Luxury car maker required a Business Intelligence (BI) system to gain insights about their business processes and gain insights about customer needs and preferences, driving business growth and future opportunities.
Client: A US-based luxury electric car automaker
Problem: The client had multiple cloud database systems capturing different aspects of the business and the needs and preferences of their customers. They required to bring all the cloud databases into a single data storage solution while also building Business Intelligence (BI) and analytics tools to allow for dashboard and visualization creation. The problem can be thought of has having three parts
- Gather all current data sources into a newly built one data storage system
- Build a foundation for business analytics for various internal departments, so that they can easily get actionable insights and enable data-driven decision making.
- Create enterprise level, interactive BI reports, and dashboards like customer usage reports, customer experience reports or such for the various different departments of the enterprise.
VT Solution: Since the client foresaw a 100x growth with a launch of a new product, one of the requirements was to have a flexible data storage system, that could be scaled rapidly. Therefore, we designed and implemented an AWS data lake to form the basis of data storage system. Bringing in source data from APIs, Legacy RDBMS, SAP, and vehicle files into AWS systems. Building Data Lake, creating data integration and ETL capacities to take raw data from data lake and creating a data warehouse.
Finally, introducing semantic data models using Power BI to perform analytics, create BI reports and dashboards. Enabled self-service BI for client’s analytics team to utilize the data warehouse and its in-built analytical tools.
Key Points:
- Achieved client’s goal by creating an end-to-end implementation of a BI system from the creation of a singular data source, data cleaning, integration, extraction to finally generating BI reports and visualizations
- Build data lake to form the singular source of raw data throughout the organization, automated data integration and ETL to create a data warehouse.
- Built Analytics and BI reporting tools on top of the data storage sources, to allow generation of reports, visualizations, and dashboards for client.
