{"id":3906,"date":"2023-10-09T05:07:46","date_gmt":"2023-10-09T05:07:46","guid":{"rendered":"https:\/\/beta74.thewebsitepreview.com\/wavicle\/dev\/?p=3906"},"modified":"2025-11-11T15:11:03","modified_gmt":"2025-11-11T15:11:03","slug":"automotive-supplier-data-modernization-strategy","status":"publish","type":"post","link":"https:\/\/beta74.thewebsitepreview.com\/wavicle\/dev\/case-studies\/automotive-supplier-data-modernization-strategy\/","title":{"rendered":"Automotive Supplier Leverages Data Modernization Strategy to Build a Foundation for Analytics and Predictive Maintenance"},"content":{"rendered":"

This leading global automotive supplier faced a pressing need to enhance reporting capabilities, expedite data-driven decision-making, and prepare their analytics environment to build a foundation for future AI and predictive maintenance initiatives. The company recognized that it needed a robust data foundation to tackle its growing analytics requirements, and their existing system did not provide the crucial business insights they needed. They partnered with Wavicle to unlock the full potential of their data resources to set a foundation for improved customer experience and better business outcomes.\u00a0<\/span><\/p>\n

Issues encountered in meeting data goals\u00a0<\/span>\u00a0<\/span><\/h2>\n

This automotive supplier is expanding into the micro-mobility market by partnering with one of India\u2019s largest micro-mobility sharing providers. The company needed assistance building the\u00a0<\/span>data architecture<\/span><\/a>\u00a0to ingest data into its cloud platform. This architecture\u2019s goal is to support operational insights and optimization while focusing on potential future\u00a0<\/span>advanced analytics capabilities<\/span><\/a>.<\/span>\u00a0<\/span><\/p>\n

The company relied on Metabase, an open-source reporting tool, for its analytics and dashboards. However, the tool was not optimized to integrate effectively with the existing data sources, including the ability to obtain both real-time and batch data, leading to difficulties in obtaining accurate insights related to identifying faulty batteries, quantity of battery wrappings, successful battery charges, and inventory counts. With a large and growing dataset, they also struggled with significant latency for high data volumes and challenges scaling their existing system. These limitations stood in the way of making decisions that had the potential to enhance the customer experience and optimize the workload of the operations team.\u00a0<\/span>\u00a0<\/span><\/p>\n

Keeping all this in mind, they wanted to proactively plan for future advanced analytics capabilities while improving data quality standards. The company also aimed to complete the project within a strict timeline and budget constraints, driven by their desire to implement operational changes swiftly to fuel business growth.<\/span>\u00a0<\/span><\/p>\n

To address these challenges and requirements, they engaged Wavicle\u2019s team of data analytics consultants to analyze their existing data landscape and formulate a scalable, future-proof data infrastructure strategy that would offer greater insight into their current operations and set them up for future success with advanced analytics initiatives.\u00a0<\/span>\u00a0<\/span><\/p>\n

Roadmap to a modern data architecture<\/span>\u00a0<\/span><\/h2>\n

Wavicle took a two-phase approach to design a strong data foundation for this automotive supplier\u2019s data and analytics capabilities.\u00a0<\/span>\u00a0<\/span><\/p>\n

First, Wavicle embarked on an extensive information-gathering process, meticulously collecting requirements from various business units. Our team engaged with the customer\u2019s stakeholders across multiple functions to thoroughly understand their reporting pain points, key analytics use cases, and needs for the new data architecture.<\/span><\/p>\n

Following this, Wavicle crafted a trio of potential architectural solutions tailored to fulfill the automotive supplier\u2019s fundamental requirements for a modern, scalable data platform with low vendor dependency. This solution will support crucial reporting functions and serve as a foundation for advanced analytics and insights, including their long-term vision for predictive maintenance in their micro-mobility fleet. The three solutions included:<\/span><\/p>\n