user-friendly, and streamlined reporting system. We continue to provide ongoing support and maintenance to enhance these solutions.
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- Azure Data Factory
- Azure DevOps
- Azure SQL
- SQL Server Analysis Services (SSAS)
- SQL Server Master Data Services (MDS)
- SQL Server Reporting Services (SSRS)
- Visual Studio
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