Data assessment and strategy

Automotive Supplier Leverages Data Modernization Strategy to Build a Foundation for Analytics and Predictive Maintenance

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. 

Issues encountered in meeting data goals  

This automotive supplier is expanding into the micro-mobility market by partnering with one of India’s largest micro-mobility sharing providers. The company needed assistance building the data architecture to ingest data into its cloud platform. This architecture’s goal is to support operational insights and optimization while focusing on potential future advanced analytics capabilities. 

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.  

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. 

To address these challenges and requirements, they engaged Wavicle’s 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.  

Roadmap to a modern data architecture 

Wavicle took a two-phase approach to design a strong data foundation for this automotive supplier’s data and analytics capabilities.  

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

Following this, Wavicle crafted a trio of potential architectural solutions tailored to fulfill the automotive supplier’s 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:

• A Databricks model that would offer a unified platform for data engineering, data science, and data analytics with low maintenance costs and an intuitive, customizable interface. 
• An AWS-native model that would offer serverless resources to process data in real time with the capacity to automatically scale on demand without the need to manage infrastructure.  

• An open-source model that would offer significant flexibility and customizability at a low cost with no vendor lock-in. 

These options offered the customer multiple unique routes to meet their goals. Wavicle’s team of experts provided deep analysis of the pros and cons for each option to enable their stakeholders to make a well-informed decision about their company’s data architecture that aligns seamlessly with their company’s technical and business requirements.

Impact of proposed solutions to enhance analytics initiatives 

The company ultimately selected a Databricks architecture with Amazon QuickSight for reporting because it best fulfills their specific requirements for a scalable, flexible, and cost-efficient data and analytics architecture.

This modern data architecture illuminates a new path forward for the global automotive supplier. The Databricks-based architecture will significantly enhance the company in the following ways:

• Data visibility: Provide a comprehensive understanding of their operations, unveil hidden insights, and illuminate critical trends. 
• Decision-making: Leverage anticipated insights to optimize the battery processes, improve charging efficiency, and refine inventory management. 
• Operational efficiency: Improve reporting, reduce costs, and lighten operational burdens by centralizing data for immediate access. 
• Customer experience: Access historical data to help the company track metrics like battery performance and enhance the customer experience. 
• Data utilization: Unlock advanced analytics capabilities to help the company anticipate battery demand and maintenance requirements to gain a competitive edge. 

• Future-proof infrastructure: Address current and future data challenges through a phased approach, positioning the company to scale its analytics initiatives as it continues to accumulate more data. 

Wavicle’s consultants provided a valuable roadmap for the global automotive supplier’s data infrastructure that will accelerate future business initiatives. It will empower the company to make data-driven decisions, adapt to change, and continuously enhance operations to stay at the forefront of their industry.