About
Sakha’s client is an Indian multinational motorcycle manufacturer headquartered in Chennai. It is the third largest motorcycle company in India in terms of revenue. It had a large fleet of vehicles with telematics devices installed, generating a massive amount of data on a daily basis. Sakha empowered the client through a telematics-based Big Data Lake and data pipelines project using Microsoft Azure.
The Challenge
The motorcycles manufacturer had a large fleet of vehicles with telematics devices installed, generating a massive amount of data on a daily basis. However, the company lacked a unified platform for storing and processing this data, leading to siloed data storage, duplication, and lack of standardized data formats. As a result, the company was facing challenges in gaining insights from this data, including identifying trends, predicting maintenance issues, and improving the overall customer experience.
Solution
The solution was to create a telematics-based Big Data Lake and data pipelines project using Microsoft Azure. Sakha’s solution included the following features:
- Data ingestion and processing: Azure Event Hubs were used to ingest telematics data from the motorcycles in real-time. The data was then stored in an Azure Data Lake Storage Gen2, which allowed for efficient storage and retrieval of large amounts of unstructured data.
- Data transformation and cleansing: Azure Data Factory was used to extract, transform, and load (ETL) data from various sources and transform it into standardized formats. This helped in reducing data duplication and cleaning the data for analysis.
- Data analysis and insights: Azure Databricks was used for running data science workloads, including machine learning and predictive analytics, on the cleaned and transformed data. This allowed for the identification of trends, predictive maintenance, and improved customer experiences.
- Data visualization and reporting: Power BI was used to create interactive visualizations and reports for business users to gain insights from the data.
- Improved customer experience: The telematics-based Big Data Lake and data pipelines project enabled the company to identify trends and patterns in customer behaviour, allowing for personalized recommendations and improved customer experiences.
- Reduced maintenance costs: Predictive analytics enabled by the solution allowed for the identification of potential maintenance issues before they occurred, reducing downtime and maintenance costs.
- Increased operational efficiency: The centralized data storage and processing platform allowed for faster and more efficient data analysis, enabling the company to make data-driven decisions quickly.
- Improved data quality: The data transformation and cleansing process improved the quality of the data, reducing data duplication and errors in analysis.
- Scalability: The solution was built using cloud-based technologies, which allowed for scalability and cost-effectiveness in managing large volumes of data.