Automotive Data
Management
Services

In a nutshell, automotive data management services cover the process of collecting, storing, processing, and analyzing various types of automotive data, ranging from information generated by connected vehicles to service centers' data.

Speaking of the automotive industry, data management is extremely important since it helps to handle the complexity and wide array of automotive parts involved in vehicle repair and manufacturing. Due to automotive data management services, automotive manufacturers, dealers, service centers, and customers can easily access accurate vehicle information across various touchpoints and channels.

To sum up, automotive data management services ensure operational efficiency (e.g., smarter supply chains and predictive maintenance solutions), enhanced customer experience (e.g, high level of personalization and responsiveness), and compliance with industry regulations & safety standards (e.g., cybersecurity protocols).

Both cloud and on-premise solutions offer significant advantages, so everything depends on your long-term data management goals and budget limitations.

It is essential to consider that automotive data is infrequently static. Driver behavior, mileage, repair history — everything changes exponentially. In this regard, we highly recommend considering your current automotive data management goals: whether you need the data immediately or are prepared to play the long-term game. On-premise automotive data management solutions are a great option when it comes to immediate results. By way of illustration, think of manufacturing QA systems or real-time in-car analytics utilized before the data ever leaves the car. Cloud automotive data management solutions, in turn, are perfect for predictive maintenance. Interesting fact to consider: cloud is the only environment that can handle billions of heterogeneous data points from multiple geographies.

Let's talk about price. Although on-premise automotive data management solutions may seem cheaper at first, they can result in a large investment if you plan to scale your solution (spoiler: you will likely scale at some point). Cloud-based automotive data management is a more expensive option, but it can save considerable amounts of money if you operate in the data-driven services sector.

Nowadays, it is impossible to underestimate the role of artificial intelligence (AI) and machine learning (ML) in automotive data management. Generating terabytes of data, automotive systems cannot be handled manually, which raises the need to leverage advanced technologies like AI and ML.

In terms of the recent project, our automotive data management team utilized a trained machine learning model responsible for determining a coefficient used to estimate the number of labor hours required for a specific vehicle model. This logic is applied in slot-availability calculations. For example, if a vehicle model is very old or has high mileage, the repair will likely require more time. The model relies on the following inputs: the client's city and state, the vehicle's VIN, mileage, and model year.

Typically, artificial intelligence components are wrapped in an API so that the service can be used conveniently without integrating extra libraries or datasets directly into the client application.

In automotive data management, predictive maintenance can be implemented in several ways. Let's take a look at each of them:

• Predictive maintenance to manage the workload in vehicle service and repair centers. In such cases, the predictive maintenance model gathers data from several sources: DMS partners and internal ERP/CRM systems. Next, the data are integrated into a single source of information utilized by Agentic AI and ML algorithms to predict the capacity of work, technical resources, and employee workload.
• Predictive maintenance for customer use. Same data sources, with an emphasis on real-time service center data, to predict the most suitable time slots that benefit both parties. This predictive maintenance model also relies on AI and ML algorithms to offer personalized discounts for unpopular hours, thereby streamlining the workload and allocating resources appropriately.
• In-vehicle predictive maintenance alerts. This predictive maintenance model collects data from ultrasonic sensors, camera-based perception, and object detection and tracking technologies that detect unusual engine behavior, engine temperature changes, high tire pressure, and so on. Next, the driver receives timely alerts that help prevent severe car issues by visiting service centers in advance to eliminate the problem at its onset.

At devabit, we follow comprehensive data integration mechanisms to ensure the raw data from multiple sources is unified into one easy-to-use system. Here are several efficient automotive data management practices we implement while integrating data from various sources:

• data fabric architecture deployment for connecting ERP systems, MES, and supply chain platforms into a single information layer;
• semantic mapping and master data management (MDM) to reconcile VINs, part numbers, dealer IDs, and customer profiles;
• integrating event-driven pipelines to ensure real-time data is triggered by manufacturing anomalies, logistics updates, etc.;
• utilizing AI and ML algorithms to fill in the missing workflow gaps and enrich the systems with predictive maintenance capabilities.

• Ingestion & Streaming (Kafka, MQTT, AWS Kinesis, Azure Event Hubs)
• Storage & Data Lakes (AWS S3, Azure Data Lake, Google Cloud Storage, Delta Lake, Parquet)
• Processing & Analytics (Apache Spark, Flink, Databricks, Snowflake, BigQuery)
• AI & Machine Learning (TensorFlow, PyTorch, Scikit-learn, MLflow, Kubeflow, SageMaker)
• Integration & Governance (Airflow, dbt, Talend, Fivetran, Collibra, Apache Atlas)
• Visualization & Apps (Power BI, Tableau, Looker, custom APIs/portals)
• Security & Compliance (Okta, Keycloak, AWS IAM, KMS, HSMs)