One Data Platform for Every Vehicle
At a glance
CLIENT
A European fleet management company
SERVICE
- Data platform strategy, data warehousing services, data analytics consulting services, big data consulting services, cloud devops, custom software development
INDUSTRY
- Fleet Management / Logistics
A European fleet management company operated tens of thousands of vehicles across leasing, rental, and managed fleets. Telematics data, workshop records, fuel transactions, and driver incidents were split across multiple vendors and legacy systems. Each new analytics request turned into a one-off project, and operations teams relied on static reports that were weeks out of date.
LeanCoded partnered with the company to build a cloud data platform for telemetry and operations analytics. Over 15 months, we designed and implemented a unified data foundation on the cloud, integrated more than ten data sources, and delivered near-real-time dashboards for utilization, maintenance, and safety. The platform combined data warehousing services, cloud devops, and machine learning consulting services to support current reporting needs and prepare for future AI use cases.
When Every Vendor Brings Its Own Data Island
The company used different telematics providers across regions, each with its own API, schema, and data quality issues. Maintenance data sat in workshop systems, while fuel and toll records were managed by separate providers. Analysts spent most of their time extracting CSV files, aligning IDs, and reconciling differences before they could answer basic questions about utilization or cost per kilometer. LeanCoded conducted an assessment of the existing data landscape, cataloguing sources, volumes, and usage patterns to define a realistic roadmap for consolidation.
At the same time, leadership wanted to move from static monthly reports to continuous insight: early detection of underutilized vehicles, maintenance issues before breakdowns, and higher visibility into risky driving behavior. Achieving this required a scalable data platform, not just another reporting layer. LeanCoded designed a cloud-native architecture using data analytics consulting services, big data consulting services, and cloud devops practices so the company could ingest high-frequency telemetry, join it with business data, and feed both dashboards and downstream applications from a single source.
From Disparate Feeds to a Unified Fleet Data Platform
LeanCoded started by defining core data products: vehicle 360, trip history, maintenance events, and cost metrics. For each product, we specified the attributes, refresh frequencies, and consumers—operations, maintenance planning, finance, and future data science teams. Data engineers then implemented ingestion pipelines from telematics vendors, workshop systems, ERP, and fuel providers into a centralized warehouse and streaming layer.
To make data usable beyond IT, we created curated data sets and semantic models for key KPIs such as utilization, idle time, breakdown rates, and total cost of ownership. Using custom software development and software development services, LeanCoded delivered APIs and data views that BI tools and operational applications could consume without deep knowledge of raw schemas. This made the platform a shared backbone for dashboards, alerts, and eventually predictive models.
Use cases and KPIs first
Ingestion and normalization across vendors
Cloud data platform and governance
Operational dashboards and alerts
Foundation for predictive analytics
How LeanCoded Turned Fleet Data into Operational Insight
The goal was not just to “land data in the cloud”, but to give operations a reliable engine for decisions. LeanCoded combined engineering, analytics, and practical change support so that the new platform became part of daily fleet management.
- One data foundation for the entire fleet
All key operational data—telemetry, maintenance, fuel, and incidents—now flows into a single cloud platform, reducing manual reconciliation and conflicting reports. - Standard KPIs across regions and vendors
Utilization, downtime, and safety metrics are calculated using shared definitions, enabling fair comparison across different telematics providers and markets. - Near-real-time visibility for operations
Fleet managers see up-to-date information on vehicle status and exceptions, with alerts that highlight where intervention is needed instead of combing through static reports. - Ready for AI without rework
By applying data analytics consulting services and machine learning consulting services from the start, the platform now supports predictive models on top of existing data products instead of requiring another rebuild.
Impact on Utilization, Downtime, and Planning
Within the first year, the data platform became the primary source for operational reporting. Manual data preparation for monthly fleet reviews was reduced significantly as teams adopted the standardized dashboards and APIs. Fleet managers could identify underutilized vehicles and reallocate them earlier in the cycle, while maintenance planners used consolidated fault and service data to schedule workshop work more effectively.
The company also started a pilot for predictive maintenance on selected vehicle groups, using historical patterns from the platform to flag units at higher risk of breakdown. Although early, these pilots demonstrated fewer roadside incidents and more scheduled interventions. By combining data warehousing services, big data consulting services, cloud devops, and custom software development, LeanCoded helped the fleet management company turn fragmented operational data into a scalable, analytics-ready asset that supports both current KPIs and future AI initiatives.