Our Work

Case Studies

BI Self-service Data Mart & Reporting Solution

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Challenge

Our client identified that their IT/BI Team was struggling to keep up with the requests. The conventional method of reporting was too slow and resource-consuming. They were aiming to remove data silos and expedite new data integrations and reduce time to insights.

Our focus on this project was to resolve the bottleneck from the IT/BI Team by proposing self service solutions that can be used for both Ad-hoc data requests and standardized reporting.

Secondary Info

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Solution

Build a self-service Data Mart to service Standard & Ad-hoc Reporting

Client has been provided with a new level of performance optimization, scalability, and analytical depth by embracing advanced data modeling techniques as below:

Multiple Transactional Big Data Source, data migrated to Google Cloud
Dally ETLs developed to transform data on a regular basis
Large Scale Power BI Data Marts developed for the self service reporting
Additional standard dashboards developed on Power BI
Incremental Refresh and Data Partitioning implemented on Power BI data models through Tabular Editor and SQL Server Management Studio
Alerts and Data Quality Checks set up for proactive monitoring

Advanced Techniques Used

Incremental Refresh on PowerBI

Data Marts can contain data upwards of 10GB. In order to effectively refresh data regularly, incremental refresh techniques are utilized to drastically bring time and processing costs for daily refresh

Time Intelligence and other Dynamic Measures

By utilizing tools such as Tabular Editor, dynamic time intelligence measures are created to provide YTD, MTD, Previous Year and several other time comparisons.

Complex Relational Models

As Data Marts are general purpose, their relational data models are complex and utilize several active and inactive relationships to be able to service the various reporting needs of the end user.

Refresh Partitions through SSMS

Unlike conventional PowerBI files, due to the large size of the data models, Data Marts’ storage is managed through SSMS (SQL Server Management Studio). SSMS provides faster and more control over the various partitions per table generated through Incremental Refresh.

Memory Optimisation

Due to the large size of the Data Marts, it is essential that the model only contains information of use, and on top of this the information is stored in the most efficient manner possible. Several external tools are utilised to accomplish this.