Data management optimization: 20% reduction in data storage costs

Prior to diving into the technical details, we worked with our client to understand their challenges with data management. This allowed us to grasp their pain points and helped us align our visions. By the end of this phase, we had a clear Vision and Scope document outlining the project's roadmap.

Following the discovery phase, our main priority was to unify the fragmented data landscape. By integrating data from a variety of sources, we constructed a cohesive environment where every piece of data found its rightful place.

After integrating data, it was necessary to ensure smooth data flow. We designed robust ETL pipelines, transforming raw data into actionable insights. With meticulous testing and refinement, we succeeded in automating and enhancing the ETL process.

With our ETL pipelines in place, we needed a powerhouse to store client’s data. By leveraging Snowflake capabilities, we provided a scalable storage that ensured swift data retrieval.

Based on the warehouse data, we crafted user-friendly Tableau dashboards. Thanks to comprehensible designs, we ensured that the information was accessible and easily digestible for end users.

As we approached the finish line, we concentrated on resolving issues and implementing the solution. As a result of iterations and feedback, we developed a fully functional data management system. We adhered to the Agile methodology throughout the process, ensuring flexibility and responsiveness. Our primary channels for client communication included Slack and Zoom, while Jira served as a convenient tool for tracking and managing tasks.