Challenge: The Automotive Giant, a provider of comprehensive automotive financial services, faced several challenges in managing their data. They lacked a unified data platform that could serve as a foundation service globally, resulting in each region working in silos with their data. Additionally, there was no central data hub where business-relevant data was easily accessible through a comprehensive user interface. Data ingestion processes were time-consuming, taking up 60% of the team’s time, and there was a lack of self-service ability for business owners to identify major trends that could impact the business.
Solution: To address these challenges, the Automotive Giant embarked on a multi-year project for configuration/setup and development. The team consisted of 8 members dedicated to implementing the solution. The highlights of the solution included building a data foundation on Azure using Azure Data Lake and Data Factory. They focused on building scalable advanced analytics capabilities and performed data migration to consolidate data from 42 regions into one centralized data lake. This data modernization effort aimed to establish scalable use case models.
- The implementation of the solution yielded significant results for the Automotive Giant.
- By building a unified data platform on Azure, they established a centralized hub for business-relevant data.
- The comprehensive user interface made the data easily accessible to users.
- This streamlined data ingestion processes, making it more efficient.
- The implementation also reduced the time spent on data preparation.
- As a result, resources were freed up for more valuable analysis.
- Business owners were provided with self-service ability, empowering them to identify major trends.
- This empowered identification of trends can have a more efficient impact on the business
Technology Used: The Automotive Giant leveraged various technologies as part of their solution. The data foundation was built on Azure, utilizing Azure Data Lake and Data Factory. These technologies enabled efficient data management, ingestion, and transformation processes. The implementation also focused on building scalable advanced analytics capabilities to derive valuable insights from the consolidated data. Overall, the project combined cloud-based technologies, data lake architecture, and advanced analytics to address the client’s data challenges and drive positive outcomes.